Arduino Screen Reader

Eurographics Conference on Visualization (EuroVis) 2022

Volume 41 (2022), Number 3

Rich Screen Reader Experiences for Accessible Data Visualization

Abstract

Current web accessibility guidelines ask visualization designers to support screen readers via basic non-visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables; they afford interactive data exploration at varying levels of granularity — from fine-grained datum-by-datum reading to skimming and surfacing high-level trends. In response to the lack of comparable non-visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co-design process, we identify three key design dimensions for expressive screen reader accessibility: structure, or how chart entities should be organized for a screen reader to traverse; navigation, or the structural, spatial, and targeted operations a user might perform to step through the structure; and, description, or the semantic content, composition, and verbosity of the screen reader’s narration. We op- erationalize these dimensions to prototype screen-reader-accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed-methods study with 13 blind and low vision readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process. An accessible HTML version of this paper is available at: http://vis.csail.mit.edu/pubs/rich-screen-reader-vis-experiences.

Ccs Concepts

• Human-centered computing →Visualization design and evaluation methods; Accessibility design and evaluation methods;

1. Introduction

Despite decades of visualization research and recent legal require- ments to make web-based content accessible [W3C18b; Hig19], web-based visualizations remain largely inaccessible to people with visual disabilities. Charts on mainstream publications are of- ten completely invisible to screen readers (an assistive technology that transforms text and visual media into speech) or are rendered as incomprehensible strings of “graphic graphic graphic” [SCWR21; Sar20]. Current accessibility guidelines ask visualization designers to provide textual descriptions of their graphics via alt text (short for alternative text) and link to underlying data tables [GOF08; W3C19]. However, these recommendations do not provide modes of information-seeking comparable to what sighted readers enjoy with interactive visualizations. For instance, well-written alt text can provide a high-level takeaway of what the visualization shows, but it does not allow readers to drill down into the data to explore specific sections. While tables provide readers with the ability to hone in on specific data points, reading data line-by-line quickly becomes tedious and makes it difficult to identify overall trends.

Developing rich non-visual screen reader experiences for data visualizations poses several unique challenges. Although visuomo- tor interactions (like hovering, pointing, clicking, and dragging) have been core to visualization research [DP20], screen readers redefine what interaction is for visualization. Rather than primar- ily manipulating aspects of the visualization or its backing data

∗Equal Contribution

pipeline [YKSJ07; HS12; DP20], screen readers make reading a visualization an interactive operation as well — users must inten- tionally perform actions with their input devices in order to cognize visualized elements. Moreover, as screen readers narrate elements one-at-a-time, they explicitly linearize reading a visualization. As a result, in contrast to sighted readers who can choose to selectively attend to specific elements and have access to the entire visual- ization during the reading process, screen reader users are limited to the linear steps made available by the visualization author and must remember (or note down) prior output conveyed by the screen reader. Despite these modality differences, studies have found that screen reader users share the same information-seeking goals as sighted readers: an initial holistic overview followed by comparing data points [SCWR21], akin to the information-seeking mantra of “overview first, zoom and filter, and details on demand” [Shn03].

In this paper, we begin to bridge this divide by conducting an iterative co-design process (co-author Hajas is a blind researcher with relevant experience) prototyping rich and usable screen reader experiences for web-based visualizations. We identify three design dimensions for enabling an expressive space of experiences: struc- ture, or how the different elements of a chart should be organized for a screen reader to traverse; navigation, which describes the op- erations a user may perform to move through this structure; and, description, which specifies the semantic content, composition, and verbosity of text conveyed at each step. We demonstrate how to operationalize these design dimensions through diverse accessible reading experiences across a variety of chart types.

© 2022 The Author(S)

Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

Arxiv:2205.04917V1 [Cs.Hc] 10 May 2022

J. Zong, C. Lee, & A. Lundgard et al. / Rich Screen Reader Experiences for Accessible Data Visualization To evaluate our contribution, we conduct an exploratory mixed- methods study with a subset of our prototypes and 13 blind or low vision screen reader users. We identify specific features that make visualizations more useful for screen reader users (e.g., hier- archical and segmented approaches to presenting data, cursors and roadmaps for spatial navigation) and identify behavior patterns that screen reader users follow as they read a visualization (e.g., con- stant hypothesis testing and validating their mental models).

2. Background And Related Work

Screen Reader Assistive Technology. A screen reader is an assis- tive technology that conveys digital text or images as synthesized speech or braille output. Screen readers are available as standalone third-party software or can be built-in features of desktop and mo- bile operating systems. A screen reader allows a user to navigate content linearly with input methods native to a given platform (e.g., touch on smartphones, mouse/keyboard input on desktop). Content authors must generate and attach alt text to their visual content like images or charts in order for them to be accessible to screen reader users. Functionality and user experience differs across platforms and screen readers. In this paper, however, we focus on interacting with web-based visualizations with the most widely used desktop screen readers (JAWS/NVDA for Windows, VoiceOver for Mac).

Web Accessibility Standards. In 2014, the World Wide Web Consortium (W3C) adopted the Web Accessibility Initiative’s Ac- cessible Rich Internet Applications protocol (WAI-ARIA) which introduced a range of semantically-meaningful HTML attributes to allow screen readers to better parse HTML elements [MDN21].

In particular, these attributes allow a screen reader to convey the state of dynamic widgets (e.g., autocomplete is available for text entry), alert users to live content updates, and identify common sec- tions of a web page for rapid navigation (e.g., banners or the main content). In 2018, the W3C published the WAI-ARIA Graphics Module [W3C18a] with additional attributes to support marking up structured graphics such as charts, maps, and diagrams. These at- tributes allow designers to annotate individual and groups of graph- ical elements as well as surface data values and labels for a screen reader to read aloud.

Accessible Visualization Design. In a recent survey, Kim et al. [KJRK21] describe the rich body of work that has explored multi-sensory approaches to visualization for multiple disabili- ties [YRB01; HMM*11; KWT99; BK99; WPA*21; LLS19]. Here, we focus on screen reader output native to web-based interfaces for blind users (namely via speech). Sharif et al. [SCWR21] find that many web-based charts are intentionally designed to cause screen readers to skip over them. For charts that a screen reader does de- tect, blind or low vision users nevertheless experience significant difficulties: these users spend 211% more time interacting with the charts and are 61% less accurate in extracting information com- pared to non-screen-reader users [SCWR21]. Despite the availabil- ity of ARIA, alt text and data tables remain the most commonly used and recommended methods for making web-based charts ac- cessible to screen readers [GOF08; W3C19; CJP*19]. However, each of these three approaches comes with its own limitations.

Static alt text requires blind readers to accept the author’s inter- pretation of the data; by not affording exploratory and interactive modes, alt text robs readers of the necessary time and space to in- terpret the numbers for themselves [LS21]. Recent research also suggests that blind people have nuanced preferences for the kinds of visual semantic content conveyed via text [PGFM21; LS21], and desire more interactive and exploratory representations of pictorial images [MJBC18]. Data tables, on the other hand, undo the ben- efits of abstraction that visualizations enable — they force readers to step sequentially through data values making it difficult to iden- tify larger-scale patterns or trends, and do not leverage the structure inherent to web-based grammars of graphics [BOH11; SMWH17].

Finally, ARIA labels are not a panacea; even when they are used judiciously — a non-trivial task which often results in careless de- signs that cause screen readers to simply read out long sequences of numbers without any other identifiable information [Sar20] — they present a fairly low expressive ceiling. The current ARIA specifica- tion does not afford rich and nuanced information-seeking oppor- tunities equivalent to those available to sighted readers.

There has been some promising progress for improving sup- port for accessibility within visualization toolkits, and vice-versa for improving native support for charts in screen reader technolo- gies. For instance, Vega-Lite [SMWH17] and Highcharts [Hig21] are beginning to provide ARIA support out-of-the-box. Apple’s VoiceOver Data Comprehension feature [DE19] affords more gran- ular screen reader navigation within the chart, beyond textual sum- maries and data tables, via four categories of selectable interactions for charts appearing in Apple’s Stocks or Health apps. These inter- actions include Describe Chart, which describes properties of the chart’s construction, such as its encodings, axis labels, and ranges; Summarize Numerical Data, which reports min and max data val- ues, and summary statistics like mean and standard deviation; De- scribe Data Series, which reports the rate-of-change/growth of a curve, trends, and outliers; and Play Audiograph, which plays a tonal representation of the graph’s ascending/descending trend over time [DE19]. While Apple’s features are presently limited to single-line charts, SAS’ Graphics Accelerator [Acc18] supports a similar featureset (including sonification, textual descriptions, and data tables) but for a broader range of statistical charts including bar charts, box plots, contour plots, and scatter plot matrices. Our work follows in the spirit of these tools but focuses on web-based visu- alizations rather than standalone- or platform-integrated software.

We go beyond what ARIA supports today to enable high-level and fine-grained screen reader interactions, and hope that our work will help inform ongoing discussions on improving web accessibility standards (e.g., via an Accessibility Object Model [BCMS22]).

3. Design Dimensions for Rich Screen Reader Experiences Currently, the most common ways of making a visualization ac- cessible to screen readers include adding a single high-level textual description (via alt text), providing access to low-level data via a table, or tagging visualization elements with ARIA labels to allow screen readers to step through them linearly (e.g., as with High- charts [Hig21]). While promising, these approaches do not afford rich information-seeking behaviors akin to what sighted readers en- joy with interactive visualizations. To support systematic thinking about accessible visualization design, we introduce three design di- mensions that support rich, accessible reading experiences: struc-

© 2022 The Author(S)

Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd. J. Zong, C. Lee, & A. Lundgard et al. / Rich Screen Reader Experiences for Accessible Data Visualization ture, or how elements of the visualization should be organized for a screen reader to traverse; navigation, or the mechanisms by which a screen reader user can move from one element to another; and description, or what semantic content the screen reader conveys.

Methods. We began by studying the development of multi- sensory graphical systems, covering work in critical cartogra- phy [WG69; Koc12], blind education [AS01; GL15], tactile graph- ics [FFTI18; HMM*11; dGMB21; AC97; BHR*21], and multi- sensory visualization [MTS19; CPR*21; BTOJ10; BMD*16].

Drawing on conventions and literature on crip, reflective, and par- ticipatory design [Ham13; SBDK05; Cos20], all authors began an iterative co-design process with Hajas, who is a blind researcher with relevant expertise. Hajas is a screen reader user with a PhD in HCI and accessible science communication, but he is not an ex- pert in visualization research. Co-design — particularly as encapsu- lated in the disability activism slogan, “Nothing about us, without us” [Cos20] — is important because it can eliminate prototypes that replicate existing tools, solve imaginary problems (i.e., by creat- ing disability dongles [Jac19]) or unintentionally produce harmful technology [She20]. To balance engaging disabled users while ac- knowledging academia’s traditionally extractive relationship with marginalized populations [CJ95], we intentionally acknowledge Hajas as both co-designer and co-author. We believe that the dis- tinction between co-designer — a phrase that often discounts lived experience as insufficiently academic — and researcher is minimal; technical, qualitative, and experiential expertise are all important components of this research. Hajas’ profile is a perfect example of the intersection between lived experience of existing challenges and solutions, academic experience of research procedures, and an interest in the science of visualization. While he does not represent all screen reader users, his academic expertise and lived experience uniquely qualify him to be both researcher and co-designer. Never- theless, to incorporate a diverse range of perspectives, we recruited additional participants as part of an evaluative study (§ 5).

Our work unfolded over 6 months and yielded 15 prototypes. All authors met weekly for hour-long video conferences. In each ses- sion, we would discuss the structure and affordances of the proto- types, often by observing and recording Hajas’ screen as he worked through them. We would also use these meetings to reflect on how the prototypes have evolved, compare their similarities and differ- ences, and whiteboard potential design dimensions to capture these insights. Following these meetings, Hajas wrote memos detailing the motivations for each prototype, tagging its most salient features, summarizing the types of interactions that were available, enumer- ating questions that the prototype raises, and finally providing high- level feedback about its usefulness and usability. In the following section, we liberally quote these memos to provide evidence and additional context for our design dimensions.

3.1. Structure

We define structure to mean an underlying representation of a vi- sualization that organizes its data and visual elements into a format that can be traversed by a screen reader. Through our co-design process, we identified two components important to analyzing ac- cessible structures: their form, or the shape they organize informa- tion into; and entities, or which parts of the visualization specifica- tion are used to translate a chart into a non-visual structure. Design decisions about form and entities are guided by considerations of information granularity, or how many levels comprise the range between a high-level overview and individual data values.

Form. Accessible structures organize information about the vi- sualization into different forms, including lists, tables, and trees. Consider existing best practices and common approaches. A ras- terized chart with alt text is represented to a screen reader as a single node. SVG-based visualizations can additionally be tagged with ARIA labels to describe the axes, legends, and individual data points. Despite SVG’s nesting, screen readers linearize these ARIA labels into a list structure so that the user can step through them se- quentially. Data tables, on the other hand, provide a grid structure for screen readers to traverse. At each cell of the grid, the screen reader reads out a different textual description, allowing the user to explore a space by traversing the grid spatially (up, down, left, and right) instead of merely linearly. Accessible visualization re- search has begun to explore the use of tree structures for storing chart metadata [WOH*15], but they remain relatively rare in prac- tice. Our prototypes primarily use trees as their branching and hier- archical organization allows users to browse different components of a visualization and traverse them at different levels of detail.

Entities. Where form refers to how nodes in a structure are ar- ranged, entities instead refers to what aspects of the visualization

The Nodes Represent. These Aspects Can Include:

• Data, where nodes in the structure represent individual data val- ues or different slices of the data cube (e.g., by field, bins, cat- egories, or interval ranges). For example, in a data table, every node (i.e. cell) represents a data value designated by the row and column coordinates. Depending on the form, data entities can be presented at different levels of detail. For example, one proto- type we explored represents a line chart as a binary tree structure (Fig. 2e): the root node represents the entire x-axis domain, and each left and right child node recursively splits the domain in half. Users can traverse the tree downward to binary search for specific values or understand the data distribution.

• Encodings, where nodes in the structure correspond to visual channels (e.g., position, color, size) that data fields map to. For instance, consider Figure 1a which depicts the encoding struc- ture of a Vega-Lite scatterplot. The visualization is specified as mappings from data fields to three visual encoding channels: x, y, and color. Thus, the encoding structure, which here takes the form of a tree, comprises a root node that represents the en- tire visualization and then branches for each encoding channel as well as the data rectangle (x-y grid). Descending into these branches yields nodes that select different categories or interval regions, determined by the visual affordances of the channel. For instance, descending into axis branches yields nodes for each in- terval between major ticks; x-y grid nodes represent cells in the data rectangle as determined by intersections of the axes grid- lines; and legend nodes reflect the categories or intervals of the encoding channel (i.e., for nominal or quantitative data respec- tively). Finally, the leaves of these branches represent individual data values that fall within the selected interval or category.

• Annotations, where nodes in the structure represent the rhetori- cal devices a visualization author may use to to shape a visual

© 2022 The Author(S)

Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd. J. Zong, C. Lee, & A. Lundgard et al. / Rich Screen Reader Experiences for Accessible Data Visualization Figure 1: (a) An accessible visualization structure in the form of a tree and comprised of encoding entities. Solid magenta outlines indicate the location of the screen reader cursor. Solid blue arrows between labels indicate available next steps via keyboard navigability (up, down, left, right). (b) Three ways of navigating accessible visualization structures: structural, spatial, and targeted.

narrative or guide reader interpretation of data (e.g., by draw- ing attention to specific data points or visual regions). Surfacing annotations in the visualization structure allows screen reader users to also benefit from and be guided by the author’s narra- tive intent. For example, Figure 2d illustrates an annotation tree structure derived from an example line chart with two annota- tions highlighting intervals in the temporal x-axis. The root of the tree has two children representing the two annotated regions.

The these two annotation nodes have a child node for each data point that is highlighted within the region of interest. Considerations: Information Granularity. When might users prefer nested structures (i.e. trees) over flat structures (i.e., lists and tables)? Like sighted users, screen reader users seek information by looking for an overview before identifying subsets to view in more detail [SCWR21]. Trees allow users to read summary infor- mation at the top of the structure, and traverse deeper into branches to acquire details-on-demand. Kim et al. use the term information granularity to refer to the different levels of detail at which an ac- cessible visualization might reveal information [KJRK21]. They organize granularity into three levels: existence, overview, and de- tail. Existence includes information that a chart is present, but no information about underlying data. Overview includes summary in- formation about data — e.g. axes, legends, and summary statistics like min, max, or mean — but not individual data points. Detail in- cludes information about precise data values.

We use the root node to signal the existence of the tree, and deeper nodes in the tree reflect finer levels of granularity. Branch nodes give an overview summary about the data underneath, pro- viding information scent [PC99], while leaf nodes map to individ- ual data points. In his feedback about the prototype shown in Fig- ure 1, Hajas wrote “considering how difficult reading a scatterplot with a screen reader is due to its sequential reading nature, the tree structure makes the huge number of data points fairly readable”.

Entities are not mutually exclusive, and a structure might opt to surface different entities in parallel branches. We prototyped a ver- sion of Figure 2d which placed an encoding tree and annotation tree as sibling branches under the root node. Users could descend down a given branch, and switch to the equivalent location in the other branch at will. These design decisions are motivated by find- ings in prior work: by placing encodings and annotations as co- equal branches, we produce a structure that preserves the agency of screen reader users either to start with the narrative arc of an- notations, or follow it after having the chance to interpret the data for themselves [LS21]. As Hajas confirms “Depending on my task, either the encoding or annotation tree could be more important. If my task involved checking population growth in the last 100 years, I would start with the encodings. If I were to look for sudden changes in population numbers, such war-time mortality effects, I would start exploring the annotations, then tunnel back to the other tree.”

3.2. Navigation

Screen reader users need ways to traverse accessible structures to explore data or locate specific points. When browsing a webpage, screen readers provide a cursor that represents the current loca- tion in the page. Users use keyboard commands to step the cursor backward and forward in a sequential list of selectable items on the page, or jump to important locations such as headers and links.

Through our prototyping process, we developed three ways of nav- igating through an accessible structure: structural navigation, spa- tial navigation, and targeted navigation (Fig. 1b). A key concern across these navigation schemes is reducing a user’s cognitive load by affording a sense of the boundaries of the structure.

Structural Navigation. Structural navigation refers to ways users move within the accessible structure. We identify two types of structural navigation. Local navigation refers to step-by-step movements between adjacent nodes in the structure. This includes moving up and down levels of a hierarchy, or moving side to side between sibling elements. Lateral navigation refers to movement between equivalent nodes in adjacent sub-structures. For example, Fig. 2a depicts a multi-view visualization with six facets. When the cursor is on a Y-axis interval for the first facet, directly moving to the same Y-axis interval on the second facet is a lateral move.

Spatial Navigation. Sometimes users want to traverse the visu- alization according to directions in the screen coordinate system. We refer to this as spatial navigation. For example, when travers-

© 2022 The Author(S)

Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd. J. Zong, C. Lee, & A. Lundgard et al. / Rich Screen Reader Experiences for Accessible Data Visualization ing part of an encoding structure that represents the visualization’s X-Y grid, a downward structural navigation would go down a level into the currently selected cell of the grid, showing the data points inside the cell. A downward spatial navigation, in contrast, would move to the grid cell below the current one — i.e. towards the bot- tom of the Y-axis. Spatial navigation is also useful when navigating lists of data points, which may not be sorted by X or Y value in the encoding structure. Where a leftward structural navigation would move to the previous data point in the structure, a leftward spatial navigation would move to the point with the next lowest X value.

Targeted Navigation. Navigating structurally and spatially re- quires a user to maintain a mental map of where their cursor is relative to where they want to go. If the user has a specific target location in mind, maintaining this mental map in order to find the correct path in the structure to their target can create unnecessary cognitive load. We use targeted navigation to refer to methods that only require the user to specify a target location, without needing to specify a path to get there. For example, the user might open a list of locations in the structure and select one to jump directly there. Screen readers including JAWS and VoiceOver implement an analogous form of navigation within webpages. Instead of man- ually stepping through the page to find a specific piece of content, users can open a menu with a list of locations in the page. These locations are defined in HTML using ARIA landmark roles, which can designate parts of the DOM as distinct sections when read by a screen reader. When a screen reader user open the list of landmarks and selects a landmark, their cursor moves directly to that element.

Considerations: Boundaries & Cognitive Load. Screen reader users only read part of the visualization at a time, akin to a sighted user reading a map through a small tube [HMM*11]. How do they keep track of where they are? In our co-design process, we found it easiest for a user to remember their location relative to a known starting point, which is corroborated by literature on developing spatial awareness for blind people [WG69; LKM*19; CPR*21].

Hajas noted the prevalence of the Home and End shortcuts across applications for returning to a known position in a bounded space (e.g. the start/end of a line in a text editor). We also found that grouping data by category or interval was helpful for maintaining position. Hajas noted that exploring data within a bounded region was like entering a room in a house. In his analogy, a house with many smaller rooms with doors is better than a house with one big room and no doors. Bounded spaces alleviate cognitive load by al- lowing a user to maintain their position relative to entry points.

Comparing navigation techniques, Hajas noted that spatial felt “shallow but broad” while targeted felt “deep but narrow.” While he expressed a personal preference for deep-narrow structures, he nevertheless “would not give up [spatial navigation] because it makes me believe I’m actually interacting with a visualization.” This insight demonstrates the value of offering multiple comple- mentary navigation techniques. Moreover, while targeted naviga- tion facilitates quick searching and doesn’t require the user to main- tain a mental map to find specific data points, structural and spatial exploration enable more open-ended data exploration. It also pro- vides a mechanism for establishing common ground with sighted readers (e.g., allowing both blind and sighted readers to understand a line segment as being “above” or “higher” than another).

3.3. Description

When a user navigates to a node in a structure, the screen reader narrates a description associated with that node. For example, when navigating to the chart’s legend, the screen reader output might ar- ticulate visual properties of the chart’s encoding: “Category O has color encoding green; X has color encoding orange” (Figure 1).

Or, if that visual semantic content isn’t relevant to understanding the data, it might ignore the color: “each datum belongs to either Category O or X.” The content, composition, and verbosity of the description can affect a user’s comprehension of the data. Design- ers must consider context & customization when describing charts.

Content. Semantic content is the meaningful information con- veyed not only through natural language utterances, but also through the visualization (a graphical language [Ber83]). Because graphics convey myriad different kinds of content, the challenge of natural language description is to convey information that is not only commensurate with what the chart expresses via graphical language, but also useful to its readers. Accessible chart descrip- tion guidelines from WGBH [GOF08], W3C [W3C19], and oth- ers [JMK*21] offer prescriptions for conveying specific content for blind readers (such as the chart’s title, axis encodings, and notewor- thy trends). Lundgard and Satyanarayan expand the scope of these guidelines with a more general conceptual model of four levels of semantic content: chart construction properties (e.g., axes, encod- ings, marks, title); statistical concepts and relations (e.g., outliers, correlations, descriptive statistics); perceptual and cognitive phe- nomena (e.g., complex trends, patterns); and domain-specific in- sights (e.g., socio-political context relevant to the data) [LS21].

Decoupling a chart’s semantic content from its visual represen- tation helps us better understand what data representations afford for different readers. For instance, Lundgard and Satyanarayan find that what blind readers report as most useful in a chart description is not a straightforward translation of the visual data representa- tion. Specifically, simply listing the chart’s encodings is much less useful to blind readers than conveying summary statistics and over- all trends in the data [LS21]. As Hajas noted, “I want to see the global trend, which is why sighted people rely on visualization.” For instance, for a stock market chart the reader “might see the overview from first to last data points, and then zoom into an out- lier in the middle.” These findings suggest opportunities interleave different kinds of content at different levels of a hierarchical struc- ture to yield richer, more useful screen reader navigation. For ex- ample, injecting summary statistics (say, the existence of outliers within a particular subcategory of the data) higher up in the chart’s tree structure (e.g., at the legend encoding node) might afford “scent” for “information foraging” [PC99], or further exploration down a particular branch (data subcategory) of the tree. Or, if nav- igating in a targeted fashion, the user might be afforded the option to directly navigate to outliers without traversing the tree.

Composition. The usefulness of a description depends not only on the content conveyed by its constituent sentences, but also on its composition: how those sentences are ordered in relation to each other. For example, during our co-design process, Hajas found that when navigating a chart’s tree structure, the screen reader output could quickly become redundant, affecting how quickly and effi- ciently he could pick out the meaningful information at each node.

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Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd. J. Zong, C. Lee, & A. Lundgard et al. / Rich Screen Reader Experiences for Accessible Data Visualization For instance, the utterance “Category: O, Point 3 of 15, x = 5, y = 12” and the utterance “x = 5, y = 12, Category: O, Point 3 of 15” afford significantly different experiences for a user who wishes to quickly scan through individual data points. In the first utterance, the reader immediately receives content that helps to situate them in a broader data context, namely data labeled as “Category: O” at the legend node. In the second utterance, the reader immediately re- ceives datum-specific content that helps to rapidly explore the fine- grained details within that data context. Whether a reader prefers one compositional ordering to another will depend on the task they are attempting to accomplish. As Hajas noted “I like the label at the beginning of the information, saying at which level of the tree I am at. It is important for knowing where I am. It is also great that this information is only spoken out when I change level, but not when I navigate laterally.” These compositional choices are highly consequential for readers’ experience, when they must repeatedly read nearly-identical utterances while navigating a structure.

Verbosity. Whereas composition refers to the ordering of con- tent, verbosity refers to how much content the screen reader con- veys. More content is not always better. As Hajas noted of Apple’s Data Comprehension feature [DE19]: “It can sometimes be too much information all at once, if it starts reading out all of the data.

This is very difficult if you’re interested in some data points that are in the middle. It is very play-or-stop.” Depending on the screen reader software, a user may be afforded control over how much content is conveyed. For instance, JAWS offers high, medium, and low verbosity levels [Sci21]. At higher verbosity the screen reader announces more structural, wayfinding content (e.g. the start and end of regions). For data tables, verbosity configurations can affect whether the table size is read as part of the description, and whether row and column labels are repeated for every cell. Descriptions of nodes in an encoding structure might analogously include infor- mation about the path from the root — for example, by reminding the user that they are reading Y-axis intervals. These repetitions can help users remember their location within a structure, but additional verbosity is less efficient for comprehending the data quickly.

Considerations: Context & Customization. Apart from its constituent parts (content, composition, verbosity), a description’s usefulness also depends on the context in which it is read: namely, the reader’s task or intent, and familiarity with the data interface.

The same description might be useful in some situations, but rel- atively useless in others. A reader’s information needs are fun- damentally context-sensitive. For example, as Hajas noted, when reading a news article, it may be satisfactory to accept a journalist’s description of the data on good faith. But, when reviewing scientific research, “I don’t necessarily want to just believe what is said in the text, I want to check and double-check the authors’ claims. Go down to the smallest numbers in the analysis. I want to be able to look at the confusion matrix and see if they made a mistake or not.” This targeted verification requires a description to afford users with precise look-up capabilities, in contrast to descriptions that may be generated when browsing or exploring the data.

This context-sensitivity reveals an important aspect of usabil- ity: a user’s familiarity (or lack thereof) with the data interface. Wayfinding content (e.g., “Legend. Category: O.”) can help a user remember their location in a structure, and may be useful while they assemble a mental map of the visualization. But, as they become ac- customed to the interface and visualization, such descriptions may prove cumbersome. Because user needs depend on their task, pref- erences, and familiarity, interfaces might afford personalization and customization to facilitate context-sensitive description.

4. Example Gallery

Our co-design process yielded prototypes that demonstrate a breadth of ways to operationalize our design dimensions. Figure 2 excerpts some of our highest-fidelity prototypes, implemented on top of Vega-Lite [SMWH17]. As deeply nested structures and dy- namic content are not well-supported by ARIA, we implemented our designs as in-memory data structures. Event listeners update the user’s position in the structure on keypress, and write text de- scriptions to an ARIA-Live region (an ARIA role typically used for temporary notifications). To establish common ground with sighted users, we also render the visualization graphically. The user’s po- sition in the tree drives a Vega-Lite selection that highlights points when the screen reader user is attending to them.

For every prototype, the up, down, left, and right arrow keys enable structural navigation (moving up or down a level, or stepping through siblings respectively). For example, within the facet level of Fig. 2(a), the user can press left or right keys to move between the six subplots of the multiview chart. On charts that contain a node representing the x-y grid, users can also use the WASD keys to spatially navigate the grid and data points within that branch (mimicking an interaction found in video games).

These prototypes highlight different compositions of structures and navigation schemes. Fig. 2(a) includes shift+left and shift+right for lateral navigation across facets: pressing these keys at any node within a facet branch will navigate to the same location under an adjacent branch (subplot). With the chloropleth (Fig. 2(b)), we group data in the encoding structure by U.S. state; users can then drill down into counties across either this branch or the legend one. 2(c) offers two different paths for drilling down: month first, or weather first. 2(d) structures the tree by annotations rather than encoding: users can descend into the time intervals designated by the orange and blue rectangles, and view points within those intervals. Finally, Fig. 2(e) organizes its tree in terms of data, offering a binary search structure through the years.

5. Evaluation

To evaluate our contribution, we conducted 90-minute Zoom stud- ies with 13 blind and low vision participants. Participants were asked to explore three prototype accessible screen reader expe- riences, shown one after another each with a different dataset.

The goal of our evaluation was not to determine which particu- lar combination of design elements was “best,” but rather to be exploratory — to compare the relative strengths and advantages of instantiations of our design dimensions, and understand how they afford different modes of accessible data exploration.

5.1. Evaluation Setup & Design

Following Frøkjær and Hornbæk’s Cooperative Usability Testing (CUT) method [FH05], Zong and Lee conducted each session by

© 2022 The Author(S)

Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Authors:

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

94143, Usa.

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

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

14Jlvmi Consulting Llc, Dousman, Wi, Usa

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

Abstract

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

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

Introduction

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

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

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

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

●

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

●

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

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

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

Hyperpolarized 13C-Pyruvate Preparation

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

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

General Considerations

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

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

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

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

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

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

Personnel

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

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

Equipment And Facility

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

Material Handling

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

Pharmacy Kit Filling And Assembling

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

Quality Control And Dose Release

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

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

The Final Dose Release And Injection

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

Some Key Challenges

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

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

Current Practices

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

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

In House

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

Summary

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

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

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

Mri System Setup And Calibrations

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

Imaging System

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

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

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

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

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

Rf Coils

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

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

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

Provide B1 Transmit Across The Fov (B1

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

B1

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

+ Profile But Has Been Used Because Of

relatively easy integration into the scanner bore. B1

+ Variation Results In Variations In The Flip

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

Homogeneous B1

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

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

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

(1)

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

Tx = Transmit

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

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

Phantoms

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

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

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

+) And Receive (B1

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

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

Prescan Calibration

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

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

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

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

+ Inhomogeneity As Well

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

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

Power [Kw]

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

8

13C-bicarbonate doped with dimethyl silicone, various

Maximum Values

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

Summary

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

+ Profiles. The

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

For Calibration Of B1

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

Acquisition And Reconstruction

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

+ Inhomogeneity,

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

Acquisition And Reconstruction Methods

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

Mrs/I Methods Specifically

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

Chemical Shift

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

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

Their Application To Different

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

The Majority Of

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

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

Prostate Studies

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

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

Heart Studies

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

Brain Studies

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

Abdomen And Breast Studies

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

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

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

1H Imaging

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

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

Reported Study Parameters

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

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

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

(B)

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

Summary

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

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

Data Analysis And Quantification

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

Metrics

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

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

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

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

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

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

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

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

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

Visualization

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

Metrics

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

Parameter Encoding

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

Anatomical Context

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

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