Autonomous Drone Navigation

Autonomous Navigation System For A Delivery Drone

Victor R. F. Miranda1 · Adriano M. C. Rezende1 · Thiago L. Rocha1 · H´ector Azp´urua2,3

· Luciano C. A. Pimenta1 · Gustavo M. Freitas1

Abstract The use of delivery services is an increasing trend worldwide, further enhanced by the COVID pandemic. In this context, drone delivery systems are of great interest as they may allow for faster and cheaper deliveries. This paper presents a navigation system that makes feasible the delivery of parcels with autonomous drones. The system generates a path between a start and a final point and controls the drone to follow this path based on its localization obtained through GPS, 9DoF IMU, and barometer. In the landing phase, infor- mation of poses estimated by a marker (ArUco) detection technique using a camera, ultra-wideband (UWB) devices, and the drone’s software estimation are merged by utiliz- ing an Extended Kalman Filter algorithm to improve the landing precision. A vector field-based method controls the drone to follow the desired path smoothly, reducing vibra- tions or harsh movements that could harm the transported parcel. Real experiments validate the delivery strategy and allow to evaluate the performance of the adopted techniques.

Preliminary results state the viability of our proposal for au- tonomous drone delivery.

Keywords Autonomous Drones · Unmanned Aerial

Vehicle Delivery · Path Planning · Localization · Vector

Ntroduction

Autonomous robots have been studied for a long time, and the rising demand for automated solutions for real-world 1 Escola de Engenharia, Universidade Federal de Minas Gerais.

E-Mail:

problems has accelerated research in the area. Such prob- lems involve housekeeping tasks (vacuum and lawnmower robots), military missions (rescue, patrol, attacks), security applications (surveillance, exploration), industrial operations (production and logistics), among others.

In the past few years, the online shopping market has grown significantly, and ever since, retail and mail compa- nies seek to make autonomous drone delivery a reality. The global online food delivery services reached $136.4bn in 2020, a 27% increase from the same period in 2019 (AJOT, 2021). This increase should continue in the following years with an expectation of $182.3bn in 2024. As a result, deliv- ery services face a high demand for orders and sometimes cannot maintain a short delivery time. Thus, new and inno- vative means of transportation must be developed to increase efficiency and cope with the increasing demand.

Autonomous delivery presents itself as a very convenient alternative, particularly in social isolation periods, such as those experienced by many countries in 2020 and 2021 due to the COVID-19 pandemic. In situations like this, physical contact between people should be reduced as much as pos- sible, especially with people from risk groups. Therefore, the transportation of goods by autonomous quadrotors could prevent any physical contact with the customer, thus main- taining the World Health Organization (WHO) recommen- dations and preventing the proliferation of the virus.

Some of the world’s biggest companies, such as Ama- zon, UPS, and Alphabet, are making advances toward drone delivery services. According to Schneider (2020), FlightFor- ward, the UPS branch responsible for drone flights, has al- ready achieved air carrier certification, which allows it to deliver small packages with drones. Wing, a division of Al- phabet, launched the United States’ first small commercial delivery service in Christiansburg, Virginia. These achieve- ments happened in late 2019 and show that the market for drone delivery is undoubtedly gaining ground worldwide.

In late 2020, Brazil’s National Aviation Agency (ANAC)

Arxiv:2106.08878V1 [Cs.Ro] 16 Jun 2021

Victor R. F. Miranda1 et al. granted the first authorization to a private company, Speed- Bird, to perform drone cargo tests in Brazillian urban areas

(Anac, 2020).

In this context, this paper presents a methodology for en- abling autonomous drone deliveries. After generating a path between two points of interest, the drone’s location is ob- tained through its GPS (Global Positioning System), 9DoF IMU (Inertial Measurement Unit), and a barometer. A vec- tor field control algorithm then guides the quadrotor into the desired path. However, in drone delivery, reproducible safe landings in urban areas are a critical challenge. In that re- spect, we propose an Extended Kalman Filter (EKF) algo- rithm that fuses planar visual marker and Ultra-Wideband (UWB) localization strategies with the drone’s software pose estimation to improve landing accuracy. The visual localiza- tion uses the ArUco markers , and the UWB localization is estimated via multilateration with multiple UWB anchors over the landing area. The proposed method explores the techniques described in Rezende et al. (2019), initially de- veloped for high-performance autonomous drone racing, to create a practical and robust real-world system for drone de- livery services. Real experiments validate the feasibility of the proposed strategies.

The remainder of the paper is structured as follows. First, the related works are presented in Section 2. Section 3 de- scribes the problem of delivery with autonomous drones, and Section 4 presents the methodologies used to accom- plish this task. The results obtained are discussed in Sec- tion 5. Finally, conclusions are presented in Section 6, to- gether with future research perspectives.

Related Works

Given the increasing demand involving autonomous air trans- port of cargo over short distances, several studies address different strategies for load transportation and other com- mon problems in this type of task. Drone delivery is an emerg- ing field, gaining attention in the academy and industry given the numerous challenges to overcome to perform success- ful missions in urban environments, such as guidance, tra- jectory planning, control, localization, obstacle avoidance, and safe landing, especially when global localization is not available or is unreliable (Yoo et al., 2018). Parcel deliv- ery using drones is also gaining attention, given the envi- ronmental benefit of aerial platforms against standard truck delivery (Koiwanit, 2018).

Regarding the control methods for load transportation applications, Raffo and de Almeida (2016) propose a ro- bust nonlinear control technique for load transportation us- ing quadrotors. Despite proving asymptotic stability, they consider a cable-suspended transport system susceptible to external disturbances due to the wind and drone maneuvers.

Similarly, Z´u˜niga et al. (2018) present cooperative cable- suspended load transportation, using multiples drones with consensus strategies. This approach reduces in 60% the ca- ble oscillations. Unlike these approaches, we use a trans- portation system with the load attached to the drone’s body, reducing disturbances due to the cargo movements, and a control method that minimizes aggressive maneuvers when following the path.

Several other control strategies are present in the liter- ature for autonomous drone operations. However, most of them focus on ensuring the drone’s stability at the lowest level, acting on the motors to follow a trajectory (Almakhles, 2019). The present work considers that the drone already has the lowest level controller properly tuned to guarantee the desired angular velocities. Therefore, we adopt the ap- proaches presented in Rezende et al. (2020) and Gonc¸alves et al. (2010) to define our high-level control strategy, also commonly called guidance. Rezende et al. (2020) propose a nonlinear path control method based on artificial vector fields that consider the robot’s dynamics. Gonc¸alves et al.

(2010) present a theory to generate the vector field in Rn, which can be used considering the three-dimensional case. Recent works dealing with drone delivery have focused on route optimization (Chiang et al., 2019), optimal charg- ing station location (Hong et al., 2018), and the mixture of traditional aerial routes with drone-carrying truck routes (Chang and Lee, 2018; Boysen et al., 2018). However, a holistic analysis of the requirements of a suitable delivery platform is often overlooked. Motivated by this, we propose a navigation system for complete autonomous delivery tasks using drones, focusing on the practical aspects of safe drone landing.

Traditional protocols and drone landing methods rely on expensive equipment such as DGPS or RTK GPS or do not satisfy the precision and robustness needed for drone land- ings in urban areas. Visual localization methods could aid in locating the landing area accurately, even in partially clut- tered scenarios, using equipment already deployed in the platform, for instance, RGB cameras. Planar markers such as the ArUco (Garrido-Jurado et al., 2014), can generate ro- bust pose estimation from heights up to 30 m and are a fea- sible solution for drone landing (Wubben et al., 2019; Marut et al., 2019). Many landing solutions using planar markers change the flight behavior to use only ArUco localization when in range instead of performing fusion sensing. Despite the low-cost, low-power consumption of visual localization using planar markers, they are more prone to environmental interference such as low light conditions, snow, dust, rain, and fog. Therefore, visual planar markers require constant maintenance to keep them fully functional. A different vi- sual approach for robust landing in vision-compromised en- vironments uses active Infrared (IR) beacons located at the landing platform and a special IR camera for detecting them

Autonomous Navigation System For A Delivery Drone

Fig. 1 Drone used for the autonomous delivery and its components. from afar (Nowak et al., 2017). These methods can work without external illumination but could be imprecise at di- rect sunlight or other IR emission sources.

Other types of localization systems that are less prone to environmental interference are wireless-based localiza- with Ultra-Wideband (UWB) systems using the Time-of- Flight (ToF) principle to estimate distances with centimeter precision. Drones could exploit these localization systems for indoor localization in cluttered environments such as in Tiemann and Wietfeld (2017) and Tiemann et al. (2018).

Key benefits of these types of wireless localization technolo- gies are that they could work even in visually degraded sit- uations, are easily scalable to multiple platforms (Nguyen et al., 2016), and are particularly robust to walls and reflec- tions, increasing the possible range of real-world situations where they can be applied. Recent works have fused UWB and vision localization for drone landing based on recursive least square optimization (Nguyen et al., 2019).

Our work differs from the previously mentioned ones since we propose a complete platform for drone delivery and compare popular localization methods for UAV platforms such as GPS, visual, and UWB localization in the landing phase. We also propose a sensory fusion of multiple exter- nal localization techniques given the sensing capabilities al- ready available on the UAV, including GPS, 9DoF IMU, and barometer. An Extended Kalman Filter improves landing ac- curacy considering fixed location platforms in urban areas.

Autonomous Drone Delivery

This section describes the autonomous delivery problem with drones and specifies the hardware and software used for de- velopment.

Problem Description

The problem addressed involves transporting small parcels between two points of interest using a drone in autonomous mode without receiving commands from a human pilot. We have considered obstacle-free environments and assumed dis- tances compatible with the drone endurance (maximum time of flight). Besides, since it is essential for delivery drones to carry fragile objects without abrupt movements, we have adopted a grasped transportation mechanism that reduces the risks of vibrations or unexpected package drops.

At first, residential regions will be the primary landing locality for delivery tasks; therefore, the drone must have the ability to land with accuracy in restricted and narrow areas to prevent unexpected accidents or injuries. The tests were performed considering a 1x1m landing platform.

Hardware

The DJI Matrice 1001, illustrated in Figure 1, allows devel- opers to implement their codes to control the drone. All sen- sors that come with the drone by default are used, includ- ing GPS, 9DoF IMU, and a barometer. These are respon- sible for helping to estimate the drone’s position in global coordinates (latitude and longitude), orientation, and height.

The drone’s flight board is responsible for transmitting in- formation from the sensors to another device, in addition to receiving control commands and sending them to the brush- less motors. In addition to the flight controller board already available on the drone, a Jetson Nano2 is responsible for data processing, path planning, and the quadrotor high-level con- trol.

We also developed a 3D model of the box used for de- livery, as shown in Figure 2a. Its coupling mechanism uses a servo motor, as illustrated in Figure 2b. An Arduino Nano is connected to the Jetson board to control this servo mo- tor, placing it in the position of coupling or decoupling the box on the drone. The schematic of Figure 3 illustrates the connections between the equipment and drone used during experiments.

Considering that the landing site used in the experiments has 1x1 m and that the horizontal accuracy in the drone’s lo- cation using GPS is approximately 2m, it is necessary to use additional sensors to assist the drone in making a safer and more accurate landing. For that, we verified the appli- cation of the following sensors in experiments using the real drone: (i) a RaspberryPi Camera v2.0 pointing downwards, together with ArUco markers combination on the landing platform, and (ii) Ultra-wideband (UWB) devices anchored 1DJI Matrice 100 - https://www.dji.com/br/matrice100

Machines/Embedded-Systems/Jetson-Nano/

Victor R. F. Miranda1 et al. Fig. 2 Coupling mechanism using a servo motor with a package used for cargo delivery: (a) exploded delivery box, and (b) the servo cou- pling mechanism.

Fig. 3 Connection between the drone embedded equipment. at the landing site, and a device of the same type attached to the drone. In both cases, it is possible to obtain additional information on the drone’s position with respect to the plat- form during landing and improve the localization by fusing all these data.

Software

The Operating System used is Ubuntu 18.04 together with ROS 1 (Robot Operating System). The ROS package pro- vided by DJI called Onboard-SDK-ROS3 establishes com- package allows sending commands to the embedded con- trol system and provides data from the sensors present on the drone, such as GPS, 9DoF IMU, and barometer, in addi- tion to estimating the drone’s position and orientation in an online fashion. For the identification and estimation of the 3Onboard-SDK-ROS - https://github.com/dji-sdk/Onboard-SDK-

Ros

ArUco’s pose, specific algorithms from the OpenCV4 are used. In the case of UWB devices, an algorithm uses the dif- ference between the time of arrival (TDoA) of the signal for each device to compute the drone’s position with respect to the landing site.

Ethodology

In order to satisfy the problem requirements, such as reduc- ing the delivery time while avoiding abrupt movements and with a safe landing, we propose a solution divided into three distinct tasks: (i) path planning, (ii) localization, and (iii) control.

Path Planning

The proposed path planning strategy simplifies autono-mous drone delivery. First, the method considers the altitude, lat- itude, and longitude data to define the drone’s geographical location. For test purposes, the covered distances are short, such that a flat Earth model can be considered. Thus, we transform the angles of latitude and longitude into measure- ments of distance. The drone’s position is then initially rep- resented with respect to the Earth’s reference frame FE.

Consider that the drone’s starting point is ps ∈R3 and that the load delivery point is pf ∈R3. Without loss of gen- erality, it is possible to assume an inertial coordinate system

Fi That Respects Two Conditions:

1. The path’s end point is the origin, i.e. pf = 0; 2. The x axis of the coordinate system FI is in the the hor- izontal plane, pointing in the direction of the final point,

I.E. ˆX ∥Πxy(Pf −Ps), Where Πxy(·) Represents The

projection in the xy plane. The inertial reference frame FI is easily obtained through two operations: a translation with respect to FE, in order to satisfy condition 1; and a simple rotation in the z axis to satisfy condition 2.

The proposed path planning method computes a smooth reference path connecting the two points ps and pf. The strategy consists of creating 5 path sections: (i) vertical as- cending line; (ii) arc of a circle; (iii) horizontal line towards the platform; (iv) arc of a circle; (v) vertical descending line.

In order to allow a smooth transition between sections, space is divided into 5 sectors Si, i = 1, 2, 3, 4, 5. Each path sec- tion is associated with one sector. The definition of the sec-

D

Fig. 4 Sectors S1, S2, S3, S4 e S5, defined in equation (1).

(1)

where h is the drone fly height (with respect to FI), r is the radius of the transition arcs, and d is the horizontal separa- tion between ps and pf. Starting and final points, sectors, and variables defined here are illustrated in Figure 4.

Ocalization

The Onboard-SDK-ROS package provides a georeferenced estimate of the drone’s global orientation and position (DJI- SDK Pose). This information comes from the sensory fusion of the available GPS, 9DoF IMU, and barometer, which re- sults in an accuracy of approximately 2m. Such an estimate is good enough when on a cruise flight and was therefore used throughout the flight. However, this may be insuffi- cient when landing on a 1x1 m platform like the one pro- posed in this work, whose dimensions are less than the posi- tion estimate’s accuracy. Besides, if the landing pad position changes or the georeference is not precise enough, the drone might not be able to land at the right location using GPS localization alone.

For these reasons, it is necessary to obtain additional in- formation that allows the improvement of the drone’s es- timated position with respect to the landing site. This pa- per presents a sensor fusion strategy to improve localiza- tion by merging the DJI-SDK pose estimation with infor- mation from: (i) an ArUco marker detection technique; and (ii) multilateration using Ultra-wideband (UWB) communi- cation devices.

Fig. 5 Modified ArUco marker. An smaller marker is placed in a big- ger one.

Aruco

For the use of the marker detection technique for localiza- tion, ArUco markers were printed and placed on top of the landing platform. A camera attached to the drone, pointing downwards, provides images of the marker during landing.

ArUco markers have features that facilitate their identifica- tion in the image, such as well-defined borders and high color contrast. In addition, the markers do not present ambi- guities in their orientation.

Thus, specific OpenCV algorithms identify the ArUco, as well as estimate the relative pose of the marker with re- spect to the camera. This last step is done by solving the problem of PnP (Perspective-n-Point), which proposes to estimate the three-dimensional pose of a calibrated camera given a set of 3D points and their corresponding 2D projec- tions on the camera plane.

It is possible to find the pose that minimizes the projec- tion errors of the points on the camera plane by knowing the actual size of the marker and the intrinsic calibration param- eters of the camera. These points must be distinguishable from each other, and in this case, the corners of the ArUco and its orientation allow for differentiating each of its four corners before sending to a PnP solver algorithm (Lepetit et al., 2009; Hesch and Roumeliotis, 2011).

There are works in the literature that address strategies to improve the detection of the markers. A common method is to merge different AR markers to create a group that pro- vides a better pose estimation, minimizing noise and occlu- sion, as presented in (de Santana et al., 2019). Large ArUco markers can be detected from high altitudes. However, when the drone is approaching the platform, this ArUco is quickly lost by the camera. On the other hand, a smaller ArUco has the advantage of being detectable when the drone is close to the platform (if there is no high horizontal error), even though it is difficult to be detected at high altitudes.

To improve the marker detection range at high and low al- titudes, we considered a modified ArUco marker that has a smaller marker (0.09x0.09m) inside a larger one (0.8x0.8m).

Figure 5 shows this modified ArUco. The inclusion of the smaller marker may harm the detection of the larger one. Nonetheless, in our tests, this problem did not occur.

Victor R. F. Miranda1 et al.

Ultra-Wideband (Uwb) Devices

Although the ArUco marker detection provides a good pose estimation, the method is not robust for detection in low- light environments or under visual occlusion situations. For this reason, we consider using another localization based on ultra-wideband devices, which works on these conditions and increases the landing strategy robustness.

Devices based on ultra-wideband wireless technology are commonly used for low-energy IoT communication or localization. This technology uses radio waves with a band- width greater than 500MHz, which reduces the loss due to obstructions and reflections of the environment, consequently increasing the security of transmissions (Sahinoglu, 2008).

UWB-based localization systems can be used indoors and outdoors, with an accuracy of up to 20cm, according to some manufacturers. In this method, multilateration al- gorithms estimate the position xT , yT and zT of a mobile device (called tag) with respect to a fixed reference, where other devices (called anchors) are located.

N This Paper, We Use Decawave Dwm10015 Uwb De-

vices to estimate the position of the drone with greater preci- sion when approaching the landing platform. A minimum of five devices are required for the algorithm to work, one tag embedded in the drone and four anchors in known positions; one of these devices is set as the base anchor.

The position is calculated based on the distance of the tag with respect to the anchors, which comes from the Time Difference of Arrival (TDoA) of the transmitted signal, mul- tiplied by the speed of signal propagation (speed of light), as presented in (Sayed et al., 2005).

Consider a set of enumerated UWB devices, where index 0 represents the tag placed on the robot, index 1 the base anchor (or main anchor), and the higher indexes the other anchors used on the system. The distance di1 from the i-th

(2)

where ti is the instant of time the signal sent by the tag reaches anchor i, and t1 is the instant of time this signal reaches the base anchor. Light speed is c and the number of anchors is N, such that N ≥4. Distances in equation (2) result on an intersection region that represents the Tag’s po- sition, obtained as the solution of the following set of equa-

Development-Board/

where Ji = (xixT + yiyT + zizT ) and d1 is the distance from the tag to the base anchor. Considering t0 the instant of time the signal is sent by the tag, d1 can be computed as

(4)

To use Decawave devices, a maximum distance of 10m must be kept between the anchors and the tag. This method- ology does not estimate orientation, whereas the ArUco es- timation does.

Sensory Fusion

One way to improve the localization and more accurately estimate the drone’s position, orientation, and speed states, is to use information from several sensors. Sensory fusion methods use data from different devices to obtain more ac- curate information on the states of interest. For instance, the software on the DJI Matrice 100 uses data from the GPS, 9DoF IMU, and barometer to provide the drone’s pose and speeds (DJI-SDK pose).

One of the most common fusion methods is the Extended Kalman filter (EKF) (Thrun et al., 2000). In this sense, the PnP estimation of the ArUco’s marker location and the UWB localization are merged with the DJI-SDK pose data to allow a more accurate landing. As the sensors provide data refer- ring to coincident states, a bias is considered in the position estimated by GPS.

It is possible to divide Kalman’s Extended fusion and filtering method into two stages: Prediction and Correction. For simplicity, the equations are presented with the notation b ←a indicating that b is updated with the value of a.

The prediction step in the discrete EKF involves the state

(6)

where f represents the state propagation model, which in- volves current estimate ¯x, the input vector u and the timestep ∆t. Matrix F ≡F(¯x, u, ∆t) is the partial derivative of f with respect to ¯x, and matrix G is the partial derivative of f with respect to u. Matrix Qu is the covariance matrix as- sociated with input vector u, and Qf is a covariance matrix associated with the model.

(8)

where w is the measurement vector and h(¯x) is the mea- surement model, which represents the expected value of w given the current estimated state ¯x. Matrix H is the Jacobian of h(¯x), and I is an identity matrix. Finally K represents the

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

Power [Kw]

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

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).

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