Aniform Process Optimization

Performance Computing (IHPC), A*STAR, 1 Fusionopolis Way, #16-16, Connexis North Tower, Singapore 138632, Singapore

Published January 25, 2023

This Book Chapter is a republication of an article published by Long Bin Tan and Nguyen Dang Phuc Nhat at Polymers in July 2022. (Tan, L.B.; Nhat, N.D.P. Prediction and Optimization of Process Parameters for Composite Thermoforming Using a Machine Learning Approach. Polymers 2022, 14, 2838.

How to cite this book chapter: Long Bin Tan, Nguyen Dang Phuc Nhat. Prediction and Optimization of Process Parameters for Composite Thermoforming Using a Machine Learning Approach. In: Alexandru Vasile Rusu, Monica Trif, editors.

Prime Archives in Polymer Technology. Hyderabad, India: Vide Leaf. 2023. © The Author(s) 2023. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

Permits

unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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2 www.videleaf.com Author Contributions: Conceptualization, Finite Element Simulation and Results Compilation, Manuscript review and modification, Figure artwork, Supervision, Project Management, Tan, L.B.; ANN code development, ANN model training, validation and results, Manuscript initial draft, Nhat, N.D.P.

Advanced

Manufacturing and Engineering Domain (AME)—Industry Alignment Fund, grant number A19C9a0044. Acknowledgments: We acknowledge the support given by the post-manufactured CFRTP parts for model calibration and validation.

Conflicts of Interest: The authors declare no conflict of interest.

Abstract

Thermoforming is a process where the laminated sheet is pre- heated to the desired forming temperature before being pressed and cooled between the molds to give the final formed part.

Defects such as wrinkles, matrix-smear or ply-splitting could occur if the process is not optimized. Traditionally, for thermoforming of fiber-reinforced composites, engineers would either have to perform numerous physical trial and error experiments or to run a large number of high-fidelity simulations in order to determine satisfactory combinations of process parameters that would yield a defect-free part. Such methods are expensive in terms of equipment and raw material usage, mold fabrication cost and man-hours. In the last decade, there has been an ongoing trend of applying machine learning methods to engineering problems, but none for woven composite thermoforming. In this paper, two applications of artificial neural networks (ANN) are presented. The first is the use of ANN to analyze full-field contour results from simulation so as to predict the process parameters resulting in the quality of the formed product. Results show that the developed ANN can

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3 www.videleaf.com predict some input parameters reasonably well from just inspecting the images of the thermoformed laminate. The second application is to optimize the process parameters that would result in a quality part through the objectives of minimizing the maximum slip-path length and maximizing the regions of the laminate with a predesignated shear angle range. Our results show that the ANN can provide reasonable optimization of the process parameters to yield improved product quality. Overall, the results from the ANNs are encouraging when compared against experimental data. The image analysis method proposed here for machine learning is novel for composite manufacturing as it can potentially be combined with machine vision in the actual manufacturing operation to provide active feedback to ensure quality products.

Carbon Fiber; Woven Composites; Thermoforming; Machine

Ntroduction

In the last decade, machine learning has been utilized across different industries. In engineering and manufacturing, a properly trained artificial neural network (ANN) can help to develop design guidelines or provide optimized solutions that would substantially reduce the design cycle time. In the first application, the slip-path length contour images from hundreds of finite element thermoforming analyses are used as inputs and the ANNs are trained to predict the original process parameters

Knowing

approximately the actual process parameters that formed the part can provide a causal link insight to help develop design rules and allow an experienced engineer to propose improvements on the manufacturing processes so as to mitigate part defects. In the second application, another ANN is used for process parameter optimization in order to minimize the maximum slip-path length (indicator for surface defect) and to maximize the shear angle coverage (indicator for formability) for ply angles between 40 to

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4 www.videleaf.com 50 degrees of the formed laminate part. The second criterion is in tandem with promoting the draping of the laminate by ply shear mechanism rather than out-of-plane bending which will cause wrinkling.

Background

In this work, the finite element analysis software AniFormTM is used to simulate thermoforming of woven carbon fiber thermoplastic laminate under various process conditions. Each simulation can take between 1 to 2 h which is a time-consuming process. The motivation is to apply artificial neural network (ANN) to expeditiously predict better process parameters so as to reduce design cycle time. This process can help engineers explore the design space much faster, help develop correlations and design guidelines for thermoforming and in deriving an optimal set of process parameters that would create a better- quality part.

Artificial Neural Network and Convolutional Neural

Network

Neural network in computer science is an attempt to approximate the biological neural network of human brains. Its application in the field of machine learning is vast and can include natural language processing, object recognition, data analytics and many others.

An ANN is a system of many computing devices called neurons. Usually, an ANN consists of multiple neuron layers: an input layer followed by hidden layers and a final output layer. In a feed-forward NN (FFNN), the neurons in a layer only receive information from neurons in previous layers and send information to neurons in subsequent layers. Typically, only the neurons in two consecutive layers are connected. This connection is weighed and comes with a weight value. Despite the great variety of layer types, most neurons would perform the multiply and accumulate (MAC) operation on the data received from other neurons and then apply a non-linear function on the result. In this paper, two types of FFNN are developed. The first is the fully connected neural network (FCNN), whereby each

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5 www.videleaf.com neuron in layer i is connected to all neurons in layer i + 1, and the defining parameters for each FC layer are the number of output neurons. The second is the convolutional neural network

(Cnn) That Is Commonly Used In The Field Of Image

classification, whereby ANNs with many convolutional blocks (Conv) extract features, followed by FC layers that would perform prediction using the extracted feature vector.

In a Conv layer, a sliding filter is applied to perform MAC operations on a region of the input and stores the result in the output tensor, called a feature map. The filter will take a stride, which can be multi-dimensional, to the next region after each MAC operation. Finally, an activation function is applied at each position of the feature map to compute the output of the Conv layer. As sliding filters can stride over multiple dimensions, they can be used to extract inter-dimensional and/or spatial information from the input. The defining parameters of a Conv layer include the filter size and the stride.

Figure 1 shows an example of how a sliding filter works. The input source is a 2D array while the filter is a 3 × 3 array. Each element in the filter represents a weight. When the filter is applied on a region of the input source (overlapping region), its product with the corresponding element in the filter is accumulated. Essentially, the filter is a weight matrix used to carry out MAC operations on a region of the input tensor.

Figure 1: Example of convolution operation.

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Ann Applications In Manufacturing Research

To the best of the authors’ knowledge, almost all applications of ML in manufacturing thus far belong to the category of supervised learning whereby the training dataset is fully labelled. How the data are generated is very specific to the problem investigated, and for each engineering field, there are different considerations for data. As early as 2003, applications of ML in manufacturing were present. In , an overview of basic ANN applications in polymer composites was conducted.

Methodologies reviewed in this paper were used to predict fatigue life, wear performance and dynamic mechanical properties for the material and its response under combined loading situations. Fully connected (FC) networks, using backpropagation training algorithms, were primarily used for For more recent and novel approaches, Chang et al.

developed an ANN with the desired product dimensions as modeling inputs to predict parameters for the thermoforming of polymeric foam sheets, which is an inverse problem. This work employed the use of a small FC network with layer sizes (6, 𝑥0, 𝑥1, 6) and 𝑡𝑎𝑛ℎ activation functions. The inputs to the ANN are the thickness measured at six locations while the outputs are the processing parameters: heater temperature, plug displacement, vacuum time, vacuum pressure, plug velocity and plug material type. Multiple experiments were conducted and the optimal network size was found to be (6, 5, 2, 6). Prediction for six parameters is a rare sight, especially when the reported dataset is small (just 40 cases). The data generation method is based on single-parameter variation, such that only one variable changes while others remain constant. This greatly simplifies the problem as the data are not coupled and therefore do not effectively allow the ANN to learn the relationships between parameters. As the root-mean-square errors (RMSE) or loss values for all processing parameters were under 0.01, an acceptable accuracy was reported and the method is deemed suitable for the specific problem.

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7 www.videleaf.com Simoncini et al. used ANN to predict the maximum stress of tensioned ABS coupons undergoing the IR-heating process similar in thermoforming. A FC network with three layer sizes (5, 11, 1) was used whereby the five inputs are: operating heat source, crosshead speed, thickness of sample, distance from heat source and elongation of sample. The sole output was the stress– strain response of the coupon. The dataset only comprises 24 cases so it is inconclusive as to how good the ANN was at generalization.

Leite et al. studied the application of ANN for the vacuum thermoforming process. Simulation data were generated and a FCNN was used. The five inputs comprise process parameters: heating time, electric heating power, mold actuator power, vacuum time and vacuum pressure. The outputs are dimensional deviation in height, deviation of the diagonal length, geometric deviation of flatness and of the side angles. First, the ANN was trained to predict the abovementioned deviation groups so as to obtain a set of values representative of the final product geometry. The authors then proceeded to optimize a target function that combines the four metric functions, with the ANN serving as a proxy that helped with mapping parameters to the target function, that can be understood as a criterion to meet for the part design.

Zobeiry et al. studied the application of theory-guided ML (TGML) on laminate damage characterization, which is a direct problem. In total, 10,000 simulations were conducted. The ANN took in five input parameters: Young’s modulus, initial damage slope angle and the strains at three different points of the stress– strain curve with strain-softening response based on MAT81 material model in LS-DYNA. The training process was carried out with a validation split of 70/30 from the dataset and the mean squared error (MSE) was the loss function used for the evaluation of training quality. Four fully connected NNs were trained to each consecutively to predict four parameters: overall fracture energy, peak stress, slope of the damage function and strain-softening parameter. Knowledge in mechanics was evoked to build the chain of NNs where the output of one NN is used to guide the prediction of the next parameter. An advantage

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8 www.videleaf.com of this method is that the predicted data are uncoupled. Therefore, each NN can learn more effectively. More specifically, each NN can have a dedicated input vector and their weights can be trained to model one theoretical function only.

However, the number of required simulations is huge, likely making the method infeasible to other engineering problems which may have limited data.

Nardi et al. developed an ANN to predict final part attributes using the input temperatures of the thermoforming process, comparing the results to those from analytical and finite-element modeling (FEM) methods. The FC network has the architecture (3, 5, 3) with three layers and three output neurons. The small architecture was sensible as having more neurons can lead to overfitting or long training time. The Bayesian-regularization method, different from many other ANN approaches that used some variants of backpropagation, was used. It was not explained why the Sigmoid activation function was chosen, instead of the more popular ReLU function that can help gain sparsity when computing and better prevent vanishing gradients.

More recently, Humfeld et al. studied the problem of optimizing air temperature cycle in the autoclave for composite processing. Due to uncertainties including tool placement, convective boundary conditions vary in each run. As a result, temperature histories in some of the parts may not conform to process specifications due to under-curing or over-heating.

Recurrent NNs, a class of NNs where data from past inputs are remembered, were used in a FC network. This memory of past data is taken into account when operating on the current input.

Compared to FFNN, there is a feedback link in the RNNs. Data do not simply flow from the first to last layers but can be sent backwards when computing the next input. It has broad application in natural language processing due to its ability to analyze sequential data. In total, 100,000 simulations with eight input parameters comprising heat transfer BCs, thickness of composite part, thickness of tool and air temperature profiles were conducted. The validation split was 70/30. The ANN is used for inverse modeling to predict the bottom tool temperature and the center part temperature. Multi-objective optimization,

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9 www.videleaf.com using the same ANN, that predicts the probabilities of the temperatures on the composite being acceptable or not, is then performed. For each ANN simulation case, if the maximum part temperature and part temperature rate fall into a desirable range, Tmax < 185 °C, 1 °C/min < Tpart < 3 °C/min, the case is classified as “pass”. The outputs to this FCNN would be the failure and pass probabilities whereby cases/conditions with high pass probability are chosen and a sorting step is used to find the most optimal one.

Wanigasekara et al. extended their previous work, which built a direct model to predict output characteristics of thermoplastic composite laminates used in automated fiber placement machines and resolving it using inverse modeling for the same manufacturing problem. In the direct method, an ANN was used to predict four characteristics of thermoplastic composite laminates, namely, elastic modulus, short-beam strength/inter-laminar shear strength, maximum flexural stress and maximum flexural strain, based on four inputs, i.e., the deposition rate, consolidation force, hot gas torch temperature and its corresponding nip-point temperature. The inverse problem studied here simply reverses the flow of prediction. The authors showed that from a small set of 28 experimental data, virtual data can be generated whereby the training set for the inverse model consisted of predictions made by the direct model, excluding outliers, in addition to the original data. This approach showed a relationship between the direct and inverse models where one can be used to train the other. It was inferred that as more data are available, both models can be improved with little modification to the training pipeline.

Melaibari et al. applied neural networks to predict the GO- CuO/water-EG hybrid nanofluid viscosity that is found from the process of loading graphene oxide and copper oxide nanoparticles into ethylene glycol-water. Two methods were studied and compared: neural networks and response surface methodology (RSM). The ANNs comprised three layers, with the architecture (3, 10, 1). The three inputs are temperature, mass fraction and shear rate and the output value is the viscosity.

Additionally, the Sigmoid activation function was used. However, the training setup such as the dataset size, optimizer,

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10 www.videleaf.com etc., was not reported. On the other hand, the RSM uses the same three inputs to form a polynomial predicting the viscosity.

The polynomial consists of 19 parameters that are learned. From the experiments, the ANN achieved a mean square error of 0.0125 while RSM reached 0.166. This is quite sensible, considering that the ANN had 3×10 + 10×1 = 40 parameters to fine-tune, almost double that of the RSM polynomial.

In many of these works, the neural networks used were small FC networks and the main interest came from the workflow which can be used as a guideline for other manufacturing ML In the domain of quality control, a few papers have introduced the use of CNN. Nuria et al. incorporated CNN into a computer vision quality control system for the sealing of thermoforming food packages. In particular, the system contains multiple components, from a vision software that processes images from the camera to a CNN that makes the prediction of accepting or rejecting the package. Multiple neural network architectures were used: five ResNet configurations, three VGG

Each

architecture would be trained on all five datasets separately. This is to find which dataset fits the real-word scenario the most. In total, there are 2978 training images and 628 validation images.

For each network, it can be trained from scratch or trained from a set of weights pre-trained on ImageNet—an extensive dataset for object classification, first described in . The input to the CNN is a mono-infrared (read black and white) image, while the output is a binary decision of accept/reject. For this type of prediction, the output could either be one or two neurons. It is unclear how pre-trained models are trained or used for prediction, since ImageNet contains RGB images that have different dimensions from the mono ones. The results shown were very promising. All models achieved an accuracy of at least 93%. Pre-trained DenseNet161 proved to be the best with 99%. This work proved the usefulness of CNNs in the real- world setting. It has also demonstrated that using pre-trained models can be helpful. This can serve as a guideline for other applications of CNN in manufacturing to create a system of both computer vision software and convolutional neural networks.

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Odel Overview And Material Properties

Figure 2 shows the model setup for the thermoforming process analysis. The molds are of a double-dome geometry. The support frame and mold tools are rigid and the underlying fabric- reinforced thermoplastic laminate comprises two layers of plain- woven carbon fiber prepregs that are each 0.3 mm thick. The laminate is gripped using spring tensioners and suspended within the supporting frame to transport it from the heating stage to the molding stage where the slightly tensioned laminate will be placed in between the upper and lower molds before being pressed to form the part. Tensioners are typically used to reduce the amount of laminate sagging and improve laminate alignment with the mold when the thermoplastic composite has been heated.

Figure 2: Thermoforming setup (left); Double-dome mold geometry—bottom view (right). The thermoforming behavior of the laminate is mathematically captured using various material models. Essentially, the mechanisms observed in composite laminate forming are intra- ply in-plane shear and bending and tool–ply and ply–ply interfacial slippage. Table 1 shows the ply properties used for the thermoforming analysis in AniFormTM.

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12 www.videleaf.com Table 1: Ply properties used for the plain-woven laminate thermoforming analysis.

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13 www.videleaf.com The isotropic elastic and the cross-viscosity models are jointly used to predict the in-plane and bending behaviors of the plies.

The cross model is a shear-rate-dependent viscosity model as found in the work of Macosko . It is used to model power law type of response with a viscosity plateau region at low and high shear rates, respectively. The mathematical equations

𝐃

where  is the Cauchy stress, D is the rate of deformation tensor and J is the Jacobian of the deformation gradient. The Mooney–Rivlin model is a hyperelastic model, suited to model the response of rubber-like materials. The general

𝐼2 −3)

with Cij material constants and I1 and I2 the strain invariants. AniFormTM uses the two parameter Mooney–Rivlin model, where C01 and C10 can be set for n = 1, C00 = C11 = 0. The

𝐽. (2𝐶10(𝑩−𝑰) −2𝐶01(𝑩−1 −𝑰))

where B is the Cauchy–Green deformation tensor and J is the Jacobian of the deformation gradient. A mixed model that combines the models in parallel is used to describe the overall deformation mechanism. The total stress

, Where Each Basic Model

stress tensor, i, can be scaled by a weight. In our modeling, we

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14 www.videleaf.com assume equal weightage between the elastic and viscous response, i.e., 1= 1=1.

Part Profile

A geometry for the part profile has to be selected for this machine learning work. The double-dome geometry, with doubly curved regions of steep walls and small radii, is chosen as it is widely used by research groups as a suitable benchmark for the investigation of forming behavior. The groups aim to support the development of reliable and robust simulations for forming processes. The benchmark metrics for comparison include shear angles, draw-in and possible presence of wrinkles. The chosen testing material comprised balanced plain weave (BPW), balanced twill weave (BTW) and UBTW Twintex comingled glass/PP fabric laminate.

For greater insights on the formability response of plain-woven fabric-reinforced thermoplastics, Rietman et al. compared the results of AniFormTM simulations of the double-dome (DD) geometry with published results. The authors pointed out that different laminate orientations alone would lead to completely different deformation results, which include the distribution of shear angle. It is with this consideration that our work also utilizes the double-dome geometry so as to provide insights on optimization and also gain wider readership and application.

Thermoforming Parameters Studied

Five process parameters were investigated. These include (1) laminate orientation, (2) spring stiffness of tensioners, (3) preload of grip tensioners, (4) forming/press Rate and (5) grip size. The effect of tool and laminate temperatures could not be effectively investigated as temperature-dependent ply properties were not available. The parameter values were varied to run a total of 200 AniformTM simulations. The values corresponded to practical thermoforming process parameter ranges and are shown in Table 2. It is noted that the run cases are not a full factorial space of the available parameters shown in Table 2.

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15 www.videleaf.com Table 2: Combinations of process parameters used.

, 4, 8

Metrics such as the slip-path length and intra-ply shear angle results were used to evaluate the quality of the thermoformed part. The slip-path length is defined as the total slip that was encountered by a certain point at the tool–ply interface as the laminate needs to slide along the tooling during press forming . The magnitude of this length could give an indication of the duration a particular region was exposed to a colder tooling surface, which will give a higher possibility of surface defect as shown by Figure 3, where in-house experiments and simulations, for the thermoforming of four ply 2 × 2 twill carbon fiber- reinforced thermoplastics, show positive correlation between the slip-path length and the location of matrix smearing or optical defect. This defect is caused by excessive traction and slippage of the solidifying laminate with the cooling surface. Figure 4 shows experimental verification that regions of the laminate with higher ply shear will less likely experience wrinkle formation. In the top pictures of Figure 4, the side regions of the thermoformed wall showed wrinkles upon forming, which correlates to lower ply shear angles at that region. Similarly, in the bottom pictures of Figure 4, the two sides of the triangular wall surface have higher ply shear resulting in a smoothly formed part while wrinkles were observed close to the centerline of the triangular surface which corresponds to a lower ply shear angle at that region.

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16 www.videleaf.com Figure 3: Good correlation of matrix smearing surface defect with slip-path length indicator.

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17 www.videleaf.com Figure 4: Good correlation of wrinkle formation with low ply shear angle. Figure 5 (left) shows an example of the slip-path length and ply shear angle distribution on the double-dome part after thermoforming simulation. Higher slip-path lengths (as shown by the red regions) are typically near the transition from the vertical wall of the part to the excess material, with values as high as 15 to 20 mm. The location of high shear angles typically depends on the ply orientation relative to the mold. Figure 5 (right) shows the shear angle resulting from a 0 degree laminate.

The maximum shear angles could reach +/−40 degrees. Figure 5: Example of slip-path length (left) and ply shear angle (right) distribution on the formed laminate.

Mage Data Preprocessing

Machine learning is the process of learning a predictor to correctly predict a data space with the objective being either a regression (real number) task or a classification (discrete sets) task. Many learning paradigms have been proposed over the years. Non-ANN methods are specifically designed with

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18 www.videleaf.com restrictive assumptions in mind. They can excel with data spaces that fit their assumptions, but are difficult, if not impossible, to predict data with complicated distributions. Some important reasons why ANNs are used so widely today is that virtually no assumptions about the data space are made and that they are capable of capturing nuances from the data and provide nonlinear predictions.

The first part of this project explores the possibility of predicting process parameters based on the product images from finite element (FE) simulation contours similar to those in Figure 5.

Although FE simulation contours were evaluated in our study, this does not exclude the use of images from actual formed physical products. In our study, the slip-path length (SPL) contour images are collected as input data for machine learning.

To maximize the visual information gained, multiple views of the same case run are collected as shown in Figure 6, with the bottom view giving an overview of the SPL distribution and approximate location with high values, while the front and right views give more details of the SPL distribution on the sides of the laminate.

Figure 6: Bottom (left), front (middle) and side (right) views of SPL distribution of the double-dome part from AniFormTM simulation. Before being processed by the ANNs, the images are normalized. The normalization step would convert pixel values into values between 0 and 1. As the original images have very high resolution, using these images would require extensive computational resources for ANN training. Therefore, they are rescaled to 336 × 336 pixels which allowed for good results while not requiring too much computational memory. In some cases, it is possible to rescale the images to 224 × 224 pixels and still achieve good results. Since the SPL distribution is represented by a color contour, it is appropriate to represent an

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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