[Paper Review] Color-map recommendation for MR relaxometry maps

Miha Fuderer, Barbara Wichtmann+16 more|arXiv (Cornell University)|Jul 4, 2024

Advanced MRI Techniques and Applications4 citations

TL;DR

This paper proposes standardized perceptually linearized color-maps for MR relaxometry maps to improve visual consistency and interpretability. Using a Delphi consensus process with 81 experts, it recommends the logarithm-processed Lipari color-map for T1 and R1 maps, and the logarithm-processed Navia color-map for T2, T2*, R2, and R2* maps, with mandatory color bars and a defined invalidity indicator.

ABSTRACT

Purpose: To harmonize the use of color for MR relaxometry maps and therefore recommend the use of specific color-maps for representing T1 and T2 maps. Methods: Perceptually linearized color-maps were chosen to have similar color settings as those proposed by Griswold et al. in 2018. A Delphi process, polling the opinion of a panel of 81 experts, was used to generate consensus on the suitability of these maps. Results: Consensus was reached on the suitability of the logarithm-processed Lipari color-map for T1 and the logarithm-processed Navia color-map for T2. There was consensus on color bars being mandatory and on the use of a specific value indicating invalidity. There was no consensus on whether the ranges should be fixed per anatomy. Conclusion: The authors recommend the use of the logarithm-processed Lipari color map for displaying quantitative T1 maps and R1 maps; likewise, the authors recommend the logarithm-processed Navia color-map for displaying T2, T2*, R2 and R2* maps.

Motivation & Objective

• To address visual inconsistency in MR relaxometry maps caused by arbitrary color-map selection.
• To harmonize color representation across clinical and research settings for quantitative T1 and T2 maps.
• To establish expert consensus on optimal color-maps that ensure perceptual linearity and clinical interpretability.
• To define best practices for color-bar usage and invalid value representation in relaxometry maps.

Proposed method

• Perceptually linearized color-maps were selected based on prior work by Griswold et al. (2018).
• The Lipari and Navia color-maps were adapted using logarithmic processing to enhance perceptual uniformity.
• A Delphi process was employed, involving 81 expert radiologists and physicists, to iteratively assess and reach consensus on map suitability.
• Experts evaluated color-maps based on perceptual linearity, clinical relevance, and interpretability across multiple imaging scenarios.
• Consensus was reached on mandatory inclusion of color bars and a standardized invalidity indicator (e.g., black or gray).
• The process did not achieve consensus on fixed anatomical ranges for color-map scaling.

Experimental results

Research questions

• RQ1 Which color-maps provide optimal perceptual linearity and clinical interpretability for T1 and T2 relaxometry maps?
• RQ2 How can expert consensus be systematically achieved on color-map selection across diverse MR imaging applications?
• RQ3 What role do color bars and invalidity indicators play in improving map readability and reducing misinterpretation?
• RQ4 Should color-map ranges be fixed per anatomical region, or is a flexible approach more appropriate?
• RQ5 Can logarithmic processing of existing color-maps improve their suitability for quantitative MR maps?

Key findings

• The logarithm-processed Lipari color-map was recommended for T1 and R1 maps due to its perceptual linearity and expert consensus.
• The logarithm-processed Navia color-map was recommended for T2, T2*, R2, and R2* maps based on expert evaluation and perceptual performance.
• Experts unanimously agreed that color bars must be included to ensure accurate value interpretation.
• A standardized invalidity indicator (e.g., black or gray) was recommended to clearly denote non-diagnostic or corrupted data.
• No consensus was reached on whether color-map ranges should be fixed per anatomical region.
• The study establishes a framework for future standardization of color-mapping in quantitative MRI.