Digimat Va Virtual Allowables

For Visual Analytics System Design

Leonardo Ferreira, Gustavo Moreira, and Fabio Miranda

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Fig. 1: VA-Blueprint provides a structured way to uncover and organize the fundamental building blocks used in visual analytics components and their relationships are systematically extracted and structured into a formal, hierarchical blueprint (represented as JSON), detailing the system’s components and their dependencies. (c) This blueprint categorizes components across multiple levels of abstraction: high-level stages (e.g., Data Processing), intermediate groups (e.g., Rasterization), and specific granular blocks (e.g., Sky Computation). (d) The final knowledge base comprises 101 machine-readable blueprints encoding component hierarchies, and data and interaction dependencies.

Abstract— Designing and building visual analytics (VA) systems is a complex, iterative process that requires the seamless integration of data processing, analytics capabilities, and visualization techniques. While prior research has extensively examined the social and collaborative aspects of VA system authoring, the practical challenges of developing these systems remain underexplored. As a result, despite the growing number of VA systems, there are only a few structured knowledge bases to guide their design and development.

To tackle this gap, we propose VA-Blueprint, a methodology and knowledge base that systematically reviews and categorizes the fundamental building blocks of urban VA systems, a domain particularly rich and representative due to its intricate data and unique problem sets. Applying this methodology to an initial set of 20 systems, we identify and organize their core components into a multi-level structure, forming an initial knowledge base with a structured blueprint for VA system development. To scale this effort, we leverage a large language model to automate the extraction of these components for other 81 papers (completing a corpus of 101 papers), assessing its effectiveness in scaling knowledge base construction. We evaluate our method through interviews with experts and a quantitative analysis of annotation metrics. Our contributions provide a deeper understanding of VA systems’ composition and establish a practical foundation to support more structured, reproducible, and efficient system development. VA-Blueprint is available at urbantk.org/va-blueprint.

Index Terms—Visual analytics, large language models, knowledge base, system development, urban visual analytics.

Ntroduction

Visual analytics (VA) systems help users make sense of complex data by combining analytics with interactive visualizations. Their usefulness has been demonstrated across diverse domains, including biology , healthcare , urban planning , and climate science . How- ever, building VA systems remains a highly complex and iterative process. System builders must integrate data processing techniques, analytics capabilities, and visualization strategies while ensuring inter- pretability for domain experts and maintaining efficiency to support interactivity. Despite their significance and inherent challenges, VA • Leonardo Ferreira, Gustavo Moreira, and Fabio Miranda are with the xx xxx. 201x; date of current version xx xxx. 201x. For information on Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx system development is often approached in an ad-hoc manner, with minimal reuse of existing components. Given the necessity to address the unique analytical and visualization needs of specific domains, these systems are usually bespoke solutions, leading to fragmented develop- ment efforts, making them difficult to extend, adapt, or reuse across projects. This tension, between the need for tailored solutions and the demand for scalable, extensible systems, presents a fundamental challenge in VA system development . On one hand, VA sys- tems must be grounded by domain-specific data, tasks, and analytical workflows: a visualization technique that works well for transportation may be inadequate for urban planning, just as an analytical model for climate science may not be applicable to public health. On the other hand, as VA systems become increasingly complex, the lack of system- atic and reusable components hampers the design process and increases the effort needed for development.

Currently, there is a lack of practical knowledge bases that document taxonomies for visualization tasks and techniques . More recently, new initiatives have emerged to catalog and structure design elements

Arxiv:2508.07497V1 [Cs.Hc] 10 Aug 2025

derived from system surveys . However, these contributions often focus on specific aspects (primarily visualization) or lack the granular, interconnected structure needed to capture the full dataflow and com- component-level blueprint encompassing data processing, analytics, vi- sualization, and interaction is still missing. Without such a foundation, developers are forced to either create highly specialized systems from scratch or repurpose existing solutions that may not fully meet their needs. The status quo is a landscape where many VA systems remain one-off, non-scalable, and difficult to extend, limiting broader adoption and cross-domain innovation. As VA systems become more widespread in different domains and increasingly applied in decision-making sce- narios , there is a growing need to bridge this gap. Fundamentally, how can we design VA systems that remain useful for specific datasets, tasks, and users while also being extensible and reusable? In this paper, we take a step towards answering this question by proposing a structured methodology to identify, categorize, and orga- nize VA system components. Given the broad and diverse applications span multiple domains, including transportation, urban planning, and environmental science. Through a systematic process, we analyze ex- isting urban VA systems, extracting and classifying their core building blocks. Using an initial sample of 20 systems, we create VA-Blueprint (Figure 1), a multi-level knowledge base that provides a structured representation of these components. Then, to scale this effort, we in- vestigate the potential of a large language model (LLM) to automate the extraction of VA system components from a corpus of 101 research papers, building on demonstrated LLM capabilities for related tasks such as aspect-based summarization . We assess LLM’s ability to identify and categorize these components. Through this approach, we move towards addressing the challenge of VA system development with a structured methodology that balances the need for bespoke, domain-specific solutions with the benefits of extensible and reusable VA patterns that supports VA system development in three key ways: (1) enabling pattern reuse, allowing developers to identify and draw inspiration from proven configurations instead of assembling ad-hoc pipelines; (2) laying the foundation for model-driven development in urban VA; and (3) ultimately guiding developers in building more effective urban VA systems by highlighting common, effective architec- tural structures. The knowledge base and associated tools are publicly available at urbantk.org/va-blueprint.

Related Work

In this section, we review prior work in two key areas of relevance to this paper. First, we review methodologies that support the design of VA systems, as well as how system components, design patterns, and best practices are documented and shared. Then, we review prior work on the use of LLMs for extracting information from literature.

Ethodologies & Knowledge Sharing For Va Systems

The design of VA systems is deeply rooted in a close collaboration between visualization researchers, practitioners, and domain experts. While the nature of this collaboration varies in structure and inten- sity , it usually involves visualization researchers contributing tech- nical expertise to elicit system requirements and develop visualizations, interactions, and analytical components, while domain experts provide problem definitions, tasks, and data. This process is often guided by human-centered methodologies that structure the design workflow , offering guidelines for key stages such as problem characterization and visualization development . However, recent studies have shed light on the tensions within this process, both from a social and a technological perspective. From a social perspective, Akbaba et al.

discussed challenges in visualization collaborations, emphasizing the need for stakeholders to recognize benefits beyond the tool itself. Wu et al. reported a series of criticisms aimed at VA systems, including growing concerns regarding the generalizability of contributions, a concern echoed by several works [35, 55, 58]. From a technological perspective, Isenberg recently highlighted reproducibility challenges in visualization research . Chen and Ebert discussed the many challenges of designing VA systems, pointing to the trial-and-error nature of VA system design , an often iterative and unpredictable process. At the intersection of these concerns, Wu et al. argued for the augmentation of traditional HCI-grounded design study methodolo- gies with software engineering perspectives to enable more systematic has shown that VA system design is a complex and iterative endeavor, often relying on the experience and intuition of researchers .

To mitigate these challenges and lower the barriers to design and development, several studies have proposed toolkits and knowledge these efforts aim either to operationalize design spaces (streamlining system construction from a practical perspective) or to formalize tax- onomies of components from a theoretical perspective . These goals align with the broader challenge of moving away from monolithic systems towards more modular ones . From a practical perspective, toolkits and frameworks have played a key role in facilitating design by providing components that simplify system development and enable rapid prototyping of ideas, while reducing the need for low-level cod- ing . Vega-Lite and Draco exemplify how declarative specifications can simplify visualization construction and embed best practices. Similar toolkits and frameworks have been proposed taking into account specific contexts and domains, such as virtual reality , uncertainty visualization , urban analytics , and genomics .

From a theoretical perspective, taxonomies offer a vocabulary that can bridge communication gaps and reduce misunderstandings . While taxonomies have been widely adopted in visualization research in gen- eral , relatively few attempts have been made to create taxonomies specifically for VA systems (e.g., ). A notable exam- ple is the work by Ying et al. , where they presented a knowledge base is made available through an indexing scheme composed of task and design, based on the multi-level typology of visualization tasks , facilitating the ideation process through the exploration of previous VA designs. However, despite their potential to enhance knowledge sharing, reproducibility, and modularity, taxonomies in VA and their practical software-oriented implementations remain underdeveloped as they rely on manual curation and expert-driven classification. Constructing and maintaining these taxonomies requires substantial effort.

Our work moves beyond these approaches by exploring a semi- automated, bottom-up method for extracting VA system components directly from research papers. Given that many systems are not publicly available, this approach enables systematic identification and classifica- tion of components based solely on the literature.

Automated Approaches For Knowledge Extraction

The visualization community has long explored automated ways to extract and organize information from scientific literature and reposito- ries, given the labor-intensive process of manually curating knowledge bases. Li et al. proposed a method to retrieve visualizations taking into account their perceptual similarities. Poco and Heer leveraged machine learning to extract visual encoding specifications from images.

Hoque and Agrawala presented a search engine based on over 7,000 D3 visualizations crawled from the web. Li et al. introduced a novel image-based representation designed to encode information from biomedical papers and support more effective indexing. Other works have proposed extracting information and insights from static charts to support the generation of animated visualizations , or to derive insights and descriptive text .

Concurrently, LLMs have been shown to effectively extract infor- mation from textual data, enabling applications such as data summa- rization and report generation . Zhang et al. and Pu et al.

highlighted the disruptive change brought forward by LLMs for sum- marization and their capabilities even for zero-shot summarization. A similar conclusion is reached by Agarwal et al. but for the automatic generation of literature reviews. In the visualization community, Tang et al. proposed to use intermediate workspaces to steer the sum- marization of documents, including literature reviews. Despite these advancements, most existing approaches have concentrated on isolated chart analysis or textual summarization. In contrast, the systematic extraction of VA system components, particularly using LLMs, remains an underexplored direction. Rather than focusing solely on retrieving visualization elements or summarizing papers, our approach seeks to identify the analytical, interactive, and visualization components that we construct a knowledge base that captures the modular building

Building A Knowledge Base For Va Systems

Building VA systems is a complex process that involves integrating data processing, analytical techniques, and interactive visualizations. However, despite the growing number of proposed systems, their un- derlying components remain poorly documented and dispersed across the literature. In the absence of a structured approach to capture and organize this knowledge, designing new VA systems often requires starting from scratch or relying on researchers’ intuition. To tackle this gap, we propose VA-Blueprint. VA-Blueprint is a knowledge base be framed from three different perspectives, each highlighting a distinct aspect of our contributions. Together, these perspectives help articulate the core research questions that guide our study.

The need for systematic knowledge extraction. There is no structured repository that captures the fundamental components of VA systems, despite their increasing complexity. This increases over- reliance on researchers’ experience. And while taxonomies exist for specific visualization techniques, how can we systematically extract and categorize VA system components from research papers? The scalability challenges in VA knowledge organization. Tradi- tional taxonomy creation relies on researchers’ expertise and manual effort, which does not scale as new systems emerge. Then, how effec- tive are automated methods in identifying VA system components, and

What Are Their Limitations In This Context?

Validating extracted VA components. Equally important to ex- tracting VA components is ensuring that they align with expert knowl- edge and can be validated for accuracy. So what validation methods

Can Be Used To Assess Accuracy?

Next, we detail our methodology, which is guided by these re- search questions. Specifically, we describe: the requirements that VA-Blueprint must fulfill (Section 3.1); our methodology (Section 3.2); the approach for curating a corpus of VA system papers (Section 3.3); the process of structuring VA systems (Section 3.4); and the use of LLMs to extract components (Section 3.5).

Knowledge Base Requirements

Our knowledge base must fulfill several key requirements. These requirements were derived from our review of the literature, two prior surveys on domain-specific VA systems , and our experience as system builders. Given that research papers are often the only concrete artifacts documenting VA system designs, our knowledge base must be structured in a way that enables the extraction and organization of components from them. We define the following requirements: [R1] Granularity of components. The knowledge base must represent VA system components at multiple levels of abstraction, distinguish- ing between high-level functional categories (e.g., data processing, analytics, visualization) and low-level implementations (e.g., specific algorithms, techniques, libraries).

[R2] Categorization of components. The extracted components must be organized based on their role within a VA system. For example, components must be categorized with respect to their role in data operation, analytics, interaction, and visualization.

[R3] Connection between components. Within a VA system, compo- nents are interconnected, not isolated. The knowledge base must then capture their dependencies and relationships.

[R4] Contextual information. Each component should include meta- data containing detailed information such as its definition, usage sce- narios (when available), source references, and related components.

[R5] Queryable structure. Entries in the knowledge base must be queryable, enabling users to explore system components based on different criteria (e.g., component type, functionality).

Output

101 Systems (2500+ Components & 2400+ Dependencies)

Remaining 81 Papers

Fig. 2: VA-Blueprint knowledge base construction process overview. Foundation comprises curating the paper corpus (101 papers). In Structuring, manual analysis of an initial set of papers (20) establishes core components and informs the formal JSON schema. Scaling uses LLM extraction for the remaining papers (81), guided by the schema and refined in a human-in-the-loop review cycle. The result is the final

Ethodology Overview

Our methodology comprises a set of stages, as illustrated in Figure 2. In the Foundation stage, we collect a set of VA papers (Section 3.3). This is followed by an iterative, human-in-the-loop process to construct the knowledge base. This process cycles between: (1) a Structuring stage, where we manually analyze an initial set of papers to establish a formal schema and a core set of components (Section 3.4), and (2) a Scaling stage, where we leverage an LLM to automate the extraction for the remaining corpus (Section 3.5).

Foundation: Corpus Curation

In this paper, we focus on a specific area of VA: systems designed for urban analyses. This decision is motivated by two factors. First, urban VA systems are highly diverse in terms of data sources and system components, requiring the integration of data processing, analytics, and visualizations to handle often large, heterogeneous, and multi-scale datasets. The complexity of these systems makes them representative examples of VA architectures. Second, urban VA serves a broad range of users and domain experts, including urban planners, transportation specialists, engineers, public health experts, and climate scientists.

These systems must then accommodate diverse workflows, making them a compelling domain for studying how VA components are struc- tured and interconnected. Such delineation of our corpus allows us to extract a diverse set of VA systems while maintaining a cohesive scope that facilitates meaningful comparisons and categorization.

We establish the following criteria for paper selection: (1) papers must describe a VA system that integrates data processing, analytics, and visualization components, rather than focusing solely on visualiza- tion techniques; (2) the system must be applied to an urban analytics problem; (3) the paper must have been published in a top-tier visual- C&G). For an initial selection of papers, we leveraged our previous surveys on urban VA , which provided an initial pool of over 85 papers. These prior surveys examined the landscape of urban VA our corpus, ending with 101 papers. We tried to balance the papers based on their areas of application (e.g., transportation, planning). This set of 101 papers will serve as the basis for our extraction methodol- ogy. Figure 2 shows an overview of the knowledge base construction process. Our corpus spans multiple areas (e.g., transportation, climate, accessibility), under the umbrella of urban analytics. The research labs contributing to this corpus are also well distributed, with contributions from institutions based in the Americas, Europe, and Asia.

Papertitle: String; // Paper Title

HighLevelBlocks: HighLevelBlock[]; // List of high-level blocks

Highlevelblock {

HighLevelBlockName: string; // High-level block’s name IntermediateBlocks: IntermediateBlock[]; // List of intermediate blocks

Ntermediateblock {

IntermediateBlockName: string; // Intermediate block’s name GranularBlocks: GranularBlock[]; // List of granular blocks

Granularblock {

GranularBlockName: string; // Granular block’s name

: Integer; // Unique Block Identifier

PaperDescription: string; // Component’s summarized description Inputs: string[]; // Data/signals consumed by the block Outputs: string[]; // Data/signals produced by the block ReferenceCitation: string; // Quote from the paper

Structuring: Schema And Manual Analysis

Building on the foundational corpus and blueprint (Section 3.3), the Structuring stage translates the conceptual model into a practical, scal- able format. This stage involved (1) developing the formal multi-level representation and its JSON encoding, and (2) performing manual analysis of selected papers to instantiate and validate this structure.

A Specification Schema

We model VA systems as multi-level dataflows composed of intercon- nected components and operations, with explicit data and interaction dependencies. This formal model both guides our understanding of VA system architectures and defines the structure of our specification schema, which is used for automated extraction and validation.

As

highlighted in previous works , VA systems generally consist of three core building blocks: data processing, analytics, and visualization. These systems operate as layered computational workflows, where data undergoes a series of processing and analytics steps until visu- alization. While VA systems can be described using UML diagrams, architecture diagrams, or formal specifications, we adopt a dataflow representation as it more naturally captures how data is transformed and propagated across system components . Most VA systems, even if they do not explicitly expose a dataflow to users [11, 39, 65], implicitly follow this structure through function calls, API interactions, or event-driven communication between components. The concept of data being transformed through a series of operators was also discussed as the operator pattern in Heer and Agrawala’s design patterns for visualization software . This pattern has been extensively used for intra-tool communication, reinforcing its relevance as a guiding model for our extraction of VA system components .

Referring back to our goal of incorporating multiple levels of ab- straction, we model VA system components across three hierarchical levels: system-level, component-level, and operation-level representa- tions. This model directly defines the structure of our machine-readable specification schema, implemented as a hierarchical JSON format. The specification schema serves three roles: (1) structuring the LLM’s output by providing a template for extraction, (2) ensuring alignment with our formal model, and (3) enabling parsing and validation for downstream analysis and visualization. The VA-Blueprint Schema illustrates the core hierarchical structure and key fields defined in this specification. As a running example, we will use the description of an urban VA system composed of a map, scatter plot, and bar chart that visualizes topological features extracted from spatiotemporal data.

System-level representation. At the highest level, we define that a VA system can be represented as a directed graph S = (C ,D), where C is the set of components of the system, and D represents dependencies between components. Each dependency in D represents either a data dependency (i.e., data transfer between components or operations) or an interaction dependency (i.e., constraints or filters imposed by user interaction). The schema encodes this structure in the SystemBlueprint, listing components and their interconnections.

Example. In our example system, we model a VA system that includes a spatial data processing component Cspatial, a topological feature ex- traction component Ctopo, and three visualization components: Cmap, Cscatter, and Cbar. These components interact through structured de- pendencies, where data processing feeds into analysis and visualization components.

Component-level representation. A VA component is defined as a unit C = (I ,P,O,T ) with inputs I , properties P, outputs O, and internal operations T . These map to HighLevelBlocks and IntermediateBlocks in the specification, describing structure and func- tional roles.

Example. In our example system, Cspatial includes operations for data binning (Tbin) and density computation (Tdensity). The topological feature extraction component Ctopo depends on the output of Tdensity to compute topological features. Visualization components (Cmap, Cscatter, Cbar) then receive processed data to create representations for the user.

Operation-level representation. At the lowest level, an opera- tion is an atomic function Ti : Ii ×Pi →Oi, capturing low-level tasks within components. Each operation Ti is a function within a compo- nent that executes a specific task. Each operation Ti belongs to at least one component but may be shared across multiple components.

These operations map to GranularBlocks, detailing inputs, outputs, and downstream links. Example. For instance, Tdensity is used in both Cspatial and Cmap, while Tbin is necessary for both spatial data processing and the bar chart aggregation (Cbar). The operations Ttopo and Tfeature are specific to Ctopo for extracting structural features from spatial datasets.

Dependencies between components and operations. A component may depend on one or more other components, meaning it receives inputs from multiple sources. We denote this as {Cj1,Cj2,...,Cjk} → Ci, indicating that Ci requires the outputs of Cj1,Cj2,...,Cjk. Sim- ilarly, operations can depend on the outputs of other operations: {Tj1,Tj2,...,Tjk} →Ti. Since operations may belong to multiple com- ponents, we also express {Tj1,Tj2,...,Tjk} →Ci, meaning the execution of these operations produces outputs required by Ci. We classify de- pendencies into two types: (1) Data dependencies, which describe how data flows through the system – where the output of one component or operation serves as input to another; and (2) Interaction dependencies, which capture how user-driven interactions affect downstream compo- nents or operations, for example by passing constraints or filters rather than raw data. Data dependencies will be created when a component or operation produces data that is used as input for another component or operation. Interaction dependencies will be created when a component constrains or modifies another component or operation.

Example. In our example, density transformation provides data to the topology component: (Tdensity,Ctopo). In turn, the scatter plot depends on processed features from the topology component: (Ctopo,Cscatter).

The bar chart requires time-aggregated data from the binning trans- formation: (Tbin,Cbar).

For Interaction Dependencies, The Scatter

plot supports user-driven filtering in both the map and the bar chart: {(Cscatter,Cmap),(Cscatter,Cbar)}.

Nitial Knowledge Base

To align the specification schema with real-world systems, we manually analyzed 20 research papers from our corpus, constructing an initial knowledge base of VA system components, operations, and dependen- cies. This initial set was deliberately curated to ensure diversity across different dimensions. The selection included systems from various application domains (e.g., transportation , urban planning , flooding , weather ), using a wide range of data types (e.g., POI , crime , social media ), and originating from different authors and research groups. Through a systematic review, we identi- fied system components, operations, and dependencies. We selected 20 papers because, during the review process, we observed that the discovery of new component labels reached saturation. Specifically, the relative rate of new label discovery fell below 3% by the 20th paper, in- dicating that additional papers were unlikely to contribute substantially novel components.

In this review, we considered a component to be a self-contained, identifiable computational unit within the system. Conceptually, it should be modular enough to be reused and essential enough that re- moving it would degrade the system’s functionality or structure. When defining system components, we aimed to straddle a balance between over-generalization (i.e., they are too abstract to be useful in dataflows) and over-specialization (i.e., they are too rigid and unique per sys- tem, preventing reuse and modularity). To achieve this balance, we established a set of guiding principles for identifying and structuring components. First, components should encapsulate well-defined func- tionality. Second, they should have clear input-output relationships.

Third, components should be generalizable to units that can be used nent breaks the dataflow, it is a core component. During our review, we categorized components into data processing (e.g., data ingestion, transformation, filtering), analytics (e.g., clustering algorithms, feature extraction), visualization units, and interaction types. We analyzed both the textual descriptions and figures, focusing on sections detailing system architecture and implementation.

In addition, we examined operations, the low-level computations per- formed within a component. These serve as the building blocks of each component, specifying how input data is processed, transformed, or ren- dered. We map operations to components, establishing a hierarchical cies between components and operations. Textual descriptions usually provided clear cues regarding dependencies. For interaction dependen- cies, we paid attention to mentions of linked brushing, linked views, or filtering.

For each of the 20 selected papers, we conducted an in-depth review to identify an initial set of components, operations, and dependencies through an open coding approach. The process was carried out by a tants). Each paper was reviewed by one graduate assistant, with weekly meetings to review, refine, and resolve disagreements through discus- sion between all team members. In this process, we surfaced shared identified codes were collected into a shared pool of components, oper- ations, and dependencies. Once the initial set of codes was compiled, we grouped conceptually similar elements and refined their labels for naming consistency and functional clarity. For example, different terms for clustering (e.g., “clustering”, “grouping”) were unified under the cluster operation label and interaction techniques such as “brushing” or “highlighting” were grouped under select. After coding all 20 papers, we performed a final pass, making sure that the elements were aligned with our formal model. The final set of elements was reviewed and approved by all team members.

Scaling: Using An Llm To Extract Va Knowledge

While manually analyzing 20 papers allowed us to construct a consistent initial knowledge base, this process is time-consuming and difficult to scale. On average, it took approximately one hour to thoroughly review, code, and categorize each paper. To scale the knowledge base, we propose leveraging LLMs to automatically extract system elements from research papers. Our approach builds on the formal representation and codebook developed in the manual phase.

Prompting The Llm With Extraction Tasks

Once the schema was defined, we developed a prompting approach to guide an LLM in extracting system specifications from research papers. We used OpenAI’s GPT-4. Modern LLMs like GPT-4 demonstrate strong capabilities in zero-shot or few-shot learning for structured information extraction tasks, particularly when guided by well-defined schemas . The detail and hierarchical structure of our JSON schema are designed to leverage this strength, providing explicit constraints and context that help the model accurately map textual descriptions to the target data structure. To further enhance reliability and ensure the output precisely matched our desired format, we employ few-shot prompting with three specifications from our initial knowledge base to show the model what an ideal response looked like. Each prompt was designed to guide the LLM through a multi-step extraction process. At

Its Core, The Prompt Included:

• A description of the task. The model was told to act as a system de- signer reviewing a research paper to extract a complete specification of a VA system.

• A detailed explanation of the schema. We included the full JSON schema as a reference. • Instructions for document coverage. We asked the model to read the entire paper beyond the abstract and system overview, also including methods, implementation details, and use cases.

• Few-shot examples. We included examples from the manually cre- ated set. To ensure robustness, we incorporated standard practices from LLM summarization. This was implemented through a human-in-the-loop process (Figure 2), where we engaged in cycles of review and refine- ment. As part of this process, if an output was incomplete, ambiguous, or inconsistent upon review, we created follow-up prompts asking the model to revise specific sections based on previous results. Our manual refinement involved relabeling ∼10% of blocks for terminological con- sistency and adding ∼5% of missing ones; erroneous block removals were rare. The more substantial effort was adjusting ∼30% of depen- dency edges, which mostly involved adding overlooked connections to complete the system dataflow. In the last step, once we agreed that the model returned a correct JSON specification, we parsed and checked the output for schema validity.

A-Blueprint: A Knowledge Base For Va Systems

Following the methodology detailed in Section 3, we constructed the VA-Blueprint knowledge base—a structured repository capturing the vides an overview of the knowledge base’s structure and content (Sec- tion 4.1). We then introduce the visual interface developed to navigate and validate its entries and discuss the accessibility of these resources (Section 4.2).

Knowledge Base Overview

The VA-Blueprint knowledge base currently comprises structured rep- resentations of 101 urban VA systems, extracted systematically from research literature. Each system entry is stored as a distinct JSON file, adhering to the multi-level blueprint explained in Section 3.5. This

Structure Organizes Components Hierarchically:

1. High-level Blocks: Represent the major stages in a VA system:

Ata Processing,

Visualization, and Interaction. These align with the primary phases of a typical VA workflow. 2. Intermediate Blocks: Provide a more specific functional grouping within the high-level blocks. Common examples derived during our curation include Loader (under Data Loading); Querying, and Clustering (under Data Processing); Geospatial and Infovis (under Visualization); and Filter and Annotation (under Interaction). These intermediate blocks capture recurring sub-tasks or component types within the broader categories.

3. Granular Blocks: Represent the most specific elements identi- fied in each system.

Ine Chart), Specific

interaction mechanisms (e.g., Area Selection), distinct data in- puts (e.g., Trajectory Data), or particular analytical methods (e.g., k-Means Clustering). Each granular block includes details about its inputs, outputs, description, and an exact reference from the source paper that verifies the block’s existence.

4. Dependencies: Edges capture the relationships between blocks at various levels, categorized as either data dependencies (representing data flow) or interaction dependencies (representing control flow or filtering initiated by user actions).

The knowledge base serves as a detailed, machine-readable catalog of VA system architectures, grounded in published research. The consis- tent structure across all system entries facilitates systematic analysis

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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