Long Fiber Composite Digimat

Synthesizing consistent and coherent long video remains a fundamental challenge. Existing methods suffer from semantic drift and narrative collapse over long horizons. We present A2RD, an Agentic Auto- Regressive Diffusion architecture that decouples creative synthesis from consistency enforcement. A2RD formulates long video synthesis as a closed-loop process that synthesizes and self-improves video segment- by-segment through a Retrieve–Synthesize–Refine–Update cycle. It comprises three core components: (i) Multimodal Video Memory that tracks video progression across modalities; (ii) Adaptive Segment Generation that switches among generation modes for natural progression and visual consistency; and (iii) Hierarchical Test-Time Self-Improvement that self-improves each segment at frame and video levels to prevent error propagation. We further introduce LVbench-C, a challenging benchmark with non-linear entity and environment transitions to stress-test long-horizon consistency. Across public and LVbench-C benchmarks spanning one- to ten-minute videos, A2RD outperforms state-of-the-art baselines by up to 30% in consistency and 20% in narrative coherence. Human evaluations corroborate these gains while also highlighting notable improvements in motion and transition smoothness.

Multi-Scene (LVBench-C; 5m): A disheveled young pianist named Clara transforms from quietly composing in a dusty attic to delivering a triumphant, emotionally charged concert performance at a grand theater, ending the night before returning to her room and gently placing the flowers on the piano in gratitude. Single-Scene (VBench-Long; 1m): A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually...

Single-Scene (VBench-Long; 1m): Animated scene features a close-up of a short fluffy monster kneeling beside a melting red candle. The art style is 3D and realistic, with a focus on lighting and texture. The mood of the painting is one of wonder and curiosity, as the monster gazes at the flame... Figure 1 | Examples of 1m and 5m videos generated by A2RD with Veo 3.1, showcasing consistent and coherent stories in static, dynamic, and multi-shot environments. Full and more storyboards are provided in Figures 2 and 3 and Section F, with videos in the supplementary materials.

Project Page: Http://Dxlong2000.Github.Io/Aard

* This work was done while Do Xuan Long was a Student Researcher at Google Cloud AI Research.

Arxiv:2605.06924V1 [Cs.Cv] 7 May 2026

A2RD: Agentic Autoregressive Diffusion for Long Video Consistency

Ntroduction

Video synthesis has emerged as a transformative capability in artificial intelligence, powering high- impact applications including cinematic storytelling, educational content, entertainment, and ad- vertising (Elmoghany et al., 2025; Ma et al., 2025). Although recent breakthroughs in diffusion models (Brooks et al., 2024; ByteDance Seed, 2026; Esser et al., 2023; Google Deepmind, 2025; Ho et al., 2022; Singer et al., 2023; Wan et al., 2025a) have achieved remarkable fidelity for second-long clips, real-world applications demand minute- to hour-long videos. Scaling to coherent long video synthesis, however, remains a fundamental challenge. At its core are two fundamental problems: temporal consistency, which requires models to track and preserve entities, environments, and motion dynamics, and narrative coherence, which demands that videos evolve meaningfully over time.

State-of-the-art long video synthesis approaches follow the dominant passive, open-loop paradigm, yet they have limitations. Frame-based autoregressive (FAR) models synthesize videos frame-by-frame or chunk-by-chunk (Chen et al., 2026; Huang et al., 2025a; Yang et al., 2026a), naturally preserving local temporal continuity. However, once a frame is generated, it is frozen as fixed conditioning for all subsequent generation, causing errors to propagate uncorrected and limiting narrative controllability.

This often leads to semantic drift and narrative repetition over long horizons (Zhao et al., 2026). Segment-based methods synthesize and concatenate short segments, either autoregressively (SAR) (An et al., 2026; Zhang et al., 2025a; Zhou et al., 2026) or in parallel (Wang et al., 2026; Wu et al., 2025b), offering stronger narrative control. However, they struggle to maintain inter-segment consistency and continuity (Elmoghany et al., 2025). While recent SAR methods employ frame-based memory to bridge segments, visual-only conditioning proves insufficient for reliably tracking entities and narratives, causing persistent consistency and coherence errors across segments (Figure 2, 3, F).

We reframe long video synthesis as a closed-loop, agentic process to address these limitations. Our resulting architecture, A2RD (/a:rd/, Agentic Auto-Regressive Diffusion), enables video diffusion models to synthesize and self-improve long videos autoregressively, enforcing temporal consistency and narrative coherence over long horizons. See Figure 1 for example videos. A2RD is training-free and built upon three pillars: a Multimodal Video Memory, an Adaptive Segment Generation, and Refine–Update cycle: for each segment, the agent retrieves relevant video world contexts from memory, determines the segment generation mode (e.g., extrapolation or interpolation) adaptively, synthesizes boundary frames then video segment with hierarchical self-improvements applied at both frame and video levels, and finally updates the memory for subsequent generation.

Moreover, existing benchmarks lack the realistic complexity of long-horizon narratives, where entities and environments undergo non-linear transitions. We contribute LVbench-C, a benchmark designed to challenge long-horizon consistency where entities and environments appear, disappear, and reappear (“cyclic”) with optional state changes. Extensive evaluations on LVbench-C and public benchmarks show that A2RD achieves state-of-the-art consistency and narrative coherence in just two self-improved iterations, corroborated by human studies. In summary, this paper contributes: • We introduce A2RD, the first agentic autoregressive architecture for long video synthesis that integrates multimodal memory, adaptive segment generation, and self-improvement to enforce temporal consistency and narrative coherence. A2RD significantly outperforms existing baselines, scaling to ultra-long video while substantially mitigating semantic drift and content collapse.

• We contribute LVbench-C, a challenging benchmark evaluating long-horizon video consistency through cyclical entity and environment appearances with optional state evolutions. • We conduct extensive experiments to provide insights into A2RD and its key components.

A2RD: Agentic Autoregressive Diffusion for Long Video Consistency ... ...

Seconds

Figure 2 | Narrative progression: a VBench-Long sample depicting a woman walking on a Japanese street. A2RD maintains coherent and continue story, while baselines exhibit poor entity and environment progression. ...

(Ours)

Figure 3 | Long-horizon consistency: a LVbench-C sample depicting a diver preparing, diving, and returning to the ship. A2RD maintains rigorous consistency while baselines suffer from unintended drifts in environments (red) and entities (yellow), such as changing character’s hair, face, accessories and ship layouts.

Related Work

Long-Form Video Synthesis. State-of-the-art (SOTA) long video synthesis approaches are passive, autoregressively, conditioning each on prior content via rolling KV caches (Huang et al., 2025a), short-window attention with frame-level sinks (Yang et al., 2026a), or initial-frame anchoring (Liu et al., 2026). While preserving local visual fidelity, they remain prone to semantic drift and content repetition, and offer limited controllability (Zhao et al., 2026). Segment-based methods synthesize segments in parallel, with (Meng et al., 2026; Wang et al., 2025a) or without shared denoising (Wang et al., 2026; Wu et al., 2025b; Yin et al., 2023), or autoregressively (SAR) (An et al., 2026; Zhang et al., 2025a; Zhou et al., 2026). These offer finer narrative control but struggle with inter-segment consistency (Elmoghany et al., 2025). Segments are typically synthesized via extrapolation from a begin frame (An et al., 2026; Wang et al., 2026; Zhou et al., 2026) or interpolation between planned boundaries (Yin et al., 2023). Yet, each has limitations: extrapolation often causes inconsistencies for details absent from the begin frame, while interpolation can yield unnatural progression from poorly planned boundary frames. Existing methods also lack mechanisms to correct such errors, causing them to propagate across segments (Figure 3, Section F). A2RD addresses these limitations by coupling SAR with an adaptive generation strategy, multimodal memory for richer conditioning, and closed-loop self-improvement, achieving strong temporal consistency, and narrative controllability.

Test-Time Scaling for Generative Models. Test-time scaling (TTS) improves generation quality by investing additional computation during inference (Snell et al., 2025). For image, this includes best-of-N sampling (Zhang et al., 2025b), iterative refinement (Qu et al., 2026; Zhuo et al., 2025), prompt optimization (Mañas et al., 2024; Wan et al., 2025b; Wang et al., 2024a), and evolutionary search (He et al., 2025). Video TTS methods have recently emerged (Gao et al., 2025; Hong et al., 2026; Huang et al., 2025b; Long et al., 2026; Yang et al., 2026b; Zhu et al., 2026), primarily focusing on prompt optimization: RAPO (Gao et al., 2025) enriches prompts through retrieval-augmented refinement, VISTA (Long et al., 2026) employs multi-agent iterative planning and critique, and VQQA (Song et al., 2026) uses VLM-generated questions for closed-loop optimization. However, these methods operate on single-segment quality only and do not address inter-segment consistency or narrative progression across segments. A2RD introduces efficient test-time algorithms specifically targeting consistency and narrative coherence in multi-segment long video synthesis.

Memory for LLM Agents. Memory has become an important component in modern agentic systems, enabling agents to maintain long-range dependencies across sequential decisions (Hu et al., 2025; Zhang et al., 2025c). Current memories for LLM agents save information in diverse formats including text (Packer et al., 2023; Zhong et al., 2024), hidden representations (Wang et al., 2025b), and graphs (Chhikara et al., 2025; Xu et al., 2025), and typically incorporate retrieval mechanisms (e.g., semantic search) alongside management strategies (e.g., updating). Memory construction for image and video synthesis has also been increasingly studied, where the memory is typically composed of images (Parmar et al., 2018; Yu et al., 2025a; Zhang et al., 2025a; Zhou et al., 2026), image–text pairs (Zhu et al., 2019), and hidden representations (Cai et al., 2026; Zhu et al., 2025). While image-based memories provide visual references, relying on the generative models to implicitly infer entity identity and narrative state is unreliable over long horizons. Representation-based memories offer seamless conditioning but lack interpretability for explicit consistency control. A2RD addresses both limitations with a multimodal memory that explicitly tracks fine-grained visual and narrative world progression across modalities, enabling targeted control over consistency and coherence.

Dentity Dependencies

Synthesize Global Ref. AGENTIC AUTO-REGRESSIVE GENERATION PIPELINE (Sec. 3.2)

Generation

Figure 4 | Overview of A2RD architecture. For each segment, A2RD retrieves relevant context from memory, adaptively determines the generation mode (extrap. or interp.), and synthesizes boundary frames followed by the video segment, with hierarchical self-improvements. Blue boxes denote methods implemented in A2RD.

A2Rd: Agentic Auto-Regressive Diffusion

We present A2RD (Figure 4), an agentic segment-based autoregressive architecture for long video synthesis. We term our basic generation unit as “segment” (equivalent to “clip”), a flexible unit that can span one or more scenes or shots. A2RD takes as input a user context 𝑃, a storyline 𝒮= {𝑆1, . . , 𝑆𝑁} (provided or planned from 𝑃) with 𝑆𝑖being 𝑖-th segment context, and optional reference images ℛ𝑢. The agent supports any Text-Image-to-Video (TI2V) model via incorporating a Multimodal Large Language Model (MLLM) and a Text-Image-to-Image (TI2I) model. It begins by initializing a Multimodal Video MEMory (MVMem) via synthesizing global entity and environment references ℛ, then synthesizing video segments autoregressively, continuously retrieving and updating the MVMem for context-aware synthesis and self-improvement.

Em Design And Initialization

MVMem enables A2RD to explicitly track evolving video world states and events, thus enforcing long-range dependencies for temporal consistency and narrative coherence across segments. Memory Schema.

Unlike existing studies that store only visual references, MVMem stores structured contexts from synthesized segments, denoted as ℳ:= {ℳ1, . . , ℳ𝑁} ∪ℛ∪𝒟. Here, ℛis the set of global reference frames (including user-provided ℛ𝑢), and 𝒟is the prompt database. Each segment memory ℳ𝑗:= {𝑇𝑗, ℱ𝑗, 𝑉𝑗} disentangles the video segment into three complementary modalities: • Textual States (𝑇𝑗). To capture the evolving narrative for consistency and coherence, we model the video’s underlying state as a structured, fine-grained representation, inspired by (Johnson et al., 2015). 𝑇𝑗consists of: (1) Visual Arcs that track entity and environment features and their temporal evolution, recording elements’ Identity, Identity Changes, and Motion; (2) Spatial Relations that capture subject-relation-object triplets from the begin frame to ground geometric layouts; and (3) Camera states that record viewport trajectories for visual continuity. We extract 𝑇𝑗 hierarchically: first deriving elements’ Identity and Spatial Relations from the begin frame (𝑇𝐹

𝑗),

then supplementing missing elements, Identity Changes, Motions, and Camera dynamics from the A2RD: Agentic Autoregressive Diffusion for Long Video Consistency full segment to form 𝑇𝑗. This decouples frame-level from video-level extraction for A2RD’s pipeline.

• Frames (ℱ𝑗or ℛ). To anchor the concrete visual details that text cannot fully articulate, MVMem stores global reference frames ℛ(both synthesized and user-provided, ℛ𝑢⊆ℛ), each indexed by a generated caption, and segment keyframes ℱ𝑗:= {𝐹begin

}, Indexed By 𝑆𝑗. Our Framework

can accommodate more advanced frame extraction and indexing methods. • Videos (𝑉𝑗). To capture temporal motion dynamics for cross-segment smooth transitions and motion continuity, MVMem saves the synthesized segments for segment verification and refinement. Like keyframes, 𝑉𝑗is simply indexed by 𝑆𝑗.

MVMem enables two core online operations: Retrieve fetches relevant past states and Update writes the newly synthesized ℳ𝑗for subsequent generation, see below. 𝒟is described in Section 3.3. MVMem Initialization.

Before synthesizing segments, inspired by identity-reference approaches (Liu et al., 2026; Zheng et al., 2024) for consistency, A2RD initializes MVMem by establishing global reference backgrounds and entities, ℛ:= ℛbg ∪ℛent ∪ℛ𝑢as a form of long-term memory: (i) Planning. The agent first reasons over 𝒮(and ℛu if available) to identify the environments and entities, their required appearances (both explicitly specified and implicitly implied from 𝒮), and generates prompts for synthesizing these references, using the MLLM:

ℛ∪𝒫u

ℛrepresents identified entities and environments’ prompts, and 𝒫u

ℛ

denotes the captions of user-provided reference frames ℛu. (ii) Identifying Dependencies. The agent constructs a dependency Directed Acyclic Graph 𝒢over 𝒫ℛ: 𝒢:= MLLMdep(𝒫ℛ), to identify which references depend on others (e.g., an entity depends on its environment). 𝒢is then decomposed into weakly connected components. Within each component, topological sorting is applied to determine the synthesis order to respect the dependencies.

(iii) Synthesizing References. The agent synthesizes a reference frame for each prompt in 𝒫ℛ∖𝒫u

ℛ

using the TI2I, conditioned on its dependent references following topological order. All components are synthesized in parallel, yielding ℛ. See Section E.1 for our prompts.

Agentic Auto-Regressive Generation Pipeline

After establishing global references, A2RD synthesizes long videos autoregressively, segment-by- segment. For each segment context 𝑆𝑖, the agent first determines the generation mode, then operates in a Retrieve–Synthesize–Refine–Update closed-loop, where Synthesize–Refine is applied first to boundary frames and then to the video segment. For convenience, we duplicate 𝑆𝑁+1 := 𝑆𝑁in 𝒮:= {𝑆1, . . , 𝑆𝑁+1}, and synthesize segments for 𝑖≤𝑁. See Section E.2 for our prompts.

(i) Adaptive Segment Generation. A key challenge for segment-based generation is balancing narrative progression with consistency. Prior works adopt either extrapolation (An et al., 2026; Zhou et al., 2026) or interpolation (Yin et al., 2023). Extrapolation allows natural video world progression but risks semantic drift, particularly for entities and environments not visible in the begin frame.

Interpolation enforces stronger consistency, but risks unnatural progression especially when TI2I models lack the temporal reasoning to reliably synthesize how environments evolve over a predefined A2RD: Agentic Autoregressive Diffusion for Long Video Consistency duration, given static references (Figure 2). A2RD instead adaptively selects the mode per segment:

Extrapolation

if 𝒞(𝑆𝑖) ∧𝑆𝑖+1 is spatio-temporally continuous with 𝑆𝑖

(2)

where 𝒞(𝑆𝑖) indicates that 𝑆𝑖does not transition to a new established environment, and both conditions are inferred by MLLMmode(𝒮). Interpolation applies when 𝑆𝑖is a multi-shot context whose shots span different environments, or when 𝑆𝑖+1 jumps to a new environment. The second condition is omitted for 𝑖= 𝑁. See Figure 8 for an example of our mode selection.

(ii) Retrieve. After determining the mode, A2RD retrieves text, image, and video contexts for synthesis. For any 𝑗-th segment context, to mitigate false-positive conditioning, the agent employs an MLLM to identify the top-𝑘most narratively relevant previous segments. It acquires the textual states 𝒯rel

Visual References ℱrel

𝑗, and the immediate temporally contiguous segment 𝒱rel

Respectively, Ensuring Subsequent

synthesis for the current segment is conditioned on the begin frame’s context. (iii) Synthesize and Self-Improve–Boundary Frame(s). Based on Equation (2), A2RD synthesizes

𝐹begin

via generating its frame prompt first, and then the frame 𝐹begin

Pgen(𝑆𝑖+1, 𝒯rel

𝑖+1) is the generated frame prompt. The case 𝑡≠𝑖−1 is particularly challenging. It arises when segment 𝑖resumes some events from the middle of a non-adjacent segment 𝑡. To obtain the end frame of the relevant shot in segment 𝑡for resumption, we extract all shot end frames 𝒦𝑡from 𝑉𝑡(Castellano), then the MLLM selects the one that best continues into 𝑆𝑖+1 (Section B.2.3 for an example). All synthesized frames by the TI2I model then undergo a frame-level self-improvement process, described in Section 3.3.

(iv) Synthesize and Self-Improve–Video Segment. After obtaining ℱ𝑖, A2RD synthesizes the segment:

𝑖

, ℱ𝑖) is the video segment prompt. Once synthesized, 𝑉𝑖undergoes a video-level self-improvement process, described in Section 3.3. (v) Update. After self-improvements, MVMem saves (ℱ𝑖, 𝑉𝑖, 𝑇𝑖, 𝑇𝐹

Subsequent Generation, Where 𝑇𝑖and 𝑇𝐹

𝑖+1 are textual states extracted during refinement processes. 3.3. Hierarchical Boundary Frame and Video Self-Improvement To mitigate the risk of cascading temporal errors, where a single inconsistent frame can propagate artifacts across the entire horizon, A2RD introduces HIerarchical Test-time Self-improvement (HITS) to self-improve synthesized frames and video segments hierarchically. Unlike existing works (Liu et al., 2025; Long et al., 2026) that apply search and closed-loop refinements to short clips, A2RD extends this paradigm to self-improve both intra- and inter-segment coherence.

A2RD: Agentic Autoregressive Diffusion for Long Video Consistency Boundary Frame Self-Improvement.

This Step Self-Improves 𝐹*

𝑖(* ∈{begin, end}) interactively. At each iteration, A2RD extracts frame textual states from the synthesized frame: 𝑇𝐹

𝑖, 𝑇𝐹

𝑖+𝛿) is then verified via a 8-metric rubric focusing on consistency and basic image quality on a scale of 1–10 in 3 groups: (i) Entity Consistency, Environment Consistency, Narrative Progression, and Spatial Logicalness; (ii) Entity State, Environment State; (iii) Instruction Following and

𝑖is Input Into The Mllm To Decide The Mode

and, if Edit is chosen, to suggest the edit prompt. The Edit mode targets a single issue only, as it is challenging to fix multiple errors simultaneously. If Regenerate is chosen, 𝑃*

𝑖is Optimized Through

our Memory-Augmented Prompt Optimization (MAPO) algorithm, see below, and 𝐹*

(8)

where ℱcand is the set of candidate frames generated across all refinement iterations. Video Segment Self-Improvement. This step self-improves 𝑉𝑖interactively. Similar to above, A2RD first extracts full video states: 𝑇𝑖:= MLLMvid

𝑖, 𝑉𝑖) (Section 3.1). It Then Verifies (𝑉𝑖,

𝑇𝑖) via a 10-metric rubric focusing on inter-segment consistency, intra-segment consistency, and basic video quality, divided into three groups, each scored on a scale of 1–10: (i) Inter Entity Consistency, Inter Environment Consistency, Inter Motion Consistency, Camera Consistency; (ii) Intra Entity Consistency, Intra Environment Consistency; and (iii) Instruction Following, Physical Plausibility, Narrative Progression, and Frame Fit (only when 𝐹end

𝑖, 𝑆𝑖, 𝑆𝑡, 𝑇𝑖, 𝑇𝑡, 𝑉𝑖, 𝑉𝑡)

⏟ ⏞

(9)

The agent refines 𝑉𝑖depending on the availability of 𝐹end

𝑖

is unavailable, prompt-only optimization is insufficient, as entities

𝑖

or transformed during the segment can drift from references.

From 𝑉𝑖, Self-

improves it following the frame self-improvement process above with Edit mode only (to preserve any natural layout progression), re-optimizes 𝑃𝑖via MAPO conditioned on the updated boundary frames

𝑖

}, and then re-synthesizes 𝑉𝑖for the next iteration. The final 𝑉𝑖is:

(10)

where 𝒱cand is the set of candidate videos generated across refinement iterations. Memory-Augmented Prompt Optimization (MAPO).

To Improve The Refinement Efficacy, We

introduce MAPO, which leverages the history of successful and failed cases indexed by rubric scores. Specifically, MVMem maintains a prompt database 𝒟:= {(𝑃, 𝑃*, 𝒬, ℓ)} where each entry stores an original prompt 𝑃, its refined version 𝑃*, rubric scores 𝒬, and a hard label ℓ∈{pos, neg} indicating positive and negative refinements. Each entry is indexed by a semantic embedding Emb(𝑃, 𝒬) for A2RD: Agentic Autoregressive Diffusion for Long Video Consistency

✓

Table 1 | Comparison of text-to-video generation benchmarks. Existing benchmarks focus on single-scene consistency but lack evaluation of the challenging cyclical state tracking where entities and environments appear, disappear for extended periods, then reappear with non-trivial evolved states.

efficient retrieval. 𝒟is seeded with a few prior cases and updated online: a case is assigned as ‘pos’ if all rubric scores improve, and ‘neg’ if all scores worsen. Given 𝒬𝐹

𝑖/𝑃𝑖, Mapo Retrieves

the top-𝑘relevant positive and negative cases via cosine similarity over (𝒬, 𝑃) embeddings from 𝒟. Inspire by Zhao et al. (2024), MAPO then contrasts the positive and negative cases to derive refinement guidelines, reasons over 𝒬to identify root causes of failures, and applies targeted edits with guidelines to produce 𝑃*. After each refinement, the new case is labeled and added to 𝒟online.

A2Rd-Parallel (A2Rd-Par)

We introduce A2RD-Par, a parallel version of A2RD, for efficiency. A2RD-Par synthesizes both boundary

𝑖

}, ∀𝑖autoregressively and performs the same frame self-improvement process. All video segments are then synthesized in parallel, with no video self-improvement applied. In A2RD, video synthesis latency takes 𝑁𝑘𝑣𝐿𝑉, where 𝑁is the #segments, 𝑘𝑣is the #self-improvement iterations, and 𝐿𝑉is the latency of a single video synthesis call. A2RD-Par removes the sequential dependency, reducing this to 𝐿𝑉under ideal hardware (Section B.5). While this sacrifices some spatial consistency for evolving environments, A2RD-Par still enforces strict character consistency and produces coherent stories for scenes with static environments (Figures 2 and 3 for examples).

Ong Video Bench-Challenge (Lvbench-C)

Existing single- or multi-scene benchmarks neglect real-world scenarios where entities and environ- ments undergo non-linear transitions—appearing, disappearing, and reappearing with optional state changes across scenes (Table 1). We introduce LVbench-C, a benchmark that stress-tests models’ ability to maintain consistent and coherent world states in such scenarios. LVbench-C features three challenge types: (i) Evolving Character States, where characters reappear with evolved states (e.g., clothing, appearance, physical condition); (ii) Evolving Object States, where objects reappear with changed states (e.g., position, orientation, condition); and (iii) Evolving Environment States (e.g., evolution, progressive revelation of details). We instantiate LVbench-C with 120 text-only scenarios: 50 samples each for 3- and 5-minute videos (30 character, 10 object, 10 environment), and 25 samples for 10-minute videos (10 character, 5 object, 5 environment). Each scenario consists of concise scene descriptions that either continue from previous scenes or transition to new ones, forming coherent narratives, see Appx.-Figures 17 to 19 for examples.

Human-In-The-Loop Dataset Construction. We instantiate LVbench-C with 3-, 5-, and 10-minute scenarios using a common time-independent construction pipeline. For each challenge type, we first carefully craft a professional screenwriter persona prompt, incorporating one human-designed demonstration and generate scenarios using a state-of-the-art MLLM (Google DeepMind, 2025).

A2RD: Agentic Autoregressive Diffusion for Long Video Consistency This generation is enforced with constraints: (i) Content, where story flow must be meaningful and natural, each segment fits a pre-defined duration, clear cause-and-effect relationships, no random events; (ii) Gap Rules, where main entities and environments must be absent for at least 𝑛= 10 segments before reappearing with or without state changes; and (iii) State Change, ensuring natural state changes through realistic activities, specific visual markers (positions, appearances, conditions).

During generation, we manually review generated scenarios and update constraints or demonstrations to improve diversity. Data Refinement and Deduplication.

Since raw MLLM-generated scenarios often contain dupli- cates, logical gaps, and low-quality content, we apply systematic refinement. The generated scenarios are deduplicated using the same MLLM: we summarize these scenarios one by one, and feed sum- maries into the MLLM to identify and remove similar scenarios. We then customize Self-Refine (Madaan et al., 2023) for self-refinement: selected scenarios undergo rigorous MLLM-Judge vali- dation against six criteria: (i) Specificity Verification ensuring each scene must be specific enough with clear revelations; (ii) Logic Verification where each scene reveals new details not mentioned before that must have logically existed from beginning; (iii) Natural Verification where entity actions must be natural without forced or contrived scenarios; (iv) Realism Verification ensuring details must be realistic and appropriate with everyday believable activities; (v) Repetition Verification ensuring activities and details must be varied without repetitive content; and (vi) Contradiction Verification with no contradictions across segments regarding entity and environment states. Failed scenarios in any criterion undergo refinement until successful or up to a limited number of iterations. To avoid self-preference bias, a separate MLLM (Anthropic, 2025) re-verifies all scenarios using the same criteria, and refines any minor issues. We manually review a subset of samples to confirm quality.

Settings

Benchmarks. We conduct experiments on both single-scene and multi-scene long video generation. For single-scene, following Yang et al. (2026a); Yi et al. (2025), we use VBench-Long (Huang et al., 2025c) with 40 prompts, each decomposed into 8 continuous segments using Gemini (Google DeepMind, 2025), yielding approximately 1-min videos. For multi-scene, we use our LVbench-C benchmark featuring challenging environment transitions and entity evolutions across 3-min videos (24 scenes) and 5-min videos (40 scenes), with 20 samples each, and 10-min videos in Section 6.4.

Models and Baselines. We instantiate A2RD and baselines with Gemini 3 Flash (Google Deep- Mind, 2025) as the MLLM, Nano Banana 2 (Raisinghani, 2026) as the TI2I model, and Veo 3.1 (Google Deepmind, 2025) as the TI2V model. We compare A2RD against SOTA autoregressive and parallel segment-based baselines: (i) Direct Prompting, which generates each video segment directly from its scene description without any conditioning; (ii) Naive Autoregressive (Naive-AR), which generates each video segment via extrapolation, conditioned only on the last frame of the previous segment; (iii) Naive-Par, which autoregressively synthesizes the end frame of each segment

, . . . , 𝐹end

𝑖−1) and subsequently uses the begin and end frames to interpo- late the video segments in parallel; (iv) MovieAgent (Wu et al., 2025b), a hierarchical multi-agent framework that automates long-form movie generation; (v) ViMax (HKUDS, 2025), a multi-agent framework that automates end-to-end long-form video generation through script planning, story- boarding, character design, and reference-guided shot synthesis; and (vi) VideoMemory (Zhou et al., 2026), which synthesizes long video autoregressively via maintaining a dynnamic memory bank of A2RD: Agentic Autoregressive Diffusion for Long Video Consistency

Ar

Table 2 | Experiments on VBench-Long (Huang et al., 2025c) expanded to 8 continuous scenes for 1-min videos. entity references for visual consistency. Implementation Details.

We run A2RD with two iterations for frame and two for video refinements. At each iteration, we synthesize three frames and three videos via batch inference, run all the judges in Equations (7) and (9) in parallel and the best one will be selected for next refinement iteration. We implement early stopping when average frame/video scores ≥9/10. In total, a video segment requires at most 6 videos and 6 images. For efficiency, we pre-compute Equations (2) and (3) and Equation (4) (MLLMrel-imgs) over all segments at once, see All Scenes prompts in Section E.2. For scaling to longer horizons, we limit each MVMem schema to available hardwares, and cap the maximum window size at 100 for operations involving 𝒮and image lists as MLLM inputs (e.g., Equations (1) to (3)).

Automatic Evaluations

Automatic Metrics. We evaluate generated videos across nine metrics: (i) Semantic Alignment measuring ViCLIP-based (Wang et al., 2024b) text-video similarity between each video segment and its description; (ii) Narrative Coherence following Wang et al. (2025a), where we employ Gemini 3 Pro (Google DeepMind, 2026) to score story, entity and environment progression, and causal logic on a scale of 0–1 with Self-Consistency (Wang et al., 2023), with penalties for repetitive or incoherent content. For inter consistency, we measure (iii) Character Consistency, (iv) Environment Consistency following An et al. (2026); Meng et al. (2026), and (v) Motion Consistency customized from VBench (Huang et al., 2024) for contiguous segments. For intra consistency, we report (vi) Subject Consistency, (vii) Environment Consistency from VBench. We assess general video quality in Section B.1, and provide implementation details in Section C and qualitative analysis in Section B.2.

Single-Scene Results. Table 2 presents results on 1-min generation on VBench-Long. A2RD achieves substantial improvements across all metrics in both narrative quality and consistency. For narrative coherence, existing segment-based methods perform poorly (0.69 for ViMax, 0.67 for VideoMemory), as they force shot changes at every segment and produce repetitive or inconsistent environments (Figure 2, Section F). Meanwhile, A2RD reaches 0.9, outperforming the best baseline (Naive-AR at 0.75) by 20%. For consistency, A2RD attains significant gains in both characters (0.74 vs. 0.57/0.56, a 30% improvement) and environments (0.84 vs. VideoMemory’s 0.73). Remarkably, it obtains a motion consistency of 0.9935, substantially surpassing baselines and indicating that transitions between consecutive segments are nearly as smooth as a single diffusion generation. A2RD-Par also performs competitively with 0.81 narratives while enabling parallel generation, offering a practical efficiency- consistency trade-off. Both A2RD versions achieve the highest semantic alignment, suggesting that they better satisfy user prompts than baselines.

Ar

Table 3 | Experiments on LVbench-C (Ours) for multi-scene 3-min and 5-min video synthesis.

A2Rd (Ours)

Table 4 | Human evaluation results on 40 VBench-Long samples (1–5 scale). Multi-Scene Results. Table 3 reveals the critical challenge of long-horizon consistency, with all baselines degrading notably compared to Table 2. In both 3-min and 5-min settings, baseline character consistency reaches only up to 0.38, while environment peaks at 0.40. Baselines also show significantly lower semantic alignment at 5-min than at 3-min or 1-min, highlighting the difficulty of maintaining prompt fidelity over extended horizons. A2RD achieves superior consistency, outperforming VideoMemory by 16% on average at 3-min and 13% at 5-min. It also produces notably more coherent narratives, scoring 10% higher than VideoMemory while maintaining a motion smoothness of above 0.99. Additionally, baseline narrative scores are higher on LVbench-C than VBench-Long. This is because LVbench-C focuses on multi-scene scenarios which better suit baselines that enforce regular shot transitions.

Scaling Baselines. For fair comparisons, Figure 6 experiments with best-of-N (Section C.1.1) scaling autoregressive baselines to match the same #videos and frames sampled per segment as A2RD on VBench-Long, where the best-of-N video is selected by Gemini 3 Pro. We find that consistency indeed improves remarkably for all baselines: Naive-AR improves from 0.61 →0.67 avg. consistency, while VideoMemory improves from 0.65 →0.71. However, narrative coherence is not always the case, with Naive-AR decreases. Meanwhile, A2RD shows significantly more promising test-time scaling potential thanks to its multi-dimensional judges, which can more reliably distinguish quality among candidates, improving from 0.73 →0.78 for avg. consistency and 0.74 →0.9 for coherence.

Human Evaluations

Human Metrics. To understand user satisfactions, we conduct a human study on 40 VBench-Long single-scene samples (approx. 40-min per baseline), following a similar scale to Yu et al. (2025b).

We recruit 7 highly qualified evaluators, each presented with a random subset of generated videos from all methods in randomized, anonymized order. Evaluators rate each video on six criteria on a 5-point Likert scale (1: very poor, 3: acceptable, 5: excellent): (i) Character Consistency and (ii) Object Consistency (whether characters and objects maintain consistent appearance across shots), (iii) Environment Consistency (whether environments and environments remain coherent across scene transitions), (iv) Transition Smoothness (whether cuts between segments are visually and temporally A2RD: Agentic Autoregressive Diffusion for Long Video Consistency

A2Rd Always Interpolates

Figure 5 | Ablation studies over A2RD’s MVMem, TTS algorithms, and adaptive segment generation strategies on Vbench-Long.

Figure 6 | Consistency Versus

scaling #videos per segment. natural), (v) Narrative Coherence (whether the story progresses logically with meaningful causal relationships), and (vi) Reference Consistency (how faithfully the generated video adheres to the provided reference images). Each sample is rated by at least 2 evaluators and scores are averaged.

Human Results. Table 4 presents the human results, corroborating our automatic metrics. A2RD achieves the highest scores across all six criteria with an average of 4.68 over 5.00, substantially outperforming the best baseline, VideoMemory (3.93). It excels in character consistency (4.89) and narrative coherence (4.75), confirming strong identity preservation with strong story progression in 1-min. It also scores 4.34 in transition smoothness, significantly higher than the best baseline of 3.34. Reference consistency reaches 4.91, showing potentially strong alignment when users provide reference images. While A2RD-Par maintains good character consistency, it shows notable drops in environment consistency and transition smoothness due to parallel generation from predefined frames.

This confirms the benefits of autoregressive generation for both visual and temporal coherence.

Analysis

We present our main analyses in this section while additional analyses including qualitative, test-time scaling and latency analyses are provided in Section B.

Ablation Studies

Ablation Setups. We conduct ablation studies on VBench-Long to assess each A2RD component’s contribution across three groups: (i) MVMem’s components including the complete MVMem system, its Textual States, and its Videos; (ii) Test-time scaling components including global references, HITS, and MAPO (replaced by Self-Refine (Madaan et al., 2023)); and (iii) adaptive segment generation strategy when we instead always extrapolate, or interpolate. We omit components that have been comprehensively studied in prior work, such as MVMem’s Frames.

Ablation Results. Figure 5 shows the critical role of each component. First, MVMem is the backbone of A2RD; removing it severely degrades performance to near Naive-AR levels, as the system loses long-range dependency conditioning, consistency validation, and HITS. Ablating MVMem’s individual modalities reveals their contributions: without Textual States, narrative and consistency drop notably, while removing Videos causes less impact, as they are primarily for motion continuity. Second, test- time scaling components are also critical: we find that removing HITS causes considerable drops (0.90 →0.74 for narratives, 0.74 →0.68 for characters), while without MAPO, the prompt refinements A2RD: Agentic Autoregressive Diffusion for Long Video Consistency are less effective. Interestingly, we see that removing global references minimally affects narrative and character consistency but notably drops environment consistency (0.84 →0.79), suggesting that environments are harder to maintain and benefit from global references. Finally, the adaptive mechanism is also crucial: always extrapolating maintains reasonable narrative coherence (0.83) but reduces consistency. Meanwhile, always interpolating achieves the highest consistency but at the cost of reduced narrative coherence. In this case, each frame is conditioned on richer context from previous segments, and HITS (Video) further enforce intra-shot consistency, which together can over-constrain the generation and tend to produce limited visual progression.

6.2. Generalization to Other Video Diffusion Models

A2Rd (Ours)

Table 5 | Experimental results with two open-source TI2V models. Setups. To study whether A2RD generalizes across diffusion backbones, we evaluate it with two strong open-source TI2V models: LTX-Video 0.9.8 (13B) (HaCohen et al., 2024) and Wan 2.2 (5B) (Wan et al., 2025a). For both models, we use 30 denoising steps with resolution 704 × 480 and follow the same evaluation protocol on VBench-Long in Section 5. Since Wan 2.2 does not support interpolation, we run all A2RD experiments with this backbone using the always-extrapolate mode.

Results. As shown in Table 5, A2RD improves over Naive-AR across both models. On LTX-Video, it yields notable gains in narrative coherence (0.59 →0.79) and character consistency (0.50 → 0.70). Wan 2.2 follows a similar trend, with narrative coherence rising from 0.67 →0.80, character consistency from 0.51 →0.69, and environment consistency from 0.59 →0.78. These results confirm that A2RD generalizes across different video diffusion backbones without costly retraining.

Onsistency Analysis Over Extended Horizons

Setups. We analyze how consistency degrades as generation extends over longer horizons. Since the automatic metrics in Section 5.2 cannot fully capture the evolving states of entities and environments, we develop an MLLM-Judge method to evaluate consistency in these evolving scenarios. Specifically, after grouping segments that share the same characters, objects, or environments for automatic metrics as in Section C.1, a carefully calibrated MLLM-Judge with a state-of-the-art MLLM (Google DeepMind, 2026) (Section E.5 for prompts) evaluates each dimension 𝑑∈{char, obj, env} per segment through 3 steps: (i) Identify Visual Differences by comparing with relevant segments’ frames and references; (ii) Classify Inconsistencies as expected (justified by the story) or unexplained; and (iii) Flag Inconsistencies that are unexplained and obvious. For each sample 𝑖-th, with the same notation introduced in Section 3 and 1 being the indicator function, the 𝑑’s consistency ratio is:

A2Rd (Ours; 2 Refinements)

Figure 7 | Consistency scores as a function of scene window size (1–40). All methods are evaluated on LVbench-C, 5-minute using LLM-Judge. A2RD (Ours) consistently maintains higher consistency over extended horizons compared to baselines.

Results. Figure 7 reveals interesting insights. For baselines, we see that they exhibit a monotonic decline in consistency as #scenes grows, confirming that consistency in long horizons is a non- trivial challenge. For characters, Naive-Par performs worse than VideoMemory and ViMax, which is most challenging dimension overall. Interestingly, Naive-Par performs much better than ViMax and VideoMemory here; we attribute this to these baselines forcing frequent shot changes, which causes more often environment hallucinations. Finally, all baselines perform similarly on object consistency, the least challenging dimension, likely because objects do not exhibit complex identities compared to characters and environments. Our method, A2RD, outperforms baselines across all three axes by large margins: it retains 96.7% character consistency, 91.8% environment consistency, and 95.0% object consistency. These results further validate the superiority of A2RD architecture in combating long-horizon consistency degradation.

We manually verified a subset of the MLLM-Judge outputs and found its flags to be effective (80% agreement) for detecting obvious errors. Figure 16 shows representatives. We want to note that subtle inconsistencies, which are very common in synthesized frames, may be missed—by design, as it is calibrated to flag only clear violations. Nevertheless, the method effectively captures the overall trend that A2RD significantly outperforms baselines, aligning with other automatic and human evaluations.

Scaling To Longer-Horizon Video Generation

We experiment with A2RD on ten 10-min scenarios from LVbench-C, approaching the frontier of current long-form video generation. By using the MLLM-Judge method developed above, on average, A2RD attains average consistency of 90.5% for characters, 84.0% for environments, and 91.5% for objects, confirming its strong capability in generating coherent ultra-long videos.

Onclusions

We presented A2RD, an agentic autoregressive architecture for long video synthesis. By decoupling creative synthesis from consistency, A2RD addresses two fundamental challenges: temporal con- sistency and narrative coherence. This is achieved through three key components: a multimodal video memory for cross-modal context tracking; an adaptive generation mechanism enabling natu- ral narrative progression with consistency enforcement; and hierarchical test-time self-improving A2RD: Agentic Autoregressive Diffusion for Long Video Consistency algorithms that self-refine frames and segments to prevent error propagation. We further introduced LVbench-C, a benchmark designed to stress-test long-horizon consistency via cyclical appearance with optional state evolutions. Experiments across 1-10 minute videos show that A2RD sets a new state-of-the-art for narrative coherence and visual consistency compared to existing baselines.

Imitations

We acknowledge that A2RD incurs more computational overhead than the baselines experimented in Section 5. However, since all baselines follow the passive generation paradigm, direct comparison is therefore the meaningful way to evaluate their capabilities. To ensure fairness, we also provide compute-matched variants of all autoregressive baselines in the Scaling Baselines paragraph. In addition, it is worth noting that A2RD’s active reasoning incurs insignificant costs. Our analysis following Section B.5 reveals that its cost overhead with using Gemini 3 Flash can be estimated only about less than 0.5$ per segment (with less than 10K tokens per call on average; excluding costs associated with generating 5 additional videos and 5 additional frames per segment).

In addition, we did not report human agreement scores in our study. This is because several videos are rated by exactly two reviewers, making the agreement not very informative. Additionally, evaluating long-form video generation is inherently subjective, particularly for complex criteria such as transition smoothness and narrative coherence. We decided to average scores over raters, as they are all highly qualified.

Finally, while A2RD made solid progress in long-form video synthesis, several limitations remain. It requires component models (MLLM, TI2I, TI2V models) with strong instruction-following and visual reasoning capabilities. Additionally, the verification rubrics reflect implicit assumptions about consistency and quality that may not generalize to diverse creative styles, cultural contexts, or domain- specific preferences. We encourage adapting them for domain-specific settings. Finally, our user study reveals that transition smoothness and physical environment consistency remain the most challenging aspects for segment-based methods, presenting promising directions for future work.

We thank Jinsung Yoon, Bhavana Dalvi Mishra, and our colleagues at Google Cloud AI Research for their helpful feedback. We also want to thank Xingchen Wan, Sercan Ö. Arık for several useful discussions, and Nancy F. Chen and Kenji Kawaguchi for supporting Do Xuan Long’s internship.

Authors:

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

94143, Usa.

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

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

14Jlvmi Consulting Llc, Dousman, Wi, Usa

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

Abstract

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

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

Introduction

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

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

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

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

●

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

●

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

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

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

Hyperpolarized 13C-Pyruvate Preparation

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

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

General Considerations

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

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

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

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

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

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

Personnel

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

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

Equipment And Facility

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

Material Handling

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

Pharmacy Kit Filling And Assembling

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

Quality Control And Dose Release

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

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

The Final Dose Release And Injection

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

Some Key Challenges

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

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

Current Practices

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

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

In House

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

Summary

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

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

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

Mri System Setup And Calibrations

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

Imaging System

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

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

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

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

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

Rf Coils

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

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

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

Provide B1 Transmit Across The Fov (B1

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

B1

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

+ Profile But Has Been Used Because Of

relatively easy integration into the scanner bore. B1

+ Variation Results In Variations In The Flip

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

Homogeneous B1

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

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

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

(1)

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

Tx = Transmit

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

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

Phantoms

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

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

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

+) And Receive (B1

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

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

Prescan Calibration

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

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

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

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

+ Inhomogeneity As Well

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

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

Power [Kw]

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

8

13C-bicarbonate doped with dimethyl silicone, various

Power [Kw]

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

Maximum Values

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

Summary

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

+ Profiles. The

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

For Calibration Of B1

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

Acquisition And Reconstruction

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

+ Inhomogeneity,

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

Acquisition And Reconstruction Methods

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

Mrs/I Methods Specifically

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

Chemical Shift

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

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

Their Application To Different

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

The Majority Of

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

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

Prostate Studies

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

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

Heart Studies

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

Brain Studies

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

Abdomen And Breast Studies

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

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

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

1H Imaging

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

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

Reported Study Parameters

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

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

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

(B)

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

Summary

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

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

Data Analysis And Quantification

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

Metrics

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

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

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

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

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

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

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

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

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

Visualization

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

Metrics

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

Parameter Encoding

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

Anatomical Context

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

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