Arduino School Bus Tracker

The Compact Muon Solenoid Experiment

Mailing address: CMS CERN, CH-1211 GENEVA 23, Switzerland

Before Installation

W. Adam, T. Bergauer, M. Dragicevic, M. Friedl, R. Fr¨uhwirth, S. H¨ansel, J. Hrubec, M. Krammer, M.Oberegger, M. Pernicka, S. Schmid, R. Stark, H. Steininger, D. Uhl, W. Waltenberger, E. Widl Institut f¨ur Hochenergiephysik der ¨Osterreichischen Akademie der Wissenschaften (HEPHY), Vienna, Austria P. Van Mechelen, M. Cardaci, W. Beaumont, E. de Langhe, E. A. de Wolf, E. Delmeire

Universiteit Antwerpen, Belgium

O. Bouhali, O. Charaf, B. Clerbaux, J.-P. Dewulf. S. Elgammal, G. Hammad, G. de Lentdecker, P. Marage,

C. Vander Velde, P. Vanlaer, J. Wickens

Universit´e Libre de Bruxelles, ULB, Bruxelles, Belgium V. Adler, O. Devroede, S. De Weirdt, J. D’Hondt, R. Goorens, J. Heyninck, J. Maes, M. Mozer, S. Tavernier, L. Van Lancker, P. Van Mulders, I. Villella, C. Wastiels

Vrije Universiteit Brussel, Vub, Brussel, Belgium

J.-L. Bonnet, G. Bruno, B. De Callatay, B. Florins, A. Giammanco, G. Gregoire, Th. Keutgen, D. Kcira, V. Lemaitre, D. Michotte, O. Militaru, K. Piotrzkowski, L. Quertermont, V. Roberfroid, X. Rouby, D. Teyssier, M.

Vander Donckt

Universit´e catholique de Louvain, UCL, Louvain-la-Neuve, Belgium

Universit´E De Mons-Hainaut, Mons, Belgium

E. Anttila, S. Czellar, P. Engstr¨om, J. H¨ark¨onen, V. Karim¨aki, J. Kostesmaa, A. Kuronen, T. Lamp´en, T. Lind´en, P. -R. Luukka, T. M¨aenp¨a¨a, S. Michal, E. Tuominen, J. Tuominiemi

Helsinki Institute Of Physics, Helsinki, Finland

M. Ageron, G. Baulieu, A. Bonnevaux, G. Boudoul, E. Chabanat, E. Chabert, R. Chierici, D. Contardo, R. Della Negra, T. Dupasquier, G. Gelin, N. Giraud, G. Guillot, N. Estre, R. Haroutunian, N. Lumb, S. Perries, F. Schirra,

B. Trocme, S. Vanzetto

Universit´e de Lyon, Universit´e Claude Bernard Lyon 1, CNRS/IN2P3, Institut de Physique Nucl´eaire de Lyon, France J.-L. Agram, R. Blaes, F. Drouhina), J.-P. Ernenwein, J.-C. Fontaine Groupe de Recherches en Physique des Hautes Energies, Universit´e de Haute Alsace, Mulhouse, France

Arxiv:0901.4316V1 [Physics.Ins-Det] 27 Jan 2009

J.-D. Berst, J.-M. Brom, F. Didierjean, U. Goerlach, P. Graehling, L. Gross, J. Hosselet, P. Juillot, A. Lounis,

C. Maazouzi, C. Olivetto, R. Strub, P. Van Hove

Institut Pluridisciplinaire Hubert Curien, Universit´e Louis Pasteur Strasbourg, IN2P3-CNRS, France G. Anagnostou, R. Brauer, H. Esser, L. Feld, W. Karpinski, K. Klein, C. Kukulies, J. Olzem, A. Ostapchuk, D. Pandoulas, G. Pierschel, F. Raupach, S. Schael, G. Schwering, D. Sprenger, M. Thomas, M. Weber,

B. Wittmer, M. Wlochal

F. Beissel, E. Bock, G. Flugge, C. Gillissen, T. Hermanns, D. Heydhausen, D. Jahn, G. Kaussenb), A. Linn, L. Perchalla, M. Poettgens, O. Pooth, A. Stahl, M. H. Zoeller P. Buhmann, E. Butz, G. Flucke, R. Hamdorf, J. Hauk, R. Klanner, U. Pein, P. Schleper, G. Steinbr¨uck P. Bl¨um, W. De Boer, A. Dierlamm, G. Dirkes, M. Fahrer, M. Frey, A. Furgeri, F. Hartmanna), S. Heier, K.-H. Hoffmann, J. Kaminski, B. Ledermann, T. Liamsuwan, S. M¨uller, Th. M¨uller, F.-P. Schilling,

Karlsruhe-Iekp, Germany

P. Cariola, G. De Robertis, R. Ferorelli, L. Fiore, M. Preda,c), G. Sala, L. Silvestris, P. Tempesta, G. Zito

Infn Bari, Italy

D. Creanza, N. De Filippisd), M. De Palma, D. Giordano, G. Maggi, N. Manna, S. My, G. Selvaggi INFN and Dipartimento Interateneo di Fisica, Bari, Italy S. Albergo, M. Chiorboli, S. Costa, M. Galanti, N. Giudice, N. Guardone, F. Noto, R. Potenza, M. A. Saizuc),

V. Sparti, C. Sutera, A. Tricomi, C. Tuv`E

M. Brianzi, C. Civinini, F. Maletta, F. Manolescu, M. Meschini, S. Paoletti, G. Sguazzoni

Infn Firenze, Italy

B. Broccolo, V. Ciulli, R. D’Alessandro. E. Focardi, S. Frosali, C. Genta, G. Landi, P. Lenzi, A. Macchiolo,

G. Cerati

INFN and Universit`a degli Studi di Milano-Bicocca, Italy P. Azzi, N. Bacchettaa), A. Candelori, T. Dorigo, A. Kaminsky, S. Karaevski, V. Khomenkovb), S. Reznikov,

Infn Padova, Italy

D. Bisello, M. De Mattia, P. Giubilato, M. Loreti, S. Mattiazzo, M. Nigro, A. Paccagnella, D. Pantano,

Infn Perugia, Italy

F. Ambroglini, E. Babucci, D. Benedettie), M. Biasini, B. Caponeri, R. Covarelli, M. Giorgi, P. Lariccia, G. Mantovani, M. Marcantonini, V. Postolache, A. Santocchia, D. Spiga

2

G. Bagliesi , G. Balestri, L. Berretta, S. Bianucci, T. Boccali, F. Bosi, F. Bracci, R. Castaldi, M. Ceccanti, R. Cecchi, C. Cerri, A .S. Cucoanes, R. Dell’Orso, D .Dobur, S .Dutta, A. Giassi, S. Giusti, D. Kartashov, A. Kraan, T. Lomtadze, G. A. Lungu, G. Magazz`u, P. Mammini, F. Mariani, G. Martinelli, A. Moggi, F. Palla, F. Palmonari, G. Petragnani, A. Profeti, F. Raffaelli, D. Rizzi, G. Sanguinetti, S. Sarkar, D. Sentenac, A. T. Serban, A. Slav, A. Soldani, P. Spagnolo, R. Tenchini, S. Tolaini, A. Venturi, P. Verdinia), M. Vosf), L. Zaccarelli

Infn Pisa, Italy

C. Avanzini, A. Basti, L. Benuccig), A. Bocci, U. Cazzola, F. Fiori, S. Linari, M. Massa, A. Messineo, G. Segneri,

G. Tonelli

P. Azzurri, J. Bernardini, L. Borrello, F. Calzolari, L. Fo`a, S. Gennai, F. Ligabue, G. Petrucciani , A. Rizzih),

Z. Yangi)

Scuola Normale Superiore di Pisa and INFN Pisa, Italy

Infn Torino, Italy

M.A. Borgia, R. Castello, M. Costa, E. Migliore, A. Romero D. Abbaneo, M. Abbas,I. Ahmed, I. Akhtar, E. Albert, C. Bloch, H. Breuker, S. Butt,O. Buchmuller j), A. Cattai, C. Delaerek), M. Delattre,L. M. Edera, P. Engstrom, M. Eppard, M. Gateau, K. Gill, A.-S. Giolo-Nicollerat, R. Grabit, A. Honma, M. Huhtinen, K. Kloukinas, J. Kortesmaa, L. J. Kottelat, A. Kuronen, N. Leonardo, C. Ljuslin, M. Mannelli, L. Masetti, A. Marchioro, S. Mersi, S. Michal, L. Mirabito, J. Muffat-Joly, A. Onnela, C. Paillard, I. Pal, J. F. Pernot, P. Petagna, P. Petit, C. Piccut, M. Pioppi, H. Postema, R. Ranieri, D. Ricci, G. Rolandi, F. Rongal), C. Sigaud, A. Syed, P. Siegrist, P. Tropea, J. Troska, A. Tsirou, M. Vander Donckt,

F. Vasey

European Organization for Nuclear Research (CERN), Geneva, Switzerland E. Alagoz, C. Amsler, V. Chiochia, C. Regenfus, P. Robmann, J. Rochet, T. Rommerskirchen, A. Schmidt,

S. Steiner, L. Wilke

I. Church, J. Colem), J. Coughlan, A. Gay, S. Taghavi, I. Tomalin STFC, Rutherford Appleton Laboratory, Chilton, Didcot, United Kingdom R. Bainbridge, N. Cripps, J. Fulcher, G. Hall, M. Noy, M. Pesaresi, V. Radiccin), D. M. Raymond, P. Sharpa),

I. Goitom, P. R. Hobson, I. Reid, L. Teodorescu

G. Hanson, G.-Y. Jeng, H. Liu, G. Pasztoro), A. Satpathy, R. Stringer

B. Mangano

K. Affolder, T. Affolderp), A. Allen, D. Barge, S. Burke, D. Callahan, C. Campagnari, A. Crook, M. D’Alfonso, J. Dietch, J. Garberson, D. Hale, H. Incandela, J. Incandela, S. Jaditz q), P. Kalavase, S. Kreyer, S. Kyre, J. Lamb, C. Mc Guinnessr), C. Millss), H. Nguyen, M. Nikolicm), S. Lowette, F. Rebassoo, J. Ribnik, J. Richman,

3

N. Rubinstein, S. Sanhueza, Y. Shah, L. Simmsr), D. Staszakt), J. Stoner, D. Stuart, S. Swain, J.-R. Vlimant,

K. A. Ulmer, S. R. Wagner

L. Bagby, P. C. Bhat, K. Burkett, S. Cihangir, O. Gutsche, H. Jensen, M. Johnson, N. Luzhetskiy, D. Mason, T. Miao, S. Moccia, C. Noeding, A. Ronzhin, E. Skup, W. J. Spalding, L. Spiegel, S. Tkaczyk, F. Yumiceva,

A. Zatserklyaniy, E. Zerev

Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois, USA I. Anghel, V. E. Bazterra, C. Gerber, S. Khalatian, E. Shabalina P. Baringer, A. Bean, J. Chen, C. Hinchey, C. Martin,T. Moulik, R. Robinson

A. V. Gritsan, C. K. Lae, N. V. Tran

P. Everaerts, K. A. Hahn, P. Harris, S. Nahn, M. Rudolph, K. Sung Massachusetts Institute of Technology, Cambridge, Massachusetts, USA B. Betchart, R. Demina, Y. Gotra, S. Korjenevski, D. Miner, D. Orbaker L. Christofek, R. Hooper, G. Landsberg, D. Nguyen, M. Narain,T. Speer, K. V. Tsang a) Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland

D) Now At Llr-Ecole Polytechnique, France

f) Now at IFIC, Centro mixto U. Valencia/CSIC, Valencia, Spain g) Now at Universiteit Antwerpen, Antwerpen, Belgium

J) Now At Imperial College, London, Uk

k) Now at Universit´e catholique de Louvain, UCL, Louvain-la-Neuve, Belgium l) Now at Eidgen¨ossische Technische Hochschule, Z¨urich, Switzerland o) Also at Research Institute for Particle and Nuclear Physics, Budapest, Hungary q) Now at Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

Abstract

In March 2007 the assembly of the Silicon Strip Tracker was completed at the Tracker Integration Facility at CERN. Nearly 15% of the detector was instrumented using cables, fiber optics, power supplies, and electronics intended for the operation at the LHC. A local chiller was used to circulate the coolant for low temperature operation. In order to understand the efficiency and alignment of the strip tracker modules, a cosmic ray trigger was implemented. From March through July 4.5 million triggers were recorded. This period, referred to as the Sector Test, provided practical experience with the operation of the Tracker, especially safety, data acquisition, power, and cooling systems. This paper describes the performance of the strip system during the Sector Test, which consisted of five distinct periods defined by the coolant temperature. Significant emphasis is placed on comparisons between the data and results from Monte Carlo studies.

2.1

The Tracker Safety and Control Systems . .

2.2

Cosmic Muon Trigger . .

Reconstruction

.

3.2

Data Quality Monitoring . .

3.3

Data Processing . .

4.1

Electronic Gain Measurements . .

4.2

Noise Performance Studies . .

4.3

Detector quality . .

5.1

Latency Scan . .

5.2

The signal-to-noise . .

5.3

Signal calibration . .

5.4

Validation of Online Zero Suppression . .

5.6

Hit Reconstruction Efficiency . .

Signal And Noise

.

Capacitive Coupling

.

7.3

Cluster width studies . .

Introduction

The CMS Tracker is the inner tracking detector built for the CMS experiment at the CERN Large Hadron Collider. It is a unique instrument in both size and complexity: it contains two systems based on silicon sensor technology, one employing pixels and another using silicon microstrips. The Pixel Detector, which surrounds the beam pipe, consists of three barrel layers and four disk detectors, two on each side of the barrel. It contains 64 million detector channels. The Silicon Strip Tracker, the subject of this paper, surrounds the pixel system and consists of four major subsystems: the Inner Barrel (TIB), Inner Disks (TID), an Outer Barrel (TOB) and two End Caps (TEC). It is the largest silicon detector ever built, with almost 10 million sensor channels covering a surface area of over 200 m2. The Silicon Strip Tracker (referred to simply as the Tracker in this note) was designed to measure charged particles with high efficiency and spatial resolution over wide range of momenta, and to operate with minimal intervention for the nominal LHC lifetime of 10 years. Consequently, the quality of the construction, and the performance of the components contained within it, are of vital importance and must be thoroughly evaluated under realistic conditions before operation at LHC.

First experience of the operation and detector performance of the Tracker was gained during summer 2006, with a small fraction ( 1%) of the detector inserted in the CMS experiment. Cosmic rays were detected in the presence of a 4 T solenoidal field and all the CMS sub-detectors were read out. The results are summarized in Ref. .

Silicon Strip Tracker construction proceeded during 2006 and 2007: a brief account of the integration and assembly processes can also be found in ; it was an endeavour shared by the entire Tracker collaboration and tasks were distributed throughout much of the world. The final assembly of the Tracker was carried out in a large, purpose- built, clean area at CERN: the Tracker Integration Facility (TIF). This facility was instrumented with a significant fraction of the final infrastructure and services needed to operate, control and read out a sector of the Tracker that corresponds to 15% of the entire detector: cables, patch panels, optical fibers, cooling manifolds and rack mounted power supplies and off-detector electronics were installed to allow connection to data acquisition; cooling, control and safety systems were implemented; a trigger system was devised to detect cosmic rays traversing the Tracker, and dedicated computing resources were provided.

Following assembly of the sub-system and prior to the installation into CMS, the detector was commissioned and operated for several months at the TIF, a period referred to as the Sector Test. The goals of this test included commissioning the active sector of the Tracker under realistic cabling and grounding conditions, establishing the data acquisition, and confirming stable and safe operation under the supervision of the dedicated monitoring systems. The objectives were also to develop and validate monitoring tools, to identify issues in service routing or connections, to establish stable and safe running at low temperature, and to demonstrate operating procedures.

Finally, the acquisition of cosmic ray data allowed the measurement of the detector performance, the understanding of tracking algorithms, and to perform an initial alignment of the active modules. The test progressed in an incremental way, beginning with testing of parts of the sub-systems, then proceeding to a test of the barrel systems, and finally incorporating one endcap. There were also short system tests to check for any interference between the Tracker and a small fraction of the forward pixels: these tests, which are not described in this note, did not show any interference effects.

The Sector Test provided an important opportunity to evaluate Tracker performance in some detail and address any weak points. Several million cosmic ray events were taken under a range of conditions, operating the Tracker over a range of temperatures, from 15 ◦C down to −15 ◦C.

The results obtained from the Sector Test are described in three dedicated papers. This note documents results on the detector performance while results on track reconstruction and alignment are described in Ref. and Ref. respectively.

The Sector Test setup is described in Section 2. The data sets, reconstruction and processing are presented in Section 3, while Sections 4 and 5 are concerned with the detector performance. Section 6 covers the generation of the cosmic muons in the Tracker and the simulation of the detector response. The tuning of key parameters in Monte Carlo simulation studies to match the data is the subject of Section 7.

Sector Test Description

The Silicon Strip Tracker has a barrel region with four layers in the TIB and six layers in the TOB, and a forward region with three disks in the TID and nine in the TEC on each side of the Tracker. Each TID and each TEC disk is organized in three and seven rings respectively, where each ring corresponds to a different radial range and

4

Table 1: Composition of the Sector Test with the different subdetector layers/disks.

15%

different module geometries. For the Sector Test a fraction of the Tracker was powered and read out using the APV25 front-end chip [6, 7], selected so as to maximize the crossing of all the different layers or rings by cosmic rays. The first quadrant in the Z+ side of the Tracker was chosen. Details of Sector Test sub-detectors are shown in Table 1. Sensors were biased with a voltage of 300 V for TIB, TID and TOB, and 250 V for TEC.

The analog signal from the silicon sensors is amplified, shaped and stored in a 192 element deep analogue pipeline every 25 ns. A subsequent stage can either pass directly the pipeline signal (Peak mode) or form a weighted sum of three consecutive samples effectively reducing the shaping time to 25 ns (Deconvolution mode): only results obtained with APV25 in Peak mode have been thoroughly studied at the Sector Test and therefore no Deconvolution mode results will be described. The signal is then multiplexed by the front-end chip APV25. In order to approach the behavior of an ideal CR-RC circuit with a 50 ns shaper, the APV25 settings can be optimized depending on the value of input capacitance and operating temperature. Laboratory studies were made to evaluate the optimal settings for specific geometries over a range of temperatures which were used during the Sector Test.

The signals from each APV25 channel are amplified, converted to light by an Analog Opto Hybrid (AOH) and sent via optical fibers to the Front End Driver board (FED) [9, 10] where they are digitized and further processed, prior to transmission to the central DAQ system. Data can be taken in two different modes: Virgin Raw (VR) or Zero Suppressed (ZS). In VR mode, all channels are read out with the full 10 bits ADC resolution. In ZS mode, the FED applies pedestal subtraction, common mode rejection and a fast clustering algorithm, using signal height with a reduced 8 bits resolution: only channels forming a cluster are output. Almost all cosmic ray data were taken in Virgin Raw mode since the low cosmic trigger rate did not require data reduction, and VR running allowed offline optimization of thresholds.

Clock, trigger signals, and slow control communication with the front-end electronics are managed by the Front End Controller (FEC) boards and sent via optical fibers to the Digital Opto Hybrid (DOH) [12, 13] for each control ring of the Tracker: signals are distributed by the DOH to every Communication Control Unit (CCU) in a control ring. Finally each CCU sends signals to Tracker modules, in particular clock and triggers via a Phase Locked Loop (PLL) circuit on each module . It receives monitoring data provided by each module from the Detector Control Unit (DCU) chip .

FEDs and FECs were read out during the Sector Test via VME bus, rather than the high speed S-Link interface to be utilised in CMS. The DAQ software developed to configure and manage readout of Tracker data is based on XDAQ applications which also provided algorithms to commission the detector .

The services needed consisted of 277 LV/HV cables, 52 control cables, 73 optical fiber cables and 32 cooling loops. The electronics racks required 145 power supply modules, 17 power supply control modules, all of which were units from the final system , 65 FEDs and 8 FECs for a total of 41 control rings.

The cooling plant used for the Sector Test was simpler than the final system and its cooling power limited. A min- imum operating temperature of −10 ◦C was obtained, compared to −25 ◦C in the final system. The temperatures measured at the cooling tubes proved to be very stable with variations of less than 0.1 ◦C. A test was possible at a temperature of −15 ◦C but only by limiting power to half of the Sector Test modules.

Dry air was supplied inside to maintain a sufficiently low dew point to avoid condensation; it flowed in a semi- hermetic tent that contained the Tracker.

The Tracker Safety And Control Systems

The Tracker Safety system (TSS) is designed to guarantee protection of the Tracker. It is self-contained and operates on information provided by a thousand hardwired sensors. A system based on Programmable Logic

5

Controllers (PLC) handles the process of monitoring those sensors and taking actions depending on the monitored values, which consist of digital values of temperatures, humidities and TIF, or CMS cavern, information. The TSS will interlock the Tracker power according to a user defined scheme when either a single temperature sensor or a group of sensors varies outside user-defined safety limits, or when the TIF or cavern systems fail to respond.

During the Sector Test two TSS sections, out of the final six, were fully functional and the Tracker was reliably interlocked on several occasions on over-temperature, cooling plant failure, power cuts, or other alarm conditions. The Tracker Control System (TCS) [20, 21] handles all interdependencies of control, low and high voltages, as well as fast ramp-downs in case of higher than allowed temperatures and currents in the detector, or in case of general unsafe conditions detected in the experiment cavern. The size and complexity of the CMS Tracker imposes several demands on the control system to ensure the safety and operation of the modules; the TCS evaluates about 10 000 power supply parameters, nearly 1000 parameters from TSS and 100 000 parameters from the DCUs situated on all front hybrids and CCU modules. The TCS should intervene before the TSS system enters into action, allowing a gentle switch off of parts under risk, and with a finer granularity than the TSS. During the Sector Test about 20-25% of the final system was controlled by the TCS/TSS.

The basic software building block is the commercial SCADA (Supervisory Control And Data Acquisition) software PVSS (Prozess- visualisierungs- und Steuerungssystem by ETM ). This has been greatly extended by CERN in a common LHC framework, adapting it to the needs of individual experiments by incorporating local modifications.

The LHC features also extend functionalities such as archiving of values and the treatment of alarms, warnings and error messages. The Sector Test enabled final implementation of the full TCS to PLC communication including the downloading of parameters, such as sensor limits and sensor-group to interlock-grouping, to the PLC. Complete calibrations inside PVSS were established, including all ADC to physical value conversions. Several additional checks were introduced from PVSS to PLC programming: for example, a minimal number of sensors should participate in interlock voting or limits required to stay in a sensible region. A full checkout interlock routine was developed.

The complexity of the TCS system required a network of PCs for the distribution of requests. The Sector Test was the perfect test bed to establish, check, and tune the distribution needed for the final system as performance needs for the Sector Test were very close to those of the final system. The main bottleneck was found to be memory but the OPC performance of the CAEN system was also identified as a critical item.

Cosmic Muon Trigger

The cosmic trigger configuration was designed to allow studies of tracking performance and detector alignment. The trigger design was constrained by space above and below the Tracker; in particular clearance below the Tracker allowed only 5 cm lead bricks for filtering low momentum tracks. Six scintillators (T1, T2, T3, T4, T5, T6) were placed above the Tracker, in a fixed position; below the Tracker there was initially only one scintillator (B0) mounted on a movable support structure; later a further set of four scintillators was added (B1, B2, B3, B4) to increase the trigger acceptance. The dimensions and the single trigger rates of all scintillators are shown in Table 2.

The upper scintillator signals were synchronized by simple NIM logic using cosmic rays and put in a logical-OR to obtain a Top-scintillator signal. This was put in coincidence with the lower scintillator signal. The coincidence rates are shown in Table 2. From the entries it can be seen that efficiency of the upper scintillators was not uniform.

In the same way from synchronized logical-OR signals of scintillators B1, B2, B3, B4 a Bottom-scintillator signal was obtained and was put in coincidence with the Top-scintillator signal. The rates are shown in Table 2. They show good performance: the discriminator threshold allowed to obtain a very uniform coincidence rate.

Three main trigger configurations have been used at the Sector Test: (TA)= (Top-Scintillator & B0) vertical po- sition; (TB)= (Top-Scintillator & B0) slanted position; (TC) = (TA) plus (Top-Scintillator & Bottom-scintillator) slanted position. The schematic representations of these three configurations are shown in Fig. 1. The rate of spurious coincidences relative to the true level was of order 10−9. The trigger rates achieved were: 3.5 Hz (TA), 1.5 Hz (TB), and 6.5 Hz (TC). Since the DAQ rate was limited to about 3 Hz by the FED readout via VME, a trigger veto was implemented to keep the rate under this level.

(C)

Figure 1: Layout of the various trigger scintillator configurations used during the cosmic data taking at the TIF (in chronological order): (a) configuration TA; (b) configuration TB; (c) configuration TC. The xy view is shown on the left side, the rz view is shown on the right. The straight lines connecting the active areas of the top and bottom scintillation counters indicate the acceptance region.

Table 2:

Dimensions of scintillators used at Sector Test; single and coincidence rates.

Data Sets And Reconstruction

The division of the data in different sets, is detailed in Table 3, where run number intervals are specified for the participating detectors, the trigger configurations, and the operating temperatures. The APV25 parameters listed in the table are for specific hybrid temperatures (TAPV25) and were studied for TIB and TOB detector modules only.

For the TID and the TEC (where a large number of variants exist) the APV25 parameters have not been optimized for each module type. Instead the TID used the TIB parameters while the TEC used parameters very close to those of the TOB.

Each run was checked using online and offline data quality monitoring tools. If a run did not meet the quality requirements or if a configuration or hardware problem was discovered, it was flagged as bad and excluded from the offline analysis.

Table 3: Sector Test data sets.

Reconstruction

Event reconstruction, event selection, data quality monitoring, simulation and analysis of the Tracker Sector Test at the TIF were performed within the CMS software framework known as CMSSW. Hit and track reconstruction was performed offline taking the raw data or the simulation data as input. A reconstruction job is composed of a series of applications executed for each event in the order specified by a configuration file. A fundamental part of the processing is the availability of non-event data such as cable map, pedestal, alignment constants and calibration information.

The first step consists in mapping ADC counts for individual strips as they are coming from the FED output, into objects that are uniquely assigned to a specific detector module exploiting the cabling map information stored in the configuration database. In the case of data collected without performing online zero-suppression (VR), the necessary pedestal information must be acquired from the database. At this point, the input data files, whether real data or Monte Carlo simulated events, contain the same information and can be further processed with identical

8

code. A three-threshold algorithm, described in detail in the CMS Physics TDR, is used to form clusters. The cluster seed is defined as a strip whose charge is at least three times greater than the strip noise, while neighboring strips are added if their charge exceeds twice their strip noise. The cluster is kept if the total cluster charge is more than five times the cluster noise level, defined as the quadratic sum of all the strip noise values. Finally, the position of the cluster is calculated as the centroid of the individual strip charges.

Three tracking algorithms have been applied: the Combinatorial Track Finder, the Road Search and the Cosmic Track Finder. The Combinatorial Track Finder and the Road Search are reconstruction algorithms developed for p −p collisions, while the Cosmic Track Finder is a specialized code for reconstruction of single cosmic track events . The input to the tracking algorithms are reconstructed hits as described above and information from the alignment constants (either from survey or from alignment calibration studies).

All the tracking algorithms employ the same three steps: seeding, pattern recognition and track fitting. The seeding and the pattern recognition are specific to each algorithm, while the final fit is the same for all of them. Since no magnetic field was present the tracks are extrapolated as straight lines. Material effects, energy loss and multiple Coulomb scattering are estimated each time a track crosses an active layer. The amount of material at normal incidence is estimated via the reconstruction geometry developed for p −p collisions. Since the momentum of the track is not measured, a constant value of 1 GeV/c is assigned, close to the expected average from simulation.

More detailed information about the algorithms and their performance can be found in Ref. . All the results presented in sections 5, 6 and 7 of this paper refer to tracks obtained only with the Combinatorial Track Finder algorithm to avoid duplication of information.

Data Quality Monitoring

The Data Quality Monitoring (DQM) system for the Tracker is designed to ensure that good quality data are recorded and detector problems are spotted very early on. The system is based on the “Physics and Data Quality Monitoring” framework of CMS. The task is fulfilled in three steps : (a) histograms, called Monitoring Ele- ments (MEs), are defined and filled with relevant event information by the “Producer” (DQM Source) application; (b) a “Consumer” (DQM Client) application accesses the MEs, performs further analysis and generates alarms; and (c) the “Graphical User Interface” (GUI) provides tools for visualization of the MEs.

The MEs are defined in the DQM source at various levels of data reconstruction chain. Starting from the level of pedestal, noise, digitization, cluster reconstruction and finally track-related properties are defined and filled in various MEs.

The DQM Client accesses low-level MEs and performs further analysis on them and creates summary MEs, per- forms quality tests comparing MEs with references to generate alarms. The summary plots are important as it would be too time consuming to check each of the huge set of MEs in the Tracker, which consists of over fifteen thousand detector modules. The information from detector level MEs is accessed and summarized in MEs at higher levels following the geometrical structure. Similarly the detector level MEs are compared with reference MEs or parameters and “Ok”, “Warning”, and “Error” alarms are generated, as appropriate, based on the comparisons.

The DQM system was operational with full functionality during TIF data taking. Events were accessed from the on-line data and full reconstruction was performed on an event by event basis and provided to the DQM sources. The system was stable during operations and the Tracker DQM GUI, which is a web based application, was accessible from CERN as well as from other institutes involved in the Tracker activities.

Data Processing

The tracker data processing consisted of the following steps: data archiving, conversion of data from the raw format to the CMS Event data Model (EDM) format based on ROOT, registration in the CMS official data bookkeeping services, transfer to remote computing tiers, data reconstruction and analysis in a distributed comput- ing environment.

Data taking and analysis needed a large amount of available disk space, since both raw data and on-site local analysis results had to be stored centrally in a safe way. The design chosen for the computing model was a centralized one, where all the data are stored on a single machine which is connected via NFS to the local network.

A room containing all PCs and local storage was equipped to allow the data taking control, monitoring and first analysis of the data and it is referred to as the Tracker Analysis Center (TAC).

9

All the data collected by the tracker detector were written into a main storage machine at the TAC, which behaved like a temporary data buffer. Data were backed-up to CASTOR storage at CERN every five minutes, copying data files only when they did not exist on CASTOR side and had not been accessed in the last hour (to prevent the copy of data still being modified). Once all the files belonging to a run were copied to CASTOR, a catalog was prepared for that run after a few checks. Because of the limited resources at TAC and following the CMS computing and analysis model, most of the processing of the data was done in remote sites as soon as data were officially published and transferred to them. The transfer was performed to remote sites using the CMS official data movement tool PhEDEx . Tracker data were transferred and registered successfully at Bari, Pisa and FNAL.

The datasets have been reprocessed several times to feed back into the reconstruction phase the improvements in the alignment and calibration corrections obtained from the analysis. The final reprocessing was performed in FNAL in December 2007.

Detector Performance Based On Calibration Data

Figure 2: Hybrid temperature distributions for TIB, TID, TOB, and TEC at a coolant temperature of 14 ◦C. The performance of the Tracker depends on front end electronics supply voltage and operating temperature. These quantities are measured by the DCU chip located on each module hybrid, which can be read out digitally via the control ring protocol through the electronic chain (DCU, CCU, DOH, FEC).

Results obtained for the hybrid temperature are shown in Fig. 2 and for sensor temperature in Fig. 3 for 14 ◦C coolant temperature. All TOB layers show the same temperature, while for TIB, TID and TEC differences are visible: TIB layers 1 and 2, TID ring 1 and 2, and TEC ring 1, 2 and 5 are double-sided layers and have a temperature substantially higher compared with single-sided layers; the effect is more pronounced for the hybrid than for the sensor. These differences are expected to be lower with the final cooling system.

It is also apparent that three modules of TIB layer 3 have a higher temperature of about 15 ◦C; they are in a string where the cooling pipe was blocked. Results from the other operating temperatures are consistent with those obtained at 14 ◦C.

The power supply system provides 2.5 V and 1.25 V lines to the modules, correcting for voltage drops along the long power supply cables by sensing the voltage levels as close to the modules as possible. Nevertheless, there still remains a small cable resistance which causes a non-negligible voltage drop. The operating voltage of the modules is measured by the DCU and results are shown in Fig. 4 for the 2.5 V line and in Fig. 5 for the 1.25 line. The average is below nominal for all layers of sub-detectors. These measurements show that it is possible to evaluate these voltage drops and therefore to eventually better equalize the module voltages to match the requirements.

10

Figure 3: Sensor temperature distributions at a coolant temperature of 15 ◦C for TIB, TID, TOB, and TEC. Figure 4: 2.5 V voltage line value measured at the module for TOB, TIB, TEC, and TID.

Electronic Gain Measurements

This section describes how the electrical gain of the Tracker system is both determined and adjusted, that is, the procedures by which the gains of individual APV25s are adjusted to a common and known value. The readout chain for the Tracker involves pairs of adjacent APV25s, the hybrid MUX, the AOH , an optical fiber, and the FED . Instabilities in the low or high voltages or changes in temperature can affect the gain. Measuring the electrical stability of the Tracker as a function of time, voltage, temperature, and other variables, should lead to an improved understanding of the likely performance of the full Tracker system during actual LHC operations.

The commissioning procedure determines optimal settings of the electronics to achieve a uniform electronic

11

Figure 5: 1.25 voltage line value measured at the module for TOB, TIB, TEC, and TID. gain. This is obtained by measuring the value of the height of synchronization pulses, referred to as tick-marks, generated by the APV25 which provide a stable value that can be used as reference to equalize the response of the full electronic chain. It is required that the tick-mark height of each APV25 be within the FED dynamic range and, by varying the AOH offset and gain, should correspond closely to the target value of 640 ADC channels. The full commissioning procedure was consistently applied whenever there were changes in coolant temperature, changes in APV25 parameters, or in the hardware configuration. Commissioning runs, referred to as timing runs, provide precise measurements of the tick-mark heights, therefore of the electronics gain, and were repeated at intervals between full commissioning runs to get more statistics on stability of the system.

Tick-mark height distributions obtained after the commissioning procedures are shown in Fig. 6. The several gray levels represent the components with different AOH gain settings (called 0, 1, 2, 3 from lower to higher gain): the difference between the left and right distributions is mainly due to the strong dependence on the temperature of the AOH gain. The effect is manifest by the increased number of AOH with gain equal to 0 at lower temperature. This implies that the average values of the tickmark height distribution changes when varying the temperature.

Figure 6: APV25 tickmark height distribution after commissioning procedure: run 7096, taken at T = 14 ◦C (left), and run 11176, taken at T = −10 ◦C (right). Even after the commissioning procedure the tickmark height distributions still indicate about a 10% spread in electrical gain. This variation is consistent with the coarse precision of the AOH laser gain settings. An offline

12

calibration of the electronic gain is necessary to improve the precision of the measurement of noise and signal. This is achieved by normalizing to an assumed digital header of 640 ADC counts. In the following sections, the analysis of signal and noise will take into account this calibration if not otherwise stated, by applying a correction

(1)

One limitation in the use of this equation is due to the different module operating voltages for different layers. It was known that the tick-mark amplitude is linearly proportional to the 2.5 V operating voltage, therefore tickmarks from different modules can be compared only if they operate at the same voltage. The signal at the APV25 amplifier output is not much affected by changes in the supply voltage. Therefore to make a more precise estimate of the electronic gain it is necessary either to equalize all the operating voltages or to correct the tick-mark value for the difference compared to 2.5 V. In this paper this correction was not applied therefore the electronic gain can be considered having a systematic variation of about 5% that affects direct comparison between different layers.

Tec

Figure 7: Tickmark height distribution versus run number. Error bars represent the statistical error of the average value. It is important to understand the stability of the electronics gain, since calibration runs are taken only at selected times. In the analysis of the timing runs, bad modules have been discarded and a selection of good commissioning runs have been made. Figure 7 shows average tick-mark heights for individual sub-detectors as a function of run number (with the help of Table 3 the period at different temperatures can be identified). At least one timing run was taken as part of the commissioning procedure whenever the coolant temperature changed. During the period at T = 14 −15 ◦C, in particular, there were several timing runs, which provide information on the stability of the system.

TOB shows, in 7 runs, variations of ±0.6%. The TIB/TID shows a large discrepancy in the first run (6094), where results are lower by 5%, but this is due to the fact that the modules were operating at low electrical gain and therefore that was corrected by a very high AOH gain: after removing this run the TIB shows, in 6 runs, variations of ±0.6%. The TEC shows, in 4 runs, ±0.4% variations.

Noise Performance Studies

Prior to irradiation, the noise of a module is almost completely determined by the input capacitance load at the APV25, which in turn is dominated by the silicon strips. Thus, within 8%, one expects a linear dependence of the noise on the length of the silicon strip for all modules.

In the Sector Test, modules were mounted on the final support structures and therefore were in close proximity to each other, which could have exposed APV25 inputs to other sources of noise. In particular grounding loops, cross- talk from neighboring modules or other noise sources, (digital noise, cables, power supply) could have affected

13

Figure 8: TIB noise profile for T= −10 ◦C for layers 1, 2, 3, and 4. Gaussian fit is shown. X axis is ADC value. Figure 9: TOB noise profile for T= −10 ◦C for layers 1, 2, 3, 4, 5 and 6. X axis is ADC

Value

the final noise performance. Moreover, a study of the temperature dependence is important. The commissioning procedure plays an important role, since it optimizes many system parameters and consequences are observed

14

Figure 10: Noise profiles for T= −10 ◦C for different rings of TEC and TID. Gaussian fits are shown. R2 and R3 silicon geometries are the same for TID and TEC. X axis is ADC value on the noise performance. In the following the noise has been renormalized for each pair of adjacent APV25, belonging to the same AOH laser, in order to take into account the different electronic gains, by multiplying the noise for the correction factor specified by equation 1.

Modules with known problems that affect the noise distribution calculation have been removed from the analysis at least for the period when the problem was present.

15

Strip noise distributions for runs taken at −10 ◦C are shown for each layer for TIB and TOB in Fig. 8 and Fig. 9 respectively, while for TEC and TID are shown in Fig. 10. For each distribution, a fit to a Gaussian has been performed and results are shown. For most of the layers the noise distribution is very well represented by a Gaussian, and fitted values and sigmas are almost identical for identical layers, showing extra noise sources do not affect the detector performance significantly.

The only relevant non-Gaussian tails at high values are visible for TOB layers 2, 3 and 4. They are mainly due to channels close to APV25 edges and only for modules at specific positions within a rod, closer to a clock distribution board and a power cable. This noise pickup does not affect the TOB performance, given its high signal to noise ratio, as shown in the next section. Nevertheless, during the Sector Test many possible grounding and filtering schemes were investigated in order to minimize this extra noise. A solution which was found to be very effective consisted in grounding the TOB power cable shields at the patch panel close to the Tracker. This grounding implementation was possible on the Sector Test setup only for a fraction of the TOB, therefore the tails remained for most of the module rods. This grounding scheme has subsequently been implemented during installation of the Tracker inside CMS.

Stability of the noise performance was studied by taking pedestal and noise runs at different times when the Tracker was running in a stable conditions with fixed electronics configuration settings. Results are shown in Fig. 11 displaying noise of all layers/rings versus the run number for TIB and TOB: the steps in values represent the different temperatures considered, 10 ◦C,0 ◦C, −10 ◦C and −15 ◦C. The data average and mean value of a Gaussian fit are displayed as solid and open symbols respectively. For constant coolant temperature the noise is stable to better than ±0.5%. Most importantly, the noise decreases with decreasing temperature as expected by the laboratory studies made on the APV25 performance.

Figure 11: Noise vs run number for TIB on the left and for TOB on the right. Four periods at different temperatures

Are Visible: 10 ◦C, 0 ◦C, −10 ◦C And −15 ◦C

. The (Gaussian fit) mean strip noise obtained for each different type of module has been correlated with the module strip length. Results are shown in Fig. 12 for a run taken at −10 ◦C, but similar results are obtained at the other temperatures. Error bars represent the spread of the mean module noise for each module type. As expected the behavior is well represented by a linear behavior, within statistical fluctuation, for modules from the same layer, although the average values show differences for the same strip length but different module geometries. This can be explained by the difference in supply voltage and by the different APV25 settings used for different sub-detectors.

Detector Quality

Faulty channels have been studied both from the perspective of badly behaving modules or missing fibers and of individual bad channels. Modules with known problems or that were badly behaving were removed either from DAQ or from the data anal- yses. The resulting fraction of missing modules was at the 0.5% level. Dead fibers were identified during timing runs based on low tick-mark heights. They correspond to the broken fibers whose channels showed problems during the timing runs. The number of missing fibers in the Tracker was at the 0.1% level.

Remaining isolated bad channels were identified having higher than five sigmas, referred as noisy, or lower than

16

Figure 12: Noise vs strip length for T= −10 ◦C. five sigmas, referred as dead, noise compared to the average noise per module, and this was done for each module geometry of the Tracker.

The results of this analysis as a function of the run number are shown in Fig. 13. The number of dead channels is almost constant among several runs for all sub-detectors, showing that the identification of dead channels is clear and stable. The noisy components are instead subject to fluctuations, in particular for TOB and TID. The fraction of dead (noisy) strips is 0.05% (0.04%) for TIB, 0.04% (0.15%) for TID, 0.04% (0.3%) for TOB, and 0.08% (0.02%) for TEC.

Since the analysis of defective strips is made on a per run basis, it is important to understand the number of runs in which a strip was identified to be bad. Dead strip identification is stable: the majority of dead strips (70%) were flagged in all runs. About 30% of the classified dead strips appear only in a single run which likely had a timing issue or other unusual problem. On the contrary, only a small fraction of the noisy strips were noisy throughout the Sector Test. In most cases anomalous noise persists for one or two runs at the most. These runs may reflect special operating conditions or non-optimal system configuration.

Finally, a comparison of the identified faulty channels with data from the Tracker construction database shows that 90% of the dead channels and 40% of the noisy channels had been flagged as such by the end of the construction period.

Detector Performance Based On Cosmic Ray Data

The signal performance of the Tracker is very important; it depends on several factors: charge collection of the silicon sensors, performance of the APV25 with well defined parameters, performance of the full electronic chain. A study of cosmic trigger timing is presented in the first subsection, to verify that the maximum signal was taken for all the runs. As tracking efficiency relies on a sufficiently large signal to noise ratio (S/N) on all Tracker modules, it is important to measure the S/N on a layer by layer basis. Moreover, S/N analysis does not depend on the gain calibration. Lastly, analysis of the signal size allows a straightforward comparison of the performance of different layers as a means of determining the absolute gain calibration and measurements of temperature dependence.

In order to obtain a high purity signal, only those hits that are associated with a reconstructed track have been used. The CTF track algorithm was used and only tracks with χ2/d.o.f. < 30 were considered. Only events with low track multiplicity (less than three) and with a low hit multiplicity (less than 100) were considered.

The energy (Stot) deposited in Tracker modules can be parameterized:

Authors:

Peder EZ Larson 1, 2,* , Jenna ML Bernard1, James A Bankson 3, Nikolaj Bøgh 4, Robert A Bok1, Albert P. Chen 5, Charles H Cunningham 6,7, Jeremy Gordon1, Jan-Bernd Hövener 8, Christoffer Laustsen 4, Dirk Mayer 9,10, Mary A McLean11 12, Franz Schilling13, James Slater1, Jean-Luc Vanderheyden5, 14, Cornelius von Morze 15, Daniel B Vigneron1, 2, Duan Xu1, 2, and the HP 13C

94143, Usa.

Denmark. 5 GE Healthcare, Menlo Park, California, USA. 6 Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.

8 Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Medicine, Baltimore, MD, USA. Cambridge, United Kingdom.

14Jlvmi Consulting Llc, Dousman, Wi, Usa

#See Acknowledgements for a list of all HP 13C MRI Consensus Group Members This work was supported by the ISMRM Hyperpolarized Media MR Study Group, the ISMRM Hyperpolarization Methods & Equipment Study Group, and the Hyperpolarized MRI Technology Resource Center (NIH/NIBIB grant P41EB013598).

Abstract

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the “HP 13C MRI Consensus Group” as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality.

Keywords: Hyperpolarized MRI, metabolic imaging, carbon-13, pyruvate, dissolution dynamic

Introduction

MRI with hyperpolarized 13C agents, also known as hyperpolarized (HP) 13C MRI, has shown great potential as a novel imaging modality, particularly for its ability to probe metabolic processes in real time. The first human studies with HP [1-13C]pyruvate were performed in 2011 in prostate cancer patients (1).

Since then, there have been over 60 papers published with imaging results of human subjects from 13 different sites, with applications including prostate cancer, brain tumors, breast cancer, kidney cancer, pancreatic cancer, metastatic disease, liver disease, ischemic heart disease, diabetes and cardiomyopathies. The vast majority of these studies used [1-13C]pyruvate (1–63), where [2-13C]pyruvate (64) and 13C-urea (56) have been demonstrated too.

As clinical HP 13C MRI advances, there is a growing need to build consensus for best practices, which are critical for comparing data across sites, performing multi-site trials,deploying methods to new sites, partnering with vendors, and potentially for obtaining broader regulatory approvals.

In March 2022, we initiated an effort to build consensus within the HP 13C MRI community with this opportunity in mind, and it was greeted with strong enthusiasm. The “HP 13C MRI Consensus Group”, containing over 55 members from 27 sites, identified the area of greatest need and opportunity for consensus building to be HP [1-13C]pyruvate human

●

Pyruvate is the most mature and widely used HP agent and has the most significant translational evidence emphasizing the potential clinical impact.

●

Clinical trials, particularly multi-site trials, have the strongest need for consensus methods to ensure that data can be combined across sites. This work is a Position Paper for which the goal is to describe current successful practices and study methods for HP [1-13C]pyruvate human studies along with justification to support those practices. This is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification (Fig. 1). The current successful practices and study methods include a literature review of published peer-reviewed journal papers showing human HP [1-13C]pyruvate study data, up to September 2022 (1–63), as well as new unpublished information from surveys of HP 13C study sites. Based on this information, we also highlight the evidence gaps, strengths, and limitations of current practices which are summarized at the end of each section.

Figure 1: Illustration of the HP 13C MRI human study process, including the 4 major areas covered in this paper: Hyperpolarized 13C-pyruvate preparation, MRI system setup and calibration, Acquisition and Reconstruction, and Data Analysis and Quantification.

Figure 2: Anatomical targets of HP [1-13C]pyruvate MRI human studies published up to September 2022.

Hyperpolarized 13C-Pyruvate Preparation

This section covers the processes for creating the HP agent, 13C pyruvate, and will include many aspects and considerations that are needed to safely and effectively prepare doses for metabolic imaging studies in human subjects. These include material, personnel, equipment and facility, fluid path preparation, quality control, and release.

It is helpful to understand that the specifications of a dose of 13C pyruvate suitable for in vivo MR HP metabolic imaging were shaped in part by early preclinical studies performed by GE HealthCare summarized in Ref. (65). In short, the safety of the two novel drug components, 13C pyruvate and the electron paramagnetic agent (EPA) AH111501, were demonstrated in those studies. The more precise formulation of the dose suitable for human use was then determined from clinical studies (66) that included two Phase 1 clinical trials in young and elderly healthy volunteers without hyperpolarization of the 13C nuclei and another Phase 1/2a dose escalation and imaging feasibility study with HP 13C pyruvate in 31 prostate cancer patients at the With the exception of the first HP 13C imaging clinical trial, which utilized a prototype device in a cleanroom (1), all HP 13C studies performed in humans to date have utilized the SPINlab polarizer (manufactured by GE HealthCare). Consequently all doses of the HP 13C pyruvate delivered by SPINlab have been produced using the “SPINlab Pharmacy Kit” that serves as the container-closure system for the various drug components (13C pyruvic acid and EPA mixture, dissolution medium, and neutralization and dilution medium) during sample polarization, dissolution and quality control (QC) processes. Thus many aspects of the HP sample preparation considerations discussed below are related to the SPINlab instrument and the consumables designed to be used with it (67).

General Considerations

While more than 860 patients or healthy subjects having been injected with HP 13C pyruvate as of January 2022 without reports of any serious adverse events (68), HP 13C pyruvate injection remains an investigational MR contrast agent and can only be administered by those with Investigational New Drug (IND) exemption from the Food and Drug Administration (FDA) in the USA, a Clinical Trial Application (CTA) in Canada, approval from National Research Ethics Committee Services in the UK, or approval from the relevant local regulatory body. Thus, methods and processes involved to produce a dose should have patient safety as the first priority. Since utilizing dissolution dynamic nuclear polarization (dissolution-DNP) for human use is still a relatively new development, there are no existing published regulatory guidelines specifically for this method.

There are two major production styles that determine how various sites approach the agent preparation. In the US, the most common approach is to rely on a sterilizing filter (“Terminal Sterilization”) to ensure sterility of the final product, akin to PET tracer production, where a starting molecule with a radioisotope is processed using various other ingredients to make the final, desired and injectable contrast agent within a necessarily short amount of time (69). For these sites, sterilization of the components and accessories upstream of this filter are not required, although many of them were manufactured and tested following Good Manufacturing Practice (GMP) or Good Laboratory Practice (GLP) requirements. The filling process is usually performed under an ISO 5 laminar flow hood, but a clean room or an isolator is not required.

This approach is typically accompanied by testing the integrity of the sterilizing filter prior to release of the dose for injection. Typically, post release endotoxin and sterility tests are performed using an aliquot reserved from each released dose.

In the UK and EU, the most common approach is to more-closely follow sterile pharmaceutical compounding guidelines (70), where all components and ingredients are required to be sterile or manufactured under GMP guidelines and are assembled and filled within a clean room environment or an isolator system (“Sterile Preparation”). Typically a batch of Pharmacy Kits for HP 13C pyruvate injection are prepared together. The sterility of the final dose is also ensured by batch validation testing, in addition to the sterility of the ingredients and the sterile compounding process. The endotoxin and sterility testing are performed for the process validation but are not performed for each injected dose.

Some institutions fill and assemble the Pharmacy Kit required for a specific study on the same day or the day prior to polarization, dissolution, and patient administration, but others have also demonstrated the feasibility of preparing a batch of kits, keeping them in a -20ºC freezer and using them over a period of a few months.

Beyond the obvious requirements that the process and the facility has to ultimately produce a dose that is safe to inject into a human, regulatory authorities will also focus on the question “Are you in control of your processes?”. To be in control of your process requires an in-depth and broad understanding of all processes involved in pre, post, and during the production process.

Personnel

It is typical and may be required to have licensed personnel involved in the production process depending on local regulations.Typically a pharmacist, radiopharmacist or other similarly qualified person (QP), in charge of the facility where the Pharmacy Kit filling and preparation is taking place, is responsible for the overall process and the release of the injectable dose.

Qualified cleanroom technicians are often involved in the Pharmacy Kit filling under the supervision of the pharmacist or QP. As is required for pharmaceutical compounding or PET tracer production, training requirements and training records for all personnel need to be maintained and available for audit by the FDA or equivalent.

Equipment And Facility

The facility and all equipment need to have standard operating procedures (SOPs) that describe how equipment is used, maintained, and calibrated to comply with relevant legislation. Currently, almost all the filling of the Pharmacy Kit takes place within a compounding laminar flow hood or isolator (typically ISO 5). At some sites, the filling is conducted within a cleanroom, while at others, it is conducted in a dedicated non-cleanroom space, reflecting differences in cleanroom approach and specifications between regulators worldwide (71). Some equipment or facilities, such as the compounding hood or cleanroom, may require external certified laboratories for testing.

Material Handling

Material handling guidelines (69,70) require SOPs detailing a system to track all of the materials involved in the HP production process for a particular patient dose, similar to current good manufacturing practice (cGMP) requirements for material handling for drug compounding. This includes acceptance standards, storage conditions, amount used in the patient dose for each ingredient and materials used in the assembly of the fluid path and Pharmacy Kit. Currently some users choose to open and inspect and sometimes modify the Pharmacy Kits upon arrival, but some users keep them in the sealed packaging until they are required for dose preparation.

Pharmacy Kit Filling And Assembling

As required by an IND or its equivalent, the preparation of the doses of HP 13C agent are detailed in the Chemistry, Manufacturing, and Control (CMC) section of an applicable regulatory submission; an example of this has been made available (72). It describes the processes of filling the Pharmacy Kit with the different components that make up the final drug product, and of assembling the final kit for either storage or immediate use in the polarizer. Special attention should be given to the laser welding process in order to satisfy installation qualification (IQ) and operational qualification (OQ). Typically, the final developed process is validated by process qualification (PQ) runs, during which 3 or more Pharmacy Kits are filled and used and the final HP 13C products are tested for endotoxin and sterility and to confirm that they meet the dose specifications for injections (usually including pyruvate concentration, residual EPA concentration, pH, liquid state polarization level and dose temperature). The data from 3 consecutive PQ runs are submitted as part of the IND submission (or its equivalent), and are often also reviewed by the Institutional Review Board (IRB) where the studies are conducted.

Quality Control And Dose Release

The quality control (QC) and dose release can be separated into two aspects: one is the QC and release of the filled Pharmacy Kit, and second is the QC and release of the HP 13C agent for injection, after polarization and dissolution. For institutions filling a batch of kits and storing them to use over a period of time, typically the batch can be released based on initial validation, environmental monitoring data from the day of kit production, and if filters are used during preparation of any of the components, filter integrity testing. But in some cases one or more kits are used for validation before the batch of kits are released for future use. For institutions that fill only the kits required for specific studies shortly before the experiment, the filled kits often do not go through separate release tests before they are used.

The quality control of the HP 13C pyruvate solution post dissolution is primarily performed to ensure that the agent meets the dose specifications (Table 1) before it is administered to the subject. These specifications target both safety (pH, residual EPA, temperature) and efficacy (pyruvate concentration, polarization, volume). Typically, the pyruvate concentration, residual EPA concentration, pH, dose temperature, dose volume, and liquid state polarization are measured by the QC accessory associated with the SPINlab polarizer. Some users perform a secondary measurement for one of the parameters, such as pH, using a different instrument or pH paper. For sites that do not go through a separate release testing process for batch filled kits, the integrity of the sterilization assurance filter, a part of the Pharmacy Kit, is typically tested as a part of the dose release. It is also common for these users to preserve an aliquot of the final HP 13C pyruvate solution for post-release endotoxin and sterility testing. This testing cannot be completed fast enough to test an individual dose prior to injection, but this is why other processes such as PQ runs and validation testing are done to minimize the chance a subject could be injected with a contaminated dose.

The Final Dose Release And Injection

should be done under the supervision of a licensed professional, based on local regulations.

Some Key Challenges

Many of the challenges associated with HP 13C pyruvate preparation can be attributed to the conditions required for the dissolution-DNP method of high magnetic field (~3-7 T) and very low temperature (~1 K) during polarization, with pressurized and superheated water necessary for the rapid dissolution event. These extreme conditions are quite challenging for the design of the container-closure and fluid path system. In particular, the cryogenic temperature in the polarizer requires special attention to any moisture or ambient (moist) air introduced into that portion of the fluid path, which can form an ice block at ~1 K. This ice can lead to flow restriction during the dissolution event and reduce the strength of the laser welded bond between the cryovial and its cap. This can ultimately produce failures in the dissolution step, including variations in final pyruvate concentration and pH that may fail to meet QC release criteria as well as fluid path ruptures that provide no available dose and result in polarizer down-time.

The polarization of the HP 13C pyruvate sample decays quickly over the span of a few minutes after dissolution, and thus the process of dissolution, QC for release, and injection should be completed as fast as possible to preserve the high polarization level achieved. Any delays in the preparation process, such as transportation time or equipment malfunction, can significantly reduce the final polarization and result in lower quality imaging data.

Current Practices

A summary of data collected from all sites performing clinical trials with HP 13C-pyruvate is shown in Fig. 3 and Table 1, including the specification of the final dose and how the quality control and release of the final dose are performed. There is a split in the Production Style, described in the General Considerations section above, with 8/13 sites using Sterile Preparation versus 5/13 using Terminal Sterilization. While many of the dose specifications show notable differences in acceptable ranges, all of these variations listed in tables have been successfully and safely been used to perform HP 13C pyruvate studies in humans. Their differences depend on the institutions’ preferences, resources and their particular regulatory situation. There is high similarity in pyruvate ranges, temperature ranges, EPA limits, and volume limits. There is modest variability in pH ranges and large variability in the endotoxin test limit. There is a 3-fold difference in acceptable polarization levels, which are measured to ensure a futile dose is not injected since the polarization is directly proportional to SNR. This reflects the decision by several sites to believe that useful data can be still be obtained with suboptimal polarizations.

Figure 3: Hyperpolarized agent preparation methods reported by sites currently performing HP

In House

Table 1: HP 13C-pyruvate preparation parameters, methods, and dose specifications used for quality control testing and release as well as validation. These were obtained from a survey of all sites performing clinical trials with HP [1-13C]pyruvate. The parameters used for product release are noted in bold text, otherwise these parameters are measured for batch validation or other QC measurements. The endotoxin and sterility testing are performed during process validation of the batch and/or post-injection, and largely depends on the agent production approach.

Summary

The overall safety record of HP 13C-pyruvate has been very strong, and the SPINlab hyperpolarizer has proven to provide high polarizations at human sized doses while meeting numerous QC and release criteria. A weakness remains the failure modes of the SPINlab Phamacy Kits (e.g. ice blocks, path ruptures), which are placed under extreme requirements particularly during dissolution. The preparation process still requires a high degree of expertise.

Therefore, there is a significant need to improve the reliability, robustness, and ease of operation for generating HP 13C-pyruvate doses for human studies. Furthermore, there is a divide between manufacturing and sterile compounding style preparation as well as other site-specific practices, resulting in variations in SOPs and justification required to relevant regulatory bodies. There have also been no comparisons between these approaches. It is also unclear what release criteria and QC parameters are truly required to ensure patient safety.

However, all of the reported methods are acceptable and approved by the appropriate regulatory authorities, and have led to the rapid expansion of successful human studies in recent years.

Mri System Setup And Calibrations

This section covers the MRI system setup, including the imaging system, RF coils, phantoms, and prescan calibration methods.

Imaging System

The main prerequisite for a given MRI scanner to be capable of supporting studies with HP 13C is its “broadband” capability to transmit and receive radiofrequency (RF) signal at the frequency of 13C, which is around 4 times lower than 1H. This does not come as a default on clinical MR devices. The transmit power of the broadband amplifier should also be sufficient to support the intended flip angle and RF pulse shape with the employed transmission RF coil(s) for 13C. Most studies to date use relatively low flip angles (< 90 degrees) for HP 13C in order to preserve polarization for time-resolved imaging. The capability to receive 13C signal on multiple channels is also desirable to increase SNR, as discussed further in the “RF coils” section.

The choice of magnetic field strength is primarily dependent on the metabolites’ frequency separation due to chemical shift dispersion and 1H imaging. High field strengths do not enhance hyperpolarized 13C signal as they do for 1H because the signal strength in a HP experiment relies on manipulating the population of quantum energy states outside of the MRI scanner.

However, the injected HP 13C-pyruvate and its metabolic products have greater frequency separation at higher fields, and it may thus be easier to separate and quantify these resonances at higher fields. This comes at the cost of a reduction in the achievable T2* and often reduced T1. As the initial polarization is independent of the imaging field strength it has been proposed that the increased T2* at 1.5T can potentially be exploited to increase SNR by adapting the acquisition bandwidth or reduce off-resonance imaging effects in cases when the decay of the transverse magnetization is dominated by T2* (73). In practice, 3T has been used in all published human 13C-pyruvate studies surveyed (Supporting Table S1), and comprises the majority of scanners currently in use for human studies (Table 3). A field strength of 3T is well-suited for 1H MRI anatomical reference and correlative imaging.

Stronger and more rapidly slewing magnetic field gradients support more rapid spatial encoding, particularly for metabolite-specific single-shot imaging using echo-planar imaging (EPI) or spiral imaging (See “Acquisition and Reconstruction”). Although the spatial resolution acquired for HP 13C imaging is typically much coarser than for 1H MRI, the factor of ~4 in gyromagnetic ratio leads to the same reduction factor in performance of the gradient system, so 13C experiments are potentially more limited by gradient hardware performance. To date, all human studies have used the commercially-available integrated gradient systems provided in clinical MRI scanners.

Optimization of scanner design has understandably focused on minimization of artifacts in 1H MRI, where devices such as room lights, the gradient amplifiers, and the motors driving the patient bed are checked to ensure that they do not produce RF interference at the 1H frequency, but artifacts may arise at other frequencies. Eddy current compensation is also not always appropriately adjusted for nuclei at other frequencies (74). In order to optimize for 13C, many sites have performed checks on phantoms for RF interference, gradient artifacts, and eddy currents (74), including the use of post-hoc gradient impulse response function characterisation and correction, and some vendors have fixed these issues as well.

Rf Coils

For HP 13C imaging studies in humans, RF coils for both 1H and 13C nuclei are needed, with 1H MRI providing an anatomical reference for registration and optional additional multiparametric MRI readouts. At the Larmor frequency of 13C nuclei, the relative contributions from coil noise compared to sample noise increase compared to 1H (73,75), although sample noise still is likely the dominant contributor for human-sized coils at 32.1MHz - the resonance frequency of 13C nuclei at 3T.

The key requirement for human 13C-pyruvate RF coils are that the coil geometry and sensitive volume must cover the volume of interest in the subject. Table 2 and Figure 4 shows coil configurations that have been used and optimized for applications in different anatomic regions.

Volume resonators are most commonly used for transmit, as they surround the subject to

Provide B1 Transmit Across The Fov (B1

+). While 1H relies on a large birdcage (“body”) coil built into the scanner, 13C transmit coils must be placed inside the bore. This takes up valuable space within the magnet, and also has led to the use of designs with relatively inhomogeneous

B1

+. Many human studies have used Helmholz pair resonators for transmit, including the “clamshell coil”, which has a notably inhomogeneous B1

+ Profile But Has Been Used Because Of

relatively easy integration into the scanner bore. B1

+ Variation Results In Variations In The Flip

angles that control the use of the hyperpolarized magnetization and creates errors in common HP metrics (9,76). The exception are head coils, where birdcage designs with highly

Homogeneous B1

+ can be placed around the head while easily fitting inside the bore. As with 1H MRI, higher SNR can typically be achieved by smaller receive coil elements, such as surface coils or phased arrays, and the majority of 13C receive coils used have layouts similar to 1H phased arrays.

RF coil quality control is important to ensure proper functioning of the coils to provide consistent imaging quality, especially with limited natural abundance 13C signal in vivo. It typically involves 1) a physical integrity check of the coil cables and connectors and 2) phantom SNR tests to check the coil’s performance and to monitor it over time (see Phantoms below). An useful reference for RF coil quality control is outlined in the MRI accreditation program of the American College of Radiology (77) and can be adapted for 13C coils.

Notably, configurations for brain and prostate studies used dual-tuned 1H/13C coil designs, which greatly simplify workflow and registration of 1H and 13C images, as no switching of coils is needed.

(1)

Table 2: RF coil configurations reported for human HP [1-13C]pyruvate studies.

Tx = Transmit

coil, RX = receive coil. The commonly used “clamshell” TX coil is a Helmholz pair design. For 1H RF configurations, all used the Body coil for TX unless otherwise noted, and “repositioned” indicates the 13C coil was removed for 1H imaging. One representative reference is listed for each configuration. The RF coil configurations reported in the reviewed papers are shown in Supporting Table S1.

Figure 4: Examples of RF coil configurations used for human HP [1-13C]pyruvate brain studies. (A,B) 13C Clamshell TX (Helmholz pair) and 2× 4-channel paddle RX arrays. (C) 13C Birdcage volume TX and 32-channel RX array (RX array slides into TX coil). (D) 13C Birdcage volume TX and 24-channel RX array, combined with a 1H 8-channel RX array. Image reproduced with permission from Ref (16).

Phantoms

Since hyperpolarized magnetization is non-renewable, phantoms containing 13C nuclei are important to: 1) test the multi-nuclear capabilities of the imaging system, including all parts of the signal excitation and receive chain; 2) perform calibration measurements before a scan with hyperpolarized nuclei; and 3) perform necessary pre-scan adjustments (see “Prescan Calibration” section). The phantoms currently in use are listed in Table 3. Their composition must provide sufficient 13C signal, with additional considerations of conductivity, stability, chemical shift(s) present, potential for dynamic imaging, and cost. The phantom geometries are typically either compact, in order to be used alongside the subject during a HP scan, or large enough to mimic the inner volume of a RF coil for system testing.

One popular compact design contains enriched 13C-urea at high concentration, typically 8 M, which provides a single resonance, placed inside a small container ~1 mL. The most common recipe mixes 13C-urea in a 90% water/10% glycerol solution, with glycerol used to increase the urea solubility and doping with a Gd-based contrast agent to shorten T1 which increases the potential SNR per unit time. For example, when Dotarem is added at a 3:1000 volume ratio the 13C-urea T1 is around 500 ms and T2 is around 100 ms. However, when testing pulse sequences influenced by T1 and T2, doping should be used carefully. This phantom is suitable for frequency calibration, transmit gain calibration, sequence testing, and as a fiducial marker when placed next to a patient. However, enriched 13C-urea has a relatively high cost compared to natural abundance compounds.

For larger volumes (>100 ml), the phantoms most often used contain undiluted ethylene glycol, glycerol, or dimethyl silicone. These compounds have sufficiently high carbon concentrations to provide sufficient 13C signal even with the 1.1% natural abundance of 13C. These larger phantoms matching the inner volume of an RF coil are useful for coil testing, including transmit

+) And Receive (B1

-) coil profile mapping, as well as to mimic acquisitions using in vivo FOV requirements. In this case, size and conductivity should match the expected subject size in order to mimic coil loading and get a realistic estimation of B1+. Large-volume natural abundance urea phantoms have also been used by some sites, but suffer from higher conductivity compared to biological tissues. Typically, it is easier to increase the conductivity and hence coil loading of the non-conductive phantom by adding NaCl to match physiological loading (16,78).

Dynamic phantoms that aim to mimic metabolite kinetics have also been developed (79–81), and have the potential to more closely mimic the HP experiment, but so far these are not widely used.

Prescan Calibration

Prior to performing an MRI acquisition, the so-called prescan procedure is used to set the shim parameters to maximize B0 homogeneity over the field of view (FOV) or a specific region of interest (ROI), the scanner center frequency (CF), the RF transmit gain, and the receiver gain.

While this calibration procedure is usually automated for 1H, the lack of sufficient natural abundance 13C signal prevents use of automated methods. (Although natural abundance 13C lipid signal has been detected, there are so far no reports on using this signal for prescan.) Table 3 shows current practices across sites.

Maximizing B0 homogeneity is independent of the nucleus and is therefore performed prior to 13C imaging using the 1H water signal and existing shimming tools, such as by a standard automated process (“Auto Shimming”) or using high order shimming routines. Similarly, the 13C CF can be calculated from the 1H CF using a predetermined scaling factor that depends on the target chemical shift (82). Another common approach used is to have a small, high-concentration 13C phantom, e.g. 8M 13C-urea, integrated in the RF coil or placed next to the scan subject (1). The reference frequency can also be based on real-time measurements after the HP injection but prior to imaging (83). Both the CF and B0 shimming are critical when using spectrally-selective RF pulses, as inmetabolite-specific imaging methods, where the desired excitation bandwidths are typically very narrow and frequency offsets can lead to a failure mode that is only apparent after injection.

The calibration of the RF transmit power is typically performed on a small, high-concentration 13C phantom placed near the region of interest during the scan or on a large 13C phantom of similar size and coil loading as the subject, prior to the subject scan. Reference power is often done by sweeping the power in a pulse-acquire sequence (53,62), or the Bloch-Siegert method (52,84). When using a small phantom, the location of the phantom, B1

+ Inhomogeneity As Well

as any shielding effects, e.g., when the phantom is integrated into a coil (1), may degrade the accuracy. Other methods include real-time Bloch-Siegert method measurements after the HP injection (83), and using the stronger natural abundance 23Na signal that is close enough to the 13C resonance frequency to be detected by 13C coils (82).

The receiver gain is predetermined, either systematically based on independent phantom measurements and assuming the dose and polarization of the HP compound is known prior to injection, or based on past HP imaging studies.

Power [Kw]

Phantom(s) - during study Phantom(s) - before study 13C Frequency

8

13C-bicarbonate doped with dimethyl silicone, various

Maximum Values

Table 3: Summary of the imaging systems, phantoms, and prescan procedures used at sites currently performing HP 13C-pyruvate human studies. These were obtained from a survey of all sites performing clinical trials with HP [1-13C]pyruvate. *Previously performed studies with a Siemens 3T Tim Trio. The imaging systems, phantoms, and prescan procedures reported in the reviewed papers are shown in Supporting Table S1.

Summary

Commercially available 3T MRI systems are by far the most commonly used for human HP 13C-pyruvate studies, although a systematic investigation of the impact of B0 has only recently been investigated (73). The multi-nuclear RF transmit and receive chain has proven sufficient for current acquisition strategies, although many sites have observed artifacts due to RF interference, gradient interference, and residual eddy currents when operating at the 13C frequency. A variety of 13C RF coils, tailored for numerous anatomical targets, have been successfully demonstrated, with the main limitation that most transmit coils take up a lot of additional space inside the bore and provide relatively inhomogeneous B1

+ Profiles. The

phantoms used have converged into generally 2 categories - small phantoms containing 13C-enriched compounds that can be used during the study and human-sized phantoms containing compounds with high carbon concentrations but without 13C enrichment that are used to test and calibrate the coils. There are no standardized compositions or geometry, and dynamic phantoms that recapitulate in vivo kinetics would be desirable but are still an emerging area. Prescan calibration procedures were not well defined in most publications, so we surveyed individual sites to determine current practices. Calibration procedures for the B0 field (13C CF and shimming) for most sites take advantage of 1H signal and methods, while methods

For Calibration Of B1

+ is more variable across sites, likely a reflection of remaining challenges in how to perform this calibration. Standardization of both phantoms and calibration procedures would synergistically improve the robustness and reproducibility of HP 13C studies.

Acquisition And Reconstruction

Data acquisition strategies in human HP [1-13C]pyruvate MRI studies must account for multiple chemical shifts, efficiently utilize the non-renewable HP magnetization, and acquire data quickly relative to metabolism and relaxation decay processes. These studies require spectral encoding to separate metabolites, necessitating pulse sequences that efficiently encode up to 5D data (3 spatial + 1 spectral + 1 temporal dimension). RF pulses must efficiently sample without immediately saturating the non-renewable HP magnetization, and sequences must acquire data quickly and be robust to both experimental and physiologic variation (e.g. B1

+ Inhomogeneity,

variation in perfusion) to ensure reproducibility and minimize scan-to-scan variability. This section covers current successful practices for data acquisition in human [1-13C]pyruvate studies, and accompanying 1H imaging, from different anatomic regions, including scan parameters and image reconstruction.

Acquisition And Reconstruction Methods

The acquisition methods used in human [1-13C]pyruvate studies can be classified into 3 categories: 1) MR spectroscopy or MR spectroscopic imaging (“MRS/I”), 2) chemical shift encoding methods, and 3) metabolite-specific imaging (Fig. 5).

Mrs/I Methods Specifically

resolve a spectrum that can be analyzed to extract expected as well as unexpected resonances, making this approach very robust. It was used in many initial studies (1).

Chemical Shift

encoding methods, most commonly the Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) method, use imaging sequences acquired with multiple TEs and rely on a model-based separation of expected chemical shifts (85).

Metabolite-specific imaging methods use specialized RF pulses that are spatially and spectrally selective to excite individual metabolites which are then typically imaged with fast k-space trajectories such as echo planar imaging (EPI) or spirals (86).

Their Application To Different

organ systems is described below. The image reconstruction methods used in human [1-13C]pyruvate studies have typically been conventional methods (e.g. FFT, non-uniform FFT, or equivalent). The incorporation of accelerated imaging and advanced reconstruction methods including parallel imaging (4,57,87) and compressed sensing (7) has also been applied in human studies for improved spatial resolution, temporal resolution and coverage, but have the potential for additional artifacts as well as SNR losses due to ill-conditioning of the reconstruction (e.g. g-factor).

The Majority Of

published studies do not use accelerated imaging indicating the resolution and coverage achievable without acceleration is currently adequate for successful data collection. Performing coil combination, even with fully sampled data has also been shown to have specific challenges for HP human images: using naive sum-of-squares methods suffer from high noise amplification in the relatively low SNR regime of HP [1-13C]pyruvate (compared to 1H), motivating several HP 13C-specific methods that include data-driven coil sensitivity estimation which have shown obvious improvements over sum-of-squares (11).

More recently denoising techniques have been applied as post-processing of human HP data(41,42,44). The techniques applied are based on spatial-temporal singular value decomposition for unsupervised estimation of signal and noise components. They have shown improvements in apparent SNR in the brain and liver, while care must be taken to choose parameters such as the rank threshold to avoid oversmoothing and overfitting to the estimated signal components.

Prostate Studies

Prostate cancer was the first human application of HP [1-13C]pyruvate (1), and data was acquired with MRS/I methods: 1D dynamic MRS, single-slice 2D dynamic echo-planar spectroscopic imaging (EPSI), and single time point 3D EPSI. Advances in imaging strategies led to the development and application of new acquisition schemes, including undersampled 3D EPSI with compressed-sensing (7), model-based chemical shift encoding methods that use a priori information (47,59), and metabolite-specific EPI (10), all of which can provide volumetric whole-organ coverage and dynamic acquisitions.

The pyruvate bolus arrival in the prostate can vary by ± 10 s between patients, necessitating dynamic imaging to reliably and consistently capture the pyruvate bolus (18). For this reason, all currently ongoing studies acquire dynamic data. While MRS/I, chemical shift encoding, and metabolite-specific imaging can all achieve dynamic imaging, chemical shift encoding and metabolite-specific imaging provide greater dynamic and volumetric coverage (85). For scan prescriptions, the FOV is designed to provide full prostate coverage and typically to match the orientation of the anatomic imaging used for registration. Flip angles used in current studies are constant through time, as quantification with a variable-through-time flip scheme is highly sensitive to bolus timing (8) and errors in the RF transmit (B1 +) field (76).

Heart Studies

Data acquisition methods for 13C imaging in the heart must be designed to meet the demands of significant cardiac motion and blood flow. To cope with the periodic cardiac motion, most human heart studies to date used gating to the diastolic window, the longest cardiac cycle interval, which has reduced motion (2,22,28,30,35,36,38,45,52). The duration of the diastolic window limits the available data sampling time, making cardiac acquisitions the most time-constrained of the HP 13C MRI applications. The most common acquisition approach is metabolite-specific imaging with spiral k-space trajectories (2). Their single-shot imaging capability makes these methods particularly robust to motion effects. Furthermore, spiral k-space trajectories provide rapid k-space coverage and relatively benign flow and motion artifacts. The majority of studies have used 2D multi-slice acquisitions, but 3D encoding has also been used successfully (35).

Brain Studies

For HP 13C MRI of the human brain, the majority of studies have also used 2D (slice selective) acquisitions (10–12,14,16,28,33,40,41,44,51,53,60), with a trend toward volumetric coverage using 2D multi-slice metabolite-specific imaging. 3D metabolite-specific imaging of the whole brain, with phase encoding of the slice direction (34,57), has been shown to provide similar SNR efficiency (88) compared with multislice imaging. A number of studies have employed MRS/I (5,6,29,31–33,50,55) resulting in a spectrum from each voxel, which has the advantage of not requiring a priori information about which peaks to encode. This was important in early brain studies when it was not known which peaks would be detectable. Chemical shift encoding, using a set of images with different echo times and an iterative reconstruction of the individual resonances (i.e. the IDEAL approach (85)), has also been used (12,49,54), with the drawback that coverage in the slice direction was limited due to the time required to acquire multiple echo time images.

Abdomen And Breast Studies

The fundamental approaches to data acquisition and reconstruction in the abdomen and breast are largely similar to the aforementioned applications, but demand attention to particular challenges associated with these anatomic regions, especially relating to respiratory motion.

Although it has been shown that a basic 2D MRSI approach based on phase encoding and FID readout can be successfully applied for HP 13C imaging in breast (15) and kidney (13), major advantages in terms of spatiotemporal resolution and coverage have been realized using tailored approaches based on metabolite-specific imaging (43,62) and chemical shift encoding (43), which have facilitated multi-slice or 3D dynamic acquisitions over large FOVs in the abdomen (4,37,46).

The significant respiratory motion encountered in these regions can directly blur 13C images, and has further favored these rapid acquisition strategies. Motion also degrades B0 homogeneity, which can shift frequency-selective excitation profiles and introduce artifacts into rapid imaging readouts. This makes accurate determination of the acquisition center frequency and shimming essential in these regions which often cover large FOVs. (See “Prescan Calibration” section for more information). In some studies, breath-holding was used to minimize motion effects and enforce frame-to-frame data consistency (42). A pragmatic and reasonably effective approach for dealing with respiratory motion during 13C data acquisition is an initial breath-hold (as long as can be tolerated), followed by free-breathing (46,62).

1H Imaging

Collection of 1H imaging data is essential both for prescribing the 13C acquisition and for interpretation of the resulting 13C data. Multi-planar 1H scouts are acquired prior to 13C acquisition to enable graphical prescription of the 13C imaging region. All human HP 13C-pyruvate imaging studies acquire conventional MRI scans (e.g. T1- and T2-weighted volumes) for anatomic reference, aiming to cover at least the full 13C FOV. Acquiring these anatomic scans as close as possible to the time of 13C imaging (immediately before or after) minimizes potential misregistration between the data sets. Depending on the application, other advanced 1H sequences are also acquired (e.g. diffusion-weighted imaging for cancer imaging).

When contrast-enhanced data is acquired, it is done after 13C imaging, as paramagnetic contrast agents will accelerate 13C relaxation.

Reported Study Parameters

Figures 5 and 6, and Supporting Table S2 shows the reported acquisition study parameters for human HP [1-13C]pyruvate studies published as of September 2022. Figure 5 shows a mixture of MRS/I, metabolite-specific imaging, and chemical shift encoding methods have been successfully used, where spectroscopy-based methods have become less prevalent in recent studies. Figure 6 shows the acquisition timing, including the important start time and interval/temporal resolution, is quite variable across studies.

Figure 5: Acquisition methods used in published HP [1-13C]pyruvate human studies published up to September 2022, classified into: MR spectroscopy and spectroscopy imaging (MRS/I); chemical shift encoding methods, such as IDEAL, that use multiple TEs and model-based reconstructions; and metabolite-specific imaging methods that use spectrally-selective excitation to image a single resonance at a time.

Figure 6: Temporal acquisition characteristics reported in HP [1-13C]pyruvate human studies published up to September 2022. (a) Reported referencing of acquisition start times.

(B)

Acquisition start times reported when using dynamic imaging and when timing was reported relative to the end of the injection. (c) Temporal resolutions. “Not Applicable” indicates dynamic imaging was not used.

Summary

Three general categories of acquisition strategies have been used successfully for human HP 13C-pyruvate studies: MRS/I, model-based chemical shift encoding (e.g. IDEAL) methods, and metabolite-specific imaging methods. These have enabled successful studies in the prostate, heart, brain, abdomen, and breast. Recent studies increasingly have used the imaging-based strategies of metabolite-specific imaging and chemical shift encoding which are the fastest methods, although a heads-to–head comparison between techniques has not been performed.

Metabolite-specific imaging is quite popular because of its speed and compatibility with single-shot imaging, but is sensitive to B0 field variations and thus requires careful calibrations. Nearly all studies surveyed acquired data dynamically, allowing measurement of the bolus and metabolite kinetics. The exact timings and associated flip angles vary quite widely across reported studies, with no consensus yet as to how to choose these parameters. Image reconstruction is typically done directly using Fourier Transform methods, and accelerated imaging strategies are uncommon.

Data Analysis And Quantification

This section covers the analysis of data from human HP [1-13C]pyruvate studies, including modeling and metrics, visualization, as well as considerations for how to store data and metadata. Depending on study design, the analysis may need to give quantitative or semi-quantitative output reflecting a biological process or may just reflect a contrast between different regions of interest for quantitative evaluation.

Metrics

Figure 7: HP [1-13C]pyruvate raw data (A) have typically been quantified using four categories of metrics depending on the acquisition. Data acquired as a single time point are often quantified using normalized metabolite images or metabolite ratios (B). Dynamic data can be quantified using normalized metabolite images or metabolite ratios (B), or with metabolite timings such as time-to-peak (TTP) or pharmacokinetic (PK) models (C). The latter two require the data to be time-resolved. [1-13C]alanine and 13C-bicarbonate are analyzed similarly to [1-13C]lactate but omitted here for display.

Metabolite images are commonly used as summary metrics for HP MRI data, often including some form of normalization as well as summed over time as an area under the time curve (AUC) (17). These are analogous to the visual evaluation that is most used for routine clinical work (89,90). In these metabolite images, we expect that the [1-13C]pyruvate AUC signal is predominantly weighted towards perfusion and uptake, while [1-13C]lactate, [1-13C]alanine and 13C-bicarbonate AUCs represent metabolic conversion. The strength of this approach lies in its simplicity and relatively few underlying assumptions. Limitations to the use of single-metabolite images or AUCs include sensitivity to inhomogeneous coil profiles (57,87,91), the acquisition strategy and acquisition parameters, pyruvate polarization and concentration level, and signal relaxation rates (92). Further, the reader must be careful to interpret all the images in conjunction to better understand the underlying biology; for example, increased [1-13C]lactate in the presence of decreased [1-13C]pyruvate delivery can have a very different meaning compared to increased [1-13C]lactate with increased [1-13C]pyruvate delivery.

In an attempt to address variations in coil sensitivity, polarization level, and pyruvate delivery, AUC images are often computed by normalizing to a specified parameter, such as the maximum pyruvate or average lactate signals, or presented as a ratio such as lactate/pyruvate or divided by “total Carbon” - the sum total of HP 13C signal observed across all metabolites. The AUC ratios between metabolites and pyruvate are proportional to the corresponding forward kinetic rates (81,93), but are not directly comparable to rate constants when magnetization loss rates (e.g. relaxation and losses due to signal excitation) differ between studies. Similarly, the ratios between the produced metabolites (e.g. bicarbonate/lactate) can reflect the balance between downstream metabolic pathways (12,55). Care must be taken to consider how AUC images are calculated and normalized before comparing values between studies.

To further quantify the interpretation, pharmacokinetic (PK) modeling approaches were developed to compute the apparent kinetics of pyruvate-to-metabolite exchange (92,94–99). These yield semi-quantitative to quantitative apparent rate constants, given in s-1. Some models require a vascular input function, while others avoid this requirement (95). PK models can explicitly account for acquisition-specific details such as excitation angle and repetition time, and thus may reduce the effects of these details on quantification. An input-less model, provided in the Hyperpolarized-MRI-Toolbox (https://github.com/LarsonLab/hyperpolarized-mri-toolbox) (100) and thus frequently employed for human data, has been shown to fit well and robustly to prostate and brain data (8,20). PK models are quantitative in nature, arguably provide more relevant biological information (8,20), and appear to be reproducible across sites (51). However, rate constants derived from PK models are still apparent rates, and likely do not reflect a single biological characteristic.

Some additional considerations include whether complex or magnitude data is used, as the noise behaviors will impact the analysis differently. Additionally, cut-off thresholds or other criteria may be used to identify and avoid voxels with insufficient SNR before analysis to improve robustness (20,41).

Regardless of the analysis approach, the underlying biology is not always clearly represented by the data; instead, the metrics may be influenced by perfusion, barrier permeability, intercellular shuttles, enzyme activities, co-substrate concentrations, or combinations thereof, depending on the organ and disease of interest (19,43,94,101–103). This may be addressed by incorporating complementary information. As an example, HP 13C pyruvate data is influenced by perfusion, and thus addition of perfusion MRI could be important for interpretation (98,104,105).

All the methods outlined above have been explored in clinical studies, described in Supporting Table 3 and summarized in Figure 8. As of September 2022, approximately 52% of studies involving human subjects report rate constants derived from a PK model with a few different models reported. A nearly equal fraction (51%) of the studies report AUC ratio values.

Approximately 66% of these studies report metabolite-specific images or AUC values. About 40% report SNR values; this metric is particularly frequent in manuscripts that describe technical developments for clinical HP MRI. Approximately 16% of these studies summarize model-free metrics, and 10% report measurements from a single timepoint. Most studies report a combination of quantities.

Figure 8: Reported metrics used for analysis in HP [1-13C]pyruvate human studies published up to September 2022.

Visualization

A wide variety of approaches have been used for visualizing data from human HP 13C-MRI studies. The challenges and practical considerations are: 1) choosing the appropriate metrics to display, 2) how to encode the parameters (e.g. the colormap), and 3) choosing how to provide anatomical context and other multi-parametric data. The choice of visualization also depends on the goal which could be for diagnostic interpretation, but also quality control, reproducibility among readers and publication.

Metrics

The choice of HP 13C metrics is described in detail above. At this stage in HP 13C development where there is no standardized metric, often a combination of metabolite images and ratios or PK model parameters are shown.

Parameter Encoding

The mapping function chosen should provide an adequate, often quantitative, impression of the parameter mapped. There is a consensus in the visualization field that perceptually uniform maps are best suited to visualize continuous parameters, like the greyscale typically used by radiologists as well as other monochrome (black to blue) and color ranges (fire-type, rainbow-type) (106,107). Multi-color heatmaps have been the most frequently employed method for HP 13C data, while greyscale has infrequently been used but it ensures there is no coloring-based bias as well as facilitating later reuse (Fig. 9a). Among the color schemes employed in the clinical HP 13C literature, fire-type scheme seems to be the most common [similar to “Plasma” or “Inferno” in matplotlib.org]. Next most commonly employed is the rainbow-type scheme [similar to “Rainbow” in matplotlib.org].

Anatomical Context

HP MRI faces the challenge that it does not necessarily depict the anatomical features, similar to PET, and thus requires an anatomical reference. Most often, a grayscale anatomical image is overlaid with a HP colormap (Fig. 9c,d). This approach is very intuitive, but can skew perception as the grey-scale anatomical reference may affect the brightness of the HP data (e.g. signal in the skull). This bias does not occur when showing adjacent maps (Fig. 9a, b). Here, anatomical outlines may help to provide reference (Fig. 9b).

Related Journal Articles & DOI Links

Selected peer-reviewed publications relevant to 12 Lead ECG Acquisition. Click the DOI to access the full paper (may require institutional access).

Why Choose Us?

Bangalore guidance for robotics, Spectre and autonomous systems projects.