Maze Solving Robot

Abstract

Intelligent agents need to remember salient information to reason in partially- observed environments. For example, agents with a first-person view should remember the positions of relevant objects even if they go out of view. Similarly, to effectively navigate through rooms agents need to remember the floor plan of how rooms are connected. However, most benchmark tasks in reinforcement learning do not test long-term memory in agents, slowing down progress in this important research direction. In this paper, we introduce the Memory Maze, a 3D domain of randomized mazes specifically designed for evaluating long-term memory in agents. Unlike existing benchmarks, Memory Maze measures long-term memory separate from confounding agent abilities and requires the agent to localize itself by integrating information over time. With Memory Maze, we propose an online reinforcement learning benchmark, a diverse offline dataset, and an offline probing evaluation. Recording a human player establishes a strong baseline and verifies the need to build up and retain memories, which is reflected in their gradually increasing rewards within each episode. We find that current algorithms benefit from training with truncated backpropagation through time and succeed on small mazes, but fall short of human performance on the large mazes, leaving room for future algorithmic designs to be evaluated on the Memory Maze. Videos are available on the website: https://github.com/jurgisp/memory-maze

Ntroduction

Deep reinforcement learning (RL) has made tremendous progress in recent years, outperforming humans on Atari games (Mnih et al., 2015; Badia et al., 2020), board games (Silver et al., 2016; Schrittwieser et al., 2019), and advances in robot learning (Akkaya et al., 2019; Wu et al., 2022).

Much of this progress has been driven by the availability of challenging benchmarks that are easy to use and allow for standardized comparison (Bellemare et al., 2013; Tassa et al., 2018; Cobbe et al., 2020). What is more, the RL algorithms developed on these benchmarks are often general enough to solve later solve completely unrelated challenges, such as finetuning large language models from human preferences (Ziegler et al., 2019), optimizing video compression parameters (Mandhane et al., 2022), or promising results in controlling the plasma of nuclear fusion reactors (Degrave et al., 2022).

T = 0

Figure 1: The first 150 time steps of an episode in the Memory Maze 9x9 environment. The bottom row shows the top-down view of a randomly generated maze with 3 colored objects. The agent only observes the first-person view (top row) which includes a prompt for the next object to find as a border of the corresponding color. The agent receives +1 reward when it reaches the object of the prompted color. During the episode, the agent has to visit the same objects multiple times, testing its ability to memorize their positions, the way the rooms are connected, and its own location.

Arxiv:2210.13383V1 [Cs.Ai] 24 Oct 2022

Despite the progress in RL, many current algorithms are still limited to environments that are mostly fully observed and struggle in partially-observed scenarios where the agent needs to integrate and retain information over many time steps. Despite this, the ability to remember over long time horizons is a central aspect of human intelligence and a major limitation on the applicability of is rarely the limiting factor of agent performance (Oh et al., 2015; Cobbe et al., 2020; Beattie et al., 2016; Hafner, 2021). Instead, these benchmarks evaluate a wide range of skills at once, making it challenging to measure improvements in an agent’s ability to remember.

Ideally, we would like a memory benchmark to fulfill the following requirements: (1) isolate the challenge of long-term memory from confounding challenges such as exploration and credit assignment, so that performance improvements can be attributed to better memory. (2) The tasks should challenge an average human player but be solvable for them, giving an estimate of how far current algorithms are away from human memory abilities. (3) The task requires remembering multiple pieces of information rather than a single bit or position, e.g. whether to go left or right at the end of a long corridor. (4) The benchmark should be open source and easy to use.

We introduce the Memory Maze, a benchmark platform for evaluating long-term memory in RL agents and sequence models. The Memory Maze features randomized 3D mazes in which the agent is tasked with repeatedly navigating to one of the multiple objects. To find the objects quickly, the agent has to remember their locations, the wall layout of the maze, as well as its own location. The contributions of this paper are summarized as follows:

• Environment

We introduce the Memory Maze environment, which is specifically designed to measure memory isolated from other challenges and overcomes the limitations of existing benchmarks. We open source the environment and make it easy to install and use.

• Human Performance

We record the performance of a human player and find that the benchmark is challenging but solvable for them. This offers and estimate of how far current algorithms are from the memory ability of a human.

• Memory Challenge

We confirm that memory is indeed the leading challenge in this benchmark, by observing that the rewards of the human player increases within each episode, as well as by finding strong improvements of training agents with truncated backpropagation through time.

• Offline Dataset

We collect a diverse offline dataset that includes semantic information, such as the top-down view, object positions, and the wall layout. This enables offline RL as well as evaluating representations through probing of both task-specific and task-agnostic information.

• Baseline Scores

We benchmark a strong model-free and model-based agent on the four sizes of the Memory Maze and find that they make progress on the smaller mazes but lag far behind human performance on the larger mazes, showing that the benchmark is of appropriate difficulty.

Related Work

Several benchmarks for measuring memory abilities have been proposed. This section summarizes important examples and discusses the limitations that motivated the design of the Memory Maze.

Ab

(Beattie et al., 2016) features various tasks, some of which require memory among other challenges. Parisotto et al. (2020) identified a subset of 8 DMLab tasks relating to memory but these tasks have largely been solved by R2D2 and IMPALA (see Figure 11 in Kapturowski et al. (2018)).

Moreover, DMLab features a skyline in the background that makes it trivial for the agent to localize itself, so the agent does not need to remember its location in the maze.

Simcore

(Gregor et al., 2019) studied the memory abilities of agents by probing representations and compared a range of agent objectives and memory mechanisms, an approach that we build upon in this paper. However, their datasets and implementations were not released, making it difficult for the research community to build upon the work. A standardized probe benchmark is available for Atari (Anand et al., 2019), but those tasks require almost no memory.

Emory Suite

(Fortunato et al., 2019) consists of 5 existing DMLab tasks and 7 variations of T-Maze and Watermaze tasks implemented in the Unity game engine, which neccessitates interfacing with a provided Docker container via networking. These tasks pose an exploration challenge due to the initialization far away from the goal, creating a confounding factor in agent performance.

Moreover, the tasks tend to require only small memory capacity, namely 1 bit for T-Mazes and 1 coordinate for Watermazes.

Emory 15X15

Figure 2: Examples of randomly generated Memory Maze layouts of the four sizes.

The Memory Maze

Memory Maze is a 3D domain of randomized mazes specifically designed for evaluating the long-term memory abilities of RL agents. Memory Maze isolates long-term memory from confounding agent abilities, such as exploration, and requires remembering several pieces of information: the positions of objects, the wall layout, and the agent’s own position. This section introduces three aspects of the benchmark: (1) an online reinforcement learning environment with four tasks, (2) an offline dataset, and (3) a protocol for evaluating representations on this dataset by probing.

Environment

The Memory Maze environment is implemented using MuJoCo (Todorov et al., 2012) as the physics and graphics engine and the dm_control (Tunyasuvunakool et al., 2020) library for building RL environments. The environment can be installed as a pip package memory-maze or from the source code, available on the project website . There are four Memory Maze tasks with varying sizes and difficulty: Memory 9x9, Memory 11x11, Memory 13x13, and Memory 15x15.

The task is inspired by a game known as scavenger hunt or treasure hunt. The agent starts in a randomly generated maze containing several objects of different colors. The agent is prompted to find the target object of a specific color, indicated by the border color in the observation image. Once the agent finds and touches the correct object, it gets a +1 reward, and the next random object is chosen as a target. If the agent touches the object of the wrong color, there is no effect. Throughout the episode, the maze layout and the locations of the objects do not change. The episode continues for a fixed amount of time, so the total episode return is equal to the number of targets the agent can find in the given time. See Figure 1 for an illustration.

The episode return is inversely proportional to the average time it takes for the agent to locate the target objects. If the agent remembers the location of the prompted object and how the rooms are connected, the agent can take the shortest path to the object and thus reach it quickly. On the other hand, an agent without memory cannot remember the object position and wall layout and thus has to randomly explore the maze until it sees the requested object, resulting in several times longer duration. Thus, the score on the Memory Maze tasks correlates with the ability to remember the maze layout, particularly object locations and paths to them.

Memory Maze sidesteps the hard exploration problem present in many T-Maze and Watermaze tasks. Due to the random maze layout in each episode, the agent will sometimes spawn close to the object of the prompted color and easily collect the reward. This allows the agent to quickly bootstrap to a policy that navigates to the target object once it is visible, and from that point, it can improve by developing memory. This makes training much faster compared to, for example, DM Memory Suite (Fortunato et al., 2019).

The sizes are designed such that the Memory 15x15 environment is challenging for a human player and out of reach for state-of-the-art RL algorithms, whereas Memory 9x9 is easy for a human player and solvable with RL, with 11x11 and 13x13 as intermediate stepping stones. See Table 1 for details and Figure 2 for an illustration.

Offline Dataset

We collect a diverse offline dataset of recorded experience from the Memory Maze environments. This dataset is used in the present work for the offline probing benchmark and also enables other applications, such as offline RL.

Ean Maximum Score (Oracle)

Table 1: Memory Maze environment details. We release two datasets: Memory Maze 9x9 (30M) and Memory Maze 15x15 (30M). Each dataset contains 30 thousand trajectories from Memory Maze 9x9 and 15x15 environments respectively.

A single trajectory is 1000 steps long, even for the larger maze to increase the diversity of mazes included while keeping the download size small. The datasets are split into 29k trajectories for training and 1k for evaluation.

The data is generated by running a scripted policy on the corresponding environment. The policy uses an MPC planner (Richards, 2005) that performs breadth-first-search to navigate to randomly chosen points in the maze under action noise. This choice of policy was made to generate diverse trajectories that explore the maze effectively and that form loops in space, which can be important for learning long-term memory. We intentionally avoid recording data with a trained agent to ensure a diverse data distribution (Yarats et al., 2022) and to avoid dataset bias that could favor some methods over others.

The trajectories include not only the information visible to the agent – first-person image observations, actions, rewards – but also additional semantic information about the environment, including the maze layout, agent position, and the object locations. The details of the data keys are in Table 2.

Offline Probing

Unsupervised representation learning aims to learn representations that can later be used for down- stream tasks of interest. In the context of partially observable environments, we would like unsuper- vised representations to summarize the history of observations into a representation that contains information about the state of the environment beyond what is visible in the current observation by remembering salient information about the environment. Unsupervised representations are commonly evaluated by probing (Oord et al., 2018; Chen et al., 2020; Gregor et al., 2019; Anand et al., 2019), where a separate network is trained to predict relevant properties from the frozen representations.

We introduce the following four Memory Maze offline probing benchmarks: Memory 9x9 Walls, Memory 15x15 Walls, Memory 9x9 Objects, and Memory 15x15 Objects. These are based on either using the maze wall layout (maze_layout) or agent-centric object locations (targets_vec) as the probe prediction target, trained and evaluated on either Memory Maze 9x9 (30M) or Memory Maze 15x15 (30M) offline datasets.

The evaluation procedure is as follows. First, a sequence representation model (which may be a component of a model-based RL agent) is trained on the offline dataset with a semi-supervised loss based on the first-person image observations conditioned by actions. Then a separate probe network is trained to predict the probe observation (either maze wall layout or agent-centric object locations) from the internal state of the model. Crucially, the gradients from the probe network are not propagated into the model, so it only learns to decode the information already present in the internal state, but it does not drive the representation. Finally, the predictions of the probe network are evaluated on the hold-out dataset. When predicting the wall layout, the evaluation metric is prediction accuracy, averaged across all tiles of the maze layout. When predicting the object locations, the evaluation metric is the mean-squared error (MSE), averaged over the objects. The final score is calculated by averaging the evaluation metric over the second half (500 steps) of each trajectory in the evaluation dataset. This is done to remove the initial exploratory part of each trajectory, during which the model has no way of knowing the full layout of the maze (see Figure C.1). We make this choice so that a model with perfect memory could reach 0.0 MSE on the Objects benchmark and 100% accuracy on the Walls benchmark.

The architecture of the probe network is defined as part of the benchmark to ensure comparability: it is an MLP with 4 hidden layers, 1024 units each, with layer normalization and ELU activation after each layer (see Table E.3). The input to the probe network is the representation of the model —

Urrent Target Object Color Rgb

Table 2: Entries in the offline dataset. The tensors are saved as NPZ files with an additional time step, e.g. image tensor for a 1000-step long trajectory is (1001, 64, 64, 3). The first element is the image before the first action and the last element is the image after the last action.

which should be a 1D vector of length 2048 — concatenated with the position and orientation of the agent. The agent coordinate is provided as additional input because the wall layout prediction target is presented in the global grid coordinate system, which the agent has no way of knowing from the first-person observations. The probe network is trained with BCE loss when the output is wall layout, and with MSE loss when the output is object coordinates.

We found a linear probe to not be suitable for our study because it is not powerful enough to extract the desired features (such as the wall layout) from the recurrent state. There is a potential concern that a powerful enough probe network could extract any desired information about the history of observations from the recurrent state, which would invalidate the logic of the test. We provide evidence in Appendix D that this is not the case, and the choice of the 4-layer network is justified.

Online Experiments

In this section, we present online RL benchmark results on the four Memory Maze tasks (9x9, 11x11, 13x13, 15x15) introduced in Section 3.1. We have evaluated the following baselines, including a strong model-based and model-free RL algorithm each:

• Human Player

The data was collected from one person who had experience playing first-person computer games but had not previously seen the Memory Maze task. The player first played several episodes to familiarize themselves with the task and GUI. They then played the recorded episodes, for which they were instructed to concentrate and strive for the maximum score. We recorded ten episodes for each maze size.

• Dreamer

For a model-based RL baseline we evaluated the DreamerV2 agent (Hafner et al., 2020). We mostly used the default parameters from the Atari agent in (Hafner et al., 2020) but increased the RSSM recurrent state size and tuned the KL and entropy scales to the new environment. We increased the speed of data generation (environment steps per update) to make the training faster in wall clock time, at some expense to the sample efficiency. We trained Dreamer on 8 parallel environments. The full hyperparameters are listed in Table E.1.

• Dreamer (Tbtt)

Truncated backpropagation through time (TBTT) is known to be an effective method for training RNNs to preserve information over time. For example, R2D2 agent (Kaptur- owski et al., 2018), when trained with the stored state, shows significant improvement on DMLab tasks compared to zero state initialization. Original Dreamer is trained with zero input state on each batch, which may limit its ability to form long-term memories. To test this, we implement TBTT training in Dreamer by replaying complete trajectories sequentially and passing the RSSM state from one batch to the next. We alleviate the potential problem of correlated batches by forming each T ×B batch from B different episodes, and we start replaying the first episode from a random offset to avoid synchronized episode starts.

• Impala

We choose IMPALA (V-trace) as a strong model-free baseline, which performs well on DMLab-30 (Espeholt et al., 2018) and has a reliable implementation available in SEED RL (Espeholt et al., 2019). The hyperparameters used in our experiments are shown in Table E.2. We

Oracle

Figure 3: Online RL benchmark results after 100M environment steps of training. Error bars show the standard deviation over 5 runs. We find that current algorithms benefit from training with truncated backpropagation through time and succeed on small mazes, but fall short of human performance on the large mazes, leaving room for future algorithmic designs to be evaluated on the Memory Maze.

±1.9

Table 3: Online RL benchmark results. The RL agents were trained for 100M steps, and the reported scores are averaged over five runs, with the standard deviation indicated as a subscript. Human score is an average of 10 episodes, with the bootstrapped standard error of the mean shown as a subscript.

tuned the entropy loss scale by scanning different values and then used the same hyperparameters across all four environments. We also tried increasing the LSTM size, but it did not improve performance. The efficient implementation allowed us to use 128 parallel environments, which was important for the performance achieved on the larger mazes.

• Oracle

We establish an upper bound on the task scores by training an oracle agent that observes complete information about the maze layout and follows the shortest path to the target object. Note that no real agent relying on first-person observations can achieve this upper bound score because the oracle receives the maze layout as input from the beginning of the episode without having to explore it over time. However, we can estimate that this initial exploration, if done efficiently, should not take more steps than reaching a few targets. So the maximum achievable score for agents is within a few points of the Oracle upper bound.

• Dreamer (Tbtt + Probe Loss)

Same as Dreamer (TBTT), but with added Object locations probe prediction auxiliary loss. The gradients from probe prediction are not stopped and flow back into RSSM, encouraging the world model to better extract and preserve information about the locations of objects. Since this agent uses additional probe information during training time, it should not be considered as a baseline of the online RL benchmark.

In the future, it would be interesting to evaluate additional baselines on Memory Maze, such as MRA (Fortunato et al., 2019) that uses episodic memory and GTrXL (Parisotto et al., 2020) that uses a transformer architecture. Unfortunately, the source code of these agents is not publicly available, so we were unable to include them in our comparison.

All agents were evaluated after 100 million environment steps of training. For each baseline and task, we trained five agents with different random seeds and report the average scores. A single Dreamer training run took 14 days to train using one GPU learner and 8 CPU actors. A single IMPALA training run took 20 hours to train using one GPU learner and 128 CPU actors. Our experimental results are summarized in Figure 3 and Table 3. The training curves are provided in Appendix A.

First, we observe that the task is challenging but solvable for a human player. The mean human score is approximately 75% of the oracle upper bound across all four maze sizes. Inspection of episode replays shows that the player is slow at collecting the first few rewards while exploring

Oracle

Figure 4: Comparison of Dreamer (TBTT), trained with the standard world model loss, against Dreamer (TBTT + Probe loss) agent, where we add an auxiliary Object location probe prediction loss, encouraging the model to remember relevant information. Dreamer (TBTT + Probe loss) shows a significant improvement, indicating that the task performance is indeed bottlenecked by memory.

Since this agent uses additional probe information during training time, it should not be considered as a baseline of the online RL benchmark. the new unknown layout, after which the remaining rewards are collected relatively quickly (see Appendix B). This indicates that the task indeed relies on the ability to remember. Moreover, the learning phase becomes longer in the larger mazes, indicating that the human player had to observe the object positions multiple times before remembering them.

Second, we note that the performance of RL agents exceeds the human performance on the smallest 9x9 maze but is far below the human baseline on the largest 15x15 maze, with 11x11 and 13x13 interpolating between the two extremes. This shows that our benchmark uncovers the limits of the Third, we observe the utility of training Dreamer with TBTT, which shows a clear boost to the original Dreamer across all maze sizes. For example, on the Memory Maze 9x9, only the Dreamer (TBTT) agent achieves near-optimal performance.

Finally, even though Dreamer (TBTT) outperforms model-free IMPALA on the smaller mazes, on the larger mazes IMPALA is the best RL agent, making steady progress even on the Memory Maze 15x15. We speculate that IMPALA is better at remembering task-relevant information for longer because model-free policy training encourages the encoder and RNN to only process and retain task-relevant information and ignore the rest. In contrast, the world model of Dreamer tries to remember as much information about the environment as possible, which may limit the memory horizon.

Offline Experiments

In this section, we present offline probing experiments on the four benchmarks: Memory 9x9 (Walls), Memory 9x9 (Objects), Memory 15x15 (Walls), and Memory 15x15 (Objects), that were introduced in Section 3.3. We trained and evaluated the following sequence representation learning models:

• Rssm

Recurrent State Space Model (Hafner et al., 2018) is the world model component of Dreamer agent. We use the exact same model that is part of the DreamerV2 agent (Hafner et al., 2020) that was evaluated on the online benchmark, with the hyperparameters listed in Table E.1.

The probe network receives the full internal state as input, which is a concatenation of deterministic and stochastic states of RSSM.

• Rssm (Tbtt)

As in the online experiments, we evaluate the effect of training RSSM with truncated backpropagation through time (TBTT). Instead of starting with zero recurrent state on each training batch, we sample training sequences from trajectories sequentially and carry over the state from one batch to the next. Note that during inference time, both RSSM and RSSM (TBTT) propagate the state over the complete 1000-step trajectory. The difference is only during training, where RSSM effectively resets the state to zero every 48 steps, and RSSM (TBTT) does not.

• Vae+Gru (Tbtt)

This baseline is a recurrent model that operates on separately learned representations (Ha & Schmidhuber, 2018). The image embeddings are trained with a standard VAE (Kingma & Welling, 2013) on the full dataset, without consideration of their sequential ordering. A GRU-based RNN summarizes the sequence of embeddings and actions in a recurrent state, and a dynamics MLP predicts the next observation embedding, given the current state and action. This network is trained with an MSE loss for the next-embedding prediction. The

Supervised Oracle

Figure 5: Offline probing results on Memory 9x9 and Memory 15x15 datasets. Left: Average accuracy of wall probing (higher is better), with the perfect score being 100% and VAE indicating a no-memory baseline. Right: Average mean-squared error (MSE) of object probing (lower is better), with the perfect score being 0 and VAE indicating a no-memory baseline.

±0.1

Table 4: Offline probing benchmark results. The score is measured in wall prediction accuracy (%) for the Walls benchmarks and in mean-squared error for the Object benchmarks. The scores are calculated over the second half of the evaluation episodes to remove the effect of the initial memory burn-in (see Figure C.1). The models were trained for 1M gradient steps, and we report the mean score over three training runs, with the standard deviation indicated as a subscript.

hyperparameters are chosen to match RSSM were relevant (see Table E.4). The probe predictor uses the GRU hidden state as the representation. This model is also trained with TBTT.

• Supervised Oracle

For comparison, we evaluate a baseline trained in a supervised manner, allowing the gradients of the probe prediction loss to propagate into the whole model. We use the exact same architecture as VAE+GRU, with the only difference of not stopping the probe gradients during training. It is an “oracle” in the sense that it does not follow the standard evaluation procedure, and uses hidden information (probe observations) to train the representation model.

This baseline is useful as an upper (optimistic) bound for models with similar architecture.

• Vae (No-Memory)

A model that simply uses the VAE image embeddings as the input to the probe prediction. It has no memory by design, and it is able to only predict the part of the probe observation which can be inferred from the current first-person view. It serves as a lower (pessimistic) bound for memory models.

All models were trained for 1M gradient steps, and we repeated each training with three random seeds. The evaluation results are summarized in Figure 5 and Table 4. In addition to the trained models, we include a Constant baseline that outputs the training set mean prediction. This score is relatively high on Walls (e.g. 80.8% for 9x9) because a relatively small number of grid cells vary between different layouts. The performance of models falls on the scale between the Constant baseline, as the minimum, and 100% accuracy or 0.0 MSE as the maximum.

We observe that all evaluated models show some memory capacity (being better than the no-memory baseline), but fall short of perfect memory. The 15x15 Walls is an especially challenging benchmark, where the models barely outperform the no-memory baseline. This is consistent with the low performance of the corresponding agents on the 15x15 online RL benchmark, and suggests that Among the models, RSSM (TBTT) reaches the highest performance, outperforming RSSM and VAE+GRU (TBTT). This shows that both truncated backpropagation through time and the stochastic

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

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

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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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