Arduino Video Streaming

And Christian Timmerer

Abstract—Adaptive streaming of segmented video over HTTP typically relies on a predefined set of bitrate-resolution pairs, known as a bitrate ladder. However, fixed ladders often overlook variations in content and decoding complexities, leading to suboptimal trade-offs between encoding time, decoding efficiency, and video quality. This article introduces DQ-Ladder, a deep re- inforcement learning (DRL)-based scheme for constructing time- and quality-aware bitrate ladders for adaptive video streaming applications. DQ-Ladder employs predicted decoding time, qual- ity scores, and bitrate levels per segment as inputs to a Deep Q-Network (DQN) agent, guided by a weighted reward function of decoding time, video quality, and resolution smoothness. We leverage machine learning models to predict decoding time, bitrate level, and objective quality metrics (VMAF, XPSNR), eliminating the need for exhaustive encoding or quality metric computation. We evaluate DQ-Ladder using the Versatile Video Coding (VVC) toolchain (VVenC/VVdeC) on 750 video sequences across six Apple HLS-compliant resolutions and 41 quantization parameters. Experimental results against four baselines show that DQ-Ladder achieves BD-rate reductions of at least 10.3 % for XPSNR compared to the HLS ladder, while reducing decoding time by 22 %. DQ-Ladder shows significantly lower sensitivity to prediction errors than competing methods, remaining robust even with up to 20 % noise.

Index Terms—Adaptive Video Streaming, Deep Reinforcement Learning, Q-Learning, Bitrate Ladder, Quality Prediction.

I. Introduction

Video traffic, mainly generated by video-on-demand and live streaming, dominates global Internet traffic, increasing demands for high-quality, low-latency, and sustainable con- tent delivery , . Among streaming technologies, HTTP Adaptive Streaming (HAS), standardized by MPEG Dynamic Adaptive Streaming over HTTP (DASH) and HTTP Live Streaming (HLS) , has become the dominant video delivery mechanism. HAS splits video content into segments, each encoded at multiple bitrates and resolutions, known as repre- sentations forming a bitrate ladder, stored on HTTP servers.

HAS clients employ adaptive bitrate algorithms to select and retrieve each segment from the ladder in an appropriate representation, optimizing playback quality . However, the growing demand for high-resolution content and advances in video codecs have expanded the number of representations in the ladder to accommodate diverse device capabilities .

Reza Farahani, Zoha Azimi, Hermann Hellwagner, and Christian Tim- Schahram Dustdar is with the Research Division of Distributed Systems at Traditionally, bitrate ladder construction has relied on fixed rules that ignore content-specific characteristics affecting vi- sual quality and encoding efficiency . Netflix’s per-title encoding marked a shift toward content-aware encoding by analyzing each video’s rate–distortion behavior to derive improved ladders using perceptual quality metrics such as Video Multimethod Assessment Fusion (VMAF) . Building on this paradigm, several recent works – determine resolutions within a target bitrate range to further improve compression efficiency and perceptual quality. However, these approaches typically overlook decoding time, which is in- creasingly critical for resource-constrained end-user devices.

Consequently, balancing visual quality and decoding time remains a key challenge in ladder construction. Conventional heuristic-based approaches , optimize resolution and quantization parameters (QPs) independently at each target bitrate, ignoring how early decisions influence later trade- offs such as quality consistency, decoding complexity growth, and smooth resolution transitions across the ladder . In contrast, deep reinforcement learning (DRL) formulates ladder construction as a sequential decision problem, enabling a global policy that anticipates long-term quality and complexity trade-offs and coordinates decisions across the entire ladder.

This article proposes DQ-Ladder, a DRL-based frame- work that balances quality, decoding time, and resolution smoothness. It exploits video complexity features and incor- porates lightweight machine learning (ML) predictors within a modular architecture to predict the expected bitrate, decoding time, and objective quality (measured via VMAF and XPSNR) for unseen video sequences. These predictions guide a Deep Q-Learning (DQN) agent to explore the multi-objective trade- off space. Extensive experiments on a dataset of 750 diverse real-world video sequences with varying content complexities demonstrate that DQ-Ladder outperforms four state-of-the- art methods, achieving BD-rate savings of at least 10.9 % (PSNR) and 10.3 % (XPSNR), while lowering decoding and encoding time by up to 22 % and 31 %. DQ-Ladder is robust to prediction errors, maintaining stable quality–decoding trade- offs even when rate–distortion and decoding-time estimates are perturbed by up to 20 % noise.

The contributions of DQ-Ladder are four-fold: (i) Modular architecture: We design a DRL-based architecture leveraging DQN for bitrate ladder construction, jointly optimizing quality and decoding complexity. (ii) Metric prediction: We leverage lightweight ML models to predict bitrate, decoding time, and quality metrics, enabling online content-adaptive ladder gener- ation. (iii) Problem formulation: We design a weighted reward

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function to efficiently explore the bitrate–quality–decoding time trade-off space, incorporating constraints and penalties to guide decision-making. (iv) Comprehensive evaluation: We conduct extensive experiments using 750 real video sequences with various encoding configurations and the VVC codec, showing the superiority of DQ-Ladder over four baselines in terms of rate-distortion performance, decoding time reduction, and encoding speed-up.

The article has eight sections. Section II reviews related work. Section III formulates the bitrate ladder construction problem as a Markov decision process (MDP). Section IV presents its architecture, followed by the execution workflow of DQ-Ladder in Section V. Section VI describes the dataset, prediction models, and experimental setup. Section VII ana- lyzes the evaluation results. Finally, Section VIII concludes the article and discusses future directions.

While Mean Opinion Score (Mos) Provides The Most

accurate perceptual quality assessment, its cost and subjec- tivity make it impractical for real-time applications, motivat- ing learning-based approaches for MOS prediction –.

Several works employed decision trees (DT) , multi-layer perceptron , or federated learning , leveraging encod- ing parameters, network conditions (delay and packet loss), and user data for MOS prediction. Beyond MOS, PSNR ,

Ssim , And Vmaf Have Been Widely Adopted For

content-aware video streaming and encoding optimization. Amirpour et al. present VQM4HAS, a regression model that approximates VMAF for HAS representations using low- cost features and encoder logs, avoiding explicit VMAF computation at scale. Although XPSNR seems more artifact- sensitive and computationally efficient than VMAF –, predictive modeling remains unexplored.

Kranzler et al. designed a bitstream-based decoding energy prediction model using multivariate linear regression, integrated into their proposed encoder for energy-aware rate- distortion optimization. Boujida et al. proposed an ML- based VVC per-frame decoding time prediction using extra trees regression (ETR) with features extracted from the bit- stream, i.e., coding unit size, split depth, motion vectors, enabling adaptive decoding control. Ghasempour et al.

modeled decoding energy using video resolution and framerate as features, leveraging a lightweight lookup table approach for fast estimation in live streaming. Boujida et al. extended their per-frame VVC decoding time model by integrating lightweight temporal feedback (past decoding times) and fea- ture selection, enabling accurate multi-resolution prediction for dynamic voltage and frequency scaling (DVFS)-based live streaming adaptation. Existing works rely on codec-dependent syntax or full bitstream parsing, limiting low-latency online use; their generalization to unseen content and diverse encod- ing settings remains largely unexplored.

Katsenou et al. provided a content-driven bitrate ladder for HEVC-encoded video sequences using regression-based prediction models. Bhat et al. employed RF models to predict the best resolution for each scene, while Mueller et al. combined convolutional neural networks, XGBoost, and MLP for content complexity analysis. Menon et al.

used random forest (RF) models to predict VMAF and select bitrate-resolution in a Just Noticeable Difference (JND)- aware encoding scheme. Azimi et al. integrated XGBoost models to estimate encoding time and quality metrics, enabling decoding-aware bitrate ladder construction. Lebreton et al. , proposed ladder generation based on user quitting prob- ability to align quality with user engagement. Rajendran et al. used Pareto-front analysis to predict optimized framer- ates for constructing energy-efficient ladders. Zhao et al.

proposed a per-shot bitrate ladder construction method that encodes a small subset of resolution-bitrate-preset points and predicts the remaining rate-distortion curves using curve fitting and ML models (e.g., RF, support vector machines, neural networks). Existing methods focus on quality alone, neglecting decoding complexity and resolution smoothness, and requiring exhaustive pre-encoding.

Iii. Dq-La D D E R Model

We formulate bitrate ladder construction as a Markov deci- sion process (MDP). 1) Environment: We define the environment as the video encoding system operated by the video providers, where each video is split into segments S = {s1, s2, . . , sm}, each encoded at a set of target bitrates T B = {tb1, tb2, ..., tbn}.

For each target bitrate tbi ∈T B, the DRL agent selects a resolution ri ∈R and quantization parameter qpi ∈QP from predefined sets. The environment provides three es- sential feedback signals per segment based on lightweight predictive models: decoding time bT = f1(EY , h, LY , R, QP), quality score bQ = f2(EY , h, LY , R, QP), and bitrate level bB = f3(EY , h, LY , R, QP), where f1, f2, and f3 are the cor- responding predictive functions. Here, EY shows luma texture complexity, h denotes temporal complexity, and LY represents luma brightness extracted from the video segment.

|S| · |T B|,

encompassing all possible combinations of video segments and target bitrate levels. We define the state sti ∈S as

, Where It Captures The Current

target bitrate tbi for each segment and the predicted outcomes of the previous encoding step i −1, i.e., bitrate bbi−1 ∈bB, quality bqi−1 ∈bQ, and decoding time bti−1 ∈bT , allowing the agent to reason encoding behavior without incurring the high computational cost of real encoding and metric computation.

An

encoding configuration for target bitrate tbi ∈T B defined as ai = [ri, qpi], where ri ∈R is the chosen resolution and qpi ∈QP is the selected quantization parameter. The overall action space A = {A1, . . , Ak} is the union of all possible actions across video segments S and target bitrates T B, representing the full set of feasible encoding decisions during ladder construction.

4) Reward function: The reward function guides the agent in building an efficient bitrate ladder by maximizing quality,

Update

Figure 1: DQ-Ladder system architecture. minimizing decoding time, and preserving visual stability across segment encodings. After selecting action ai = [ri, qpi] for segment sj ∈S at target bitrate tbi ∈T B, the agent

Receives A Reward Rewi Formulated As:

Rewi = λ1 · b¯qi −λ2 ·b¯ti −λ3 · δi.

(1)

where b¯qi and b¯ti denote the normalized predicted quality and decoding time, calculated using min-max normalization:

Btmax −Btmin

.

(2)

where bqmin, bqmax, btmin, and btmax represent the minimum and maximum predicted quality and decoding time values across the candidate actions. To penalize frequent resolution switches between segments and avoid unnecessary visual fluctuation,

0,

otherwise.

(3)

where ri−1 ∈R is the resolution selected in the previous en- coding step i−1. The penalty term ensures visual consistency across segments, avoiding unnecessary resolution fluctuations.

The λ coefficients control the trade-off among quality maxi- mization, decoding efficiency, and resolution stability and are empirically selected based on system preferences.

5) Encoding constraints: We impose two essential con- straints to guarantee bitrate and quality control in the con- structed ladder. First, the predicted bitrate bbi of the selected action ai = [ri, qpi] must not exceed the target bitrate thresh- old tbmax, ensuring bitrate-constrained encoding per segment: bbi ≤tbmax.

(4)

Second, we enforce quality monotonicity across increasing target bitrates to avoid quality degradation at higher rates: tbi > tbi−1 ⇒bqi ≥bqi−1.

(5)

which guarantees an ordered bitrate ladder where higher bitrate levels always provide equal or better objective quality.

Iv. Dq-Ladder Architecture

We design a modular architecture for DQ-Ladder to en- able efficient video segment-level bitrate ladder construction, as illustrated in Fig. 1. The architecture integrates three main modules: (1) video complexity analyzer, (2) lightweight ML- based metric predictors, and (3) a DQN agent. Given a set of video segments S = {s1, s2, . . , sm}, the video complexity analysis extracts spatial, temporal, and brightness features from each segment, and considers the luma-based features ⟨EY , h, LY ⟩. These features, combined with candidate encod- ing parameters (R, QP), are provided to three independent predictors f1, f2 and f3, estimating decoding time set bT , quality set bQ, and bitrate set bB without real encoding overhead.

The quality predictor is metric-agnostic. Although we train separate predictors for XPSNR and VMAF (Section VI-B), the model used at inference time is selected according to the service provider’s optimization objective. For example, when optimizing VMAF, it instantiates the VMAF predictor, and the DQN agent maximizes rewards concerning that metric.

The DQN agent interacts with the environment to construct the segment-specific ladder using three main steps: 1) DQN agent operation: The agent models the state-action value function Q(sti, ai) via a neural network and iteratively refines it using observed experience. At each decision step, for a given segment sj ∈S and target bitrate tbi ∈T B, the agent observes the current state sti = [tbi,bbi−1, bqi−1, bti−1], capturing the current target bitrate tbi and the outcomes of the previous encoding step (i −1). This design enables fast decision-making by leveraging learned predictions, avoiding real encoding computation while retaining awareness of past encoding behavior for stability. Based on this state, the agent selects an action ai = [ri, qpi] from the action space A, specifying the resolution ri ∈R and quantization parameter qpi ∈QP to encode the segment sj at tbi. The environment then returns the predicted metrics (bbi, bqi, bti) for the chosen configuration, used to compute the reward Rewi based on the multi-objective reward function defined in Eq. 1. To learn a policy that optimizes long-term cumulative reward, the agent minimizes the DQN loss, defined as the mean squared error between the Q-value predicted by the Q-network for the current state–action pair and a target value computed using the target network. The Q-network parameters are updated according to the Bellman optimality equation (Eq. (6)):

Q(Sti, Ai) ←Rewi + Γ · Max

a′ Q (sti+1, a′) .

(6)

where a′ denotes all possible actions at the next state sti+1, and γ is the discount factor that controls future reward impact. 2) DQN experience memory: The agent maintains an ex- perience replay memory Mem to enhance learning stability and avoid overfitting to recent decisions. After selecting action ai = [ri, qpi] in state sti and observing reward Rewi and next

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state sti+1, the agent stores the tuple (sti, ai, Rewi, sti+1) in Mem. To update the Q-network, the agent uniformly samples mini-batches from Mem, enabling diverse learning from past encoding experiences across different segments in S.

The Decision Process Continues Itera-

tively for all tbi ∈T B, constructing the segment-level lad- der Lj = {(tb1, ri, qpi) , . . , (tbn, rk, qpk)} for sj. The fi- nal output is the complete set of bitrate ladders L

=

{L1, L2, . . , Lm} covering all segments in S, subsequently passed to the encoder for final processing.

V. Dq-Ladder Execution Workflow

The operational workflow of DQ-Ladder includes two phases: (1) DQN training conducted provider-side using re- alistic encoding feedback and (2) DQN inference for ladder construction using lightweight predictions.

1) DQN training: Alg. 1 shows the training procedure of the DQN agent, performed on a provider-side video dataset to learn an effective bitrate ladder construction policy. The agent interacts with a real encoding environment, where each seg- ment sj ∈S is encoded with different configurations (ri, qpi) to collect actual decoding time ti, quality qi, and bitrate bi values. At each training epoch e ∈EP (line 4) and decision step for segment sj and target bitrate tbi ∈T B (lines 5 -6), the agent observes the state sti = [tbi, bi−1, qi−1, ti−1] using prior encoding results (line 7). The agent selects an action ai = [ri, qpi], specifying the resolution ri and quantization parameter qpi, using an ϵ-greedy strategy (lines 8–11). With probability ϵ, a random action is chosen to explore new configurations; otherwise, with probability 1 −ϵ, the action with the highest current Q-value is selected for exploitation.

The selected action triggers real encoding (line 12), from which the measured bitrate bi, quality qi, and decoding time ti are obtained and used to compute the reward Rewi (lines 13–14). The next state sti+1 is then observed (line 15), and the experience tuple (sti, ai, Rewi, sti+1) is then stored in the experience replay memory Mem (line 16). Next, the Q-network parameters θ are updated using mini-batches from Mem (line 17), minimizing the temporal difference loss between the predicted and target Q-values (lines 18-19).

To stabilize learning, the target network parameters θ−are periodically synchronized with θ every C steps (line 20). Next, the exploration rate ϵ is linearly decayed toward ϵend (line 21) and clamped to avoid falling below ϵend (line 22). Finally, the training proceeds over subsequent epochs (line 23) until convergence. Finally, the output is the trained Q-function with parameters θ used for inference (line 24).

We note that the DQN training procedure is codec-agnostic and independent of specific reward weight values. Codec- specific effects are implicitly captured by the measured bitrate, quality, and decoding time values obtained during training, while the reward weights reflect the provider’s optimization preferences. Thus, adapting DQ-Ladder to a different codec or service objective does not require changes to the learning formulation or network architecture, but only retraining with the corresponding measurements and updated reward weights.

Algorithm 1: DQN Training Algorithm. Input: Video segments S, Target bitrates T B, Resolutions R, QPs QP, discount factor γ, exploration rate start ϵstart, exploration rate end ϵend,

1 Initialize Replay Memory Mem

2 Initialize network Q(st, a; θ) and target network ˜

24 Return (Q(St, A; Θ))

Algorithm 2: DQN Inference Algorithm. Input: Video segments S, Target bitrates T B, Resolutions R, QPs QP,

Return (Lj)

2) DQN inference: Alg. 2 shows the DQN online inference procedure. The trained DQN agent operates without real en- coding, leveraging lightweight content features and ML-based prediction models. For each segment sj ∈S (line 1), the video complexity analyzer extracts content features ⟨EY , h, LY ⟩ (line 2). At each target bitrate tbi ∈T B (line 3), the agent observes the current state sti = [tbi,bbi−1, bqi−1, bti−1] (line 4) using the target bitrate and predicted metrics from the previous step. The action ai = [ri, qpi] is then selected by maximizing the learned Q-function (line 5). The environment returns the predicted outcomes (bbi, bqi, bti) using the predictors f1, f2, and f3 (line 6), avoiding costly real encoding. This process iterates over all target bitrates to construct the segment-specific bitrate ladder Lj (line 8). The corresponding ladder Lj is finally returned (line 8), making it available for encoding.

3) Computational complexity analysis: The offline DQN training (Alg. 1) has a time complexity of O(EP · |S| · |T B| · (Cenc + BA · CNN)), where EP is the number of epochs, |S| is the number of video segments, |T B| is the number of target bitrates, Cenc is the cost of real encoding per action (executed offline), BA is the mini-batch size, and CNN is the neural network computation cost for forward and backward passes. The online inference (Alg. 2) operates with complexity of O(|S| · |T B| · (CNN + Cpred)), where Cpred is the worst time of the lightweight prediction models f1, f2, f3.

5

Table I: Encoding and decoding configuration parameters.

Vi. Evaluation Setup

This section details the evaluation setup of DQ-Ladder. We conducted all experiments on a server with a 128-core Intel Xeon Gold CPU and two NVIDIA Quadro GV100 GPUs.

A. Dataset Explanation And Analysis

We use 750 ultra-high-definition (UHD) video sequences

Con-

tent types such as nature, urban, sports, and synthetic scenes. Each sequence was encoded at 60fps using VVenC

Resolutions

R = {360, 540, 720, 1080, 1440, 2160}p and 41 quantization parameters QP = {10, . . , 50}. For every encoded version, we recorded the actual bitrate bi, encoding time, and the de- coding time ti using VVdeC v2.3.0 . Objective quality scores qi were measured using XPSNR and VMAF, providing ground-truth labels for training the DQN agent. Spatiotemporal complexity features were extracted using VCA v2.0 . We analyzed the dataset characteristics to highlight the content diversity. Fig. 2 shows the distribution of video sequences in the (EY , h) feature space, highlighting the diversity of spatial and temporal complexity, with color indicating variations in luminance LY .

B. Ml-Based Metric Prediction Models

Given the high overhead of real encoding, decoding, and quality measurements, DQ-Ladder employs lightweight ML- based predictors for decoding time, quality, and bitrate, en- abling fast ladder construction during online inference (Sec- tion V). We evaluated seven well-known ML models covering four different categories: a) Linear Regression (LR) as a simple linear model; b) Random Forest (RF) and Extra Trees Regression (ETR) as tree-based ensemble models;

C) Adaboost , Lightgbm , And Xgboost As

gradient boosting-based ensembles; d) Multi-Layer Perceptron (MLP) as a neural network with fully connected layers. We partitioned the dataset into disjoint training (70 %) and testing (30 %) sets at the video level, ensuring that all segments from a given video belong exclusively to one set to avoid data leakage. Video identifiers were randomly shuffled with a fixed seed (seed = 42) to guarantee reproducibility and consistent splitting across the three prediction tasks. We applied Grid- SearchCV from Scikit-learn with five-fold cross- validation on the training set to search for hyperparameters in ensemble-based models. We performed an exhaustive grid search over predefined parameter spaces and retrained the best estimator on the training data. We used 10 % of the training set as a validation set and applied Optuna for hyperparameter optimization in the MLP model.

Figure 2: Content complexity feature distribution in the dataset. We applied model-specific hyperparameter tuning based on the design flexibility of each predictor. While the LR model has no tunable parameters, ensemble-based models require optimization of the number of estimators (ntrees), maximum tree depth (dmax), and learning rate (η). We fine-tuned the number of hidden units (hunits), learning rate (η), and batch size (bs) for the MLP model. Table II summarizes the explored hy- perparameter search space per model. In our final evaluation, we used the best-performing model per target metric (decoding time, bitrate, XPSNR, VMAF), and reported the corresponding hyperparameter values in Section VII-A.

C. Dqn Agent Configurations And Hyperparameters

To approximate the Q-value function, we employ a multi- layer perceptron (MLP) that takes the four-dimensional state

Vector Si

t as input. All state features are normalized to the range [0, 1] using min–max scaling to improve stability and convergence. The network consists of three connected hidden layers with 256, 128, and 64 neurons, using ReLU activation functions . The output layer is a linear fully connected layer of size |A|, where each neuron represents the Q-value of a discrete action pair (ri, qpi). The model is implemented in PyTorch with Kaiming (He) initialization and optimized using Adam . The DQN agent is trained for EP = 2000 episodes using experience replay with a memory size of |Mem| = 10000 and a mini-batch size of BA = 128. The target network is synchronized every C = 10 steps. We adopt an ϵ-greedy exploration strategy, with ϵ linearly decayed from 0.8 to ϵmin = 0.05 over 2000 steps. Hyperparameters are tuned via a combination of Optuna-based optimization and man- ual refinement. The learning rate η ∈{0.05, 0.005, 0.0005} (depending on the experiment) and discount factor γ = 0.7.

Table III summarizes the final DQN agent hyperparameters.

D. Evaluation Metrics

We use different evaluation metrics to assess DQ-Ladder performance and compare it with baselines. 1) Prediction model metrics: We use the following standard regression metrics to evaluate the accuracy and generalization performance of the prediction models: a) Coefficient of deter- mination (R2); b) Mean absolute error percentage (MAE%); c) Root mean squared error (RMSE); d) Standard deviation of absolute errors (SDAE); e) Inference time.

We Evaluate The

agent’s learning behavior using the cumulative reward evolu- tion, which aggregates per-episode rewards over training steps to reflect overall policy performance; higher values indicate better learning progress.

Authors:

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

94143, Usa.

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

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

14Jlvmi Consulting Llc, Dousman, Wi, Usa

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

Abstract

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

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

Introduction

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

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

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

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

●

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

●

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

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

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

Hyperpolarized 13C-Pyruvate Preparation

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

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

General Considerations

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

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

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

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

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

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

Personnel

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

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

Equipment And Facility

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

Material Handling

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

Pharmacy Kit Filling And Assembling

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

Quality Control And Dose Release

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

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

The Final Dose Release And Injection

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

Some Key Challenges

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

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

Current Practices

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

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

In House

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

Summary

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

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

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

Mri System Setup And Calibrations

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

Imaging System

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

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

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

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

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

Rf Coils

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

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

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

Provide B1 Transmit Across The Fov (B1

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

B1

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

+ Profile But Has Been Used Because Of

relatively easy integration into the scanner bore. B1

+ Variation Results In Variations In The Flip

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

Homogeneous B1

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

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

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

(1)

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

Tx = Transmit

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

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

Phantoms

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

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

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

+) And Receive (B1

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

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

Prescan Calibration

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

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

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

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

+ Inhomogeneity As Well

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

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

Power [Kw]

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

8

13C-bicarbonate doped with dimethyl silicone, various

Maximum Values

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

Summary

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

+ Profiles. The

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

For Calibration Of B1

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

Acquisition And Reconstruction

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

+ Inhomogeneity,

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

Acquisition And Reconstruction Methods

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

Mrs/I Methods Specifically

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

Chemical Shift

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

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

Their Application To Different

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

The Majority Of

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

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

Prostate Studies

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

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

Heart Studies

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

Brain Studies

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

Abdomen And Breast Studies

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

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

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

1H Imaging

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

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

Reported Study Parameters

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

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

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

(B)

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

Summary

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

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

Data Analysis And Quantification

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

Metrics

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

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

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

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

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

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

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

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

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

Visualization

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

Metrics

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

Parameter Encoding

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

Anatomical Context

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

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