Color Maps: Facilitating the Clinical Impact of Quantitative MRI.
color maps
contrast‐enhanced imaging
diffusion
elastography
fat fraction
relaxometry
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
23 Aug 2024
23 Aug 2024
Historique:
revised:
05
08
2024
received:
13
06
2024
accepted:
05
08
2024
medline:
24
8
2024
pubmed:
24
8
2024
entrez:
24
8
2024
Statut:
aheadofprint
Résumé
Presenting quantitative data using non-standardized color maps potentially results in unrecognized misinterpretation of data. Clinically meaningful color maps should intuitively and inclusively represent data without misleading interpretation. Uniformity of the color gradient for color maps is critically important. Maximal color and lightness contrast, readability for color vision-impaired individuals, and recognizability of the color scheme are highly desirable features. This article describes the use of color maps in five key quantitative MRI techniques: relaxometry, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI, MR elastography (MRE), and water-fat MRI. Current display practice of color maps is reviewed and shortcomings against desirable features are highlighted. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NWO
ID : NWO grant number 17986
Informations de copyright
© 2024 The Author(s). Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Références
Aide N, Lasnon C, Veit‐Haibach P, Sera T, Sattler B, Boellaard R. EANM/EARL harmonization strategies in PET quantification: From daily practice to multicentre oncological studies. Eur J Nucl Med Mol Imaging 2017;44(Suppl 1):17‐31.
Kaalep A, Sera T, Oyen W, et al. EANM/EARL FDG‐PET/CT accreditation ‐ summary results from the first 200 accredited imaging systems. Eur J Nucl Med Mol Imaging 2018;45(3):412‐422.
Lasnon C, Salomon T, Desmonts C, et al. Generating harmonized SUV within the EANM EARL accreditation program: Software approach versus EARL‐compliant reconstruction. Ann Nucl Med 2017;31(2):125‐134.
Aye N, Lehmann N, Kaufmann J, et al. Test‐retest reliability of multi‐parametric maps (MPM) of brain microstructure. Neuroimage 2022;256:119249.
Riches SF, Payne GS, Morgan VA, et al. Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast‐enhanced and spectroscopic parameters. Eur Radiol 2015;25(5):1247‐1256.
Yue X, Yang L, Wang R, et al. The diagnostic value of multiparameter cardiovascular magnetic resonance for early detection of light‐chain amyloidosis from hypertrophic cardiomyopathy patients. Front Cardiovasc Med 2022;9:1017097.
Kijowski R, Sharafi A, Zibetti MVW, Chang G, Cloos MA, Regatte RR. Age‐dependent changes in knee cartilage T(1), T(2), and T(1p) simultaneously measured using MRI fingerprinting. J Magn Reson Imaging 2023;57(6):1805‐1812.
Fuderer M, Wichtmann B, Crameri F, et al. Color‐map recommendation for MR relaxometry maps. 2024. https://doi.org/10.48550/arXiv.2407.03906.
Hasson F, Keeney S, McKenna H. Research guidelines for the Delphi survey technique. J Adv Nurs 2000;32(4):1008‐1015.
Crameri F, Shephard GE, Heron PJ. The misuse of colour in science communication. Nat Commun 2020;11(1):5444.
Wolfe JM, Horowitz TS. Five factors that guide attention in visual search. Nat Hum Behav 2017;1(3).
Sharpe LT, Stockman A, Jagle H, Knau H, Nathans J. L, M and L‐M hybrid cone photopigments in man: Deriving lambda max from flicker photometric spectral sensitivities. Vision Res 1999;39(21):3513‐3525.
Neitz J, Neitz M. The genetics of normal and defective color vision. Vision Res 2011;51(7):633‐651.
Illumination CICo. Colorimetry‐Part 4:CIE1976 L*a*b colour space. ISO/CIE 11664‐4:2019. Volume 2024. iso.org/obp/ui/en/#iso:std:iso-cie:11664:-4:ed-1:v1:en: ISO2019.
Stauffer R, Mayr GJ, Dabernig M, Zeileis A. Somewhere over the rainbow: How to make effective use of colors in meteorological visualizations. Bull Am Meteorol Soc 2015;96(2):203‐216.
Szafir DA. Modeling color difference for visualization design. IEEE Trans Vis Comput Graph 2018;24(1):392‐401.
Bujack R, Turton TL, Samsel F, Ware C, Rogers DH, Ahrens J. The good, the bad, and the ugly: A theoretical framework for the assessment of continuous colormaps. IEEE Trans Vis Comput Graph 2018;24(1):923‐933.
Ware C, Turton TL, Bujack R, Samsel F, Shrivastava P, Rogers DH. Measuring and modeling the feature detection threshold functions of colormaps. IEEE Trans Vis Comput Graph 2019;25(9):2777‐2790.
Borland D, Taylor MR 2nd. Rainbow color map (still) considered harmful. IEEE Comput Graph Appl 2007;27(2):14‐17.
Meier BJ, Spalter AM, Karelitz DB. Interactive color palette tools. IEEE Comput Graph Appl 2004;24(3):64‐72.
Nunez JR, Anderton CR, Renslow RS. Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data. PLoS One 2018;13(7):e0199239.
Bydder GM, Steiner RE, Young IR, et al. Clinical NMR imaging of the brain: 140 cases. AJR Am J Roentgenol 1982;139(2):215‐236.
Weingartner S, Messner NM, Budjan J, et al. Myocardial T(1)‐mapping at 3T using saturation‐recovery: Reference values, precision and comparison with MOLLI. J Cardiovasc Magn Reson 2016;18(1):84.
Sollmann N, Schandelmaier P, Weidlich D, et al. Patients with episodic migraine show increased T2 values of the trapezius muscles ‐ an investigation by quantitative high‐resolution magnetic resonance imaging. Cephalalgia 2021;41(8):934‐942.
Sollmann N, Mathonia N, Weidlich D, et al. Quantitative magnetic resonance imaging of the upper trapezius muscles ‐ assessment of myofascial trigger points in patients with migraine. J Headache Pain 2019;20(1):8.
Henninger B, Kremser C, Rauch S, et al. Evaluation of MR imaging with T1 and T2* mapping for the determination of hepatic iron overload. Eur Radiol 2012;22(11):2478‐2486.
He T. Cardiovascular magnetic resonance T2* for tissue iron assessment in the heart. Quant Imaging Med Surg 2014;4(5):407‐412.
Kellman P, Bandettini WP, Mancini C, Hammer‐Hansen S, Hansen MS, Arai AE. Characterization of myocardial T1‐mapping bias caused by intramyocardial fat in inversion recovery and saturation recovery techniques. J Cardiovasc Magn Reson 2015;17(1):33.
Ferreira VM, Holloway CJ, Piechnik SK, Karamitsos TD, Neubauer S. Is it really fat? Ask a T1‐map. Eur Heart J Cardiovasc Imaging 2013;14(11):1060.
Im KC, Choi IS, Ryu JS, Eo GS, Kim JS, Moon DH. PET/CT fusion viewing software for use with picture archiving and communication systems. J Digit Imaging 2010;23(6):732‐743.
Christen M, Vitacco DA, Huber L, Harboe J, Fabrikant SI, Brugger P. Colorful brains: 14 years of display practice in functional neuroimaging. Neuroimage 2013;73:30‐39.
Singh N, Zabbarova I, Ikeda Y, et al. Virtual measurements of paracellular permeability and chronic inflammation via color coded pixel‐wise T(1) mapping. Am J Physiol Renal Physiol 2020;319(3):F506‐F514.
Imaoka I, Nakatsuka T, Araki T, et al. T2* relaxometry mapping of the uterine zones. Acta Radiol 2012;53(4):473‐477.
Ramasamy SK, Roudi R, Morakote W, et al. Measurement of tumor T2* relaxation times after iron oxide nanoparticle administration. J Vis Exp 2023;195.
Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval‐Jeantet M. MR imaging of intravoxel incoherent motions: Application to diffusion and perfusion in neurologic disorders. Radiology 1986;161(2):401‐407.
Taylor DG, Bushell MC. The spatial mapping of translational diffusion coefficients by the NMR imaging technique. Phys Med Biol 1985;30(4):345‐349.
Merboldt KD, Hanicke W, Frahm J. Diffusion imaging using stimulated echoes. Magn Reson Med 1991;19(2):233‐239.
Schlaug G, Siewert B, Benfield A, Edelman RR, Warach S. Time course of the apparent diffusion coefficient (ADC) abnormality in human stroke. Neurology 1997;49(1):113‐119.
Jeurissen B, Descoteaux M, Mori S, Leemans A. Diffusion MRI fiber tractography of the brain. NMR Biomed 2019;32(4):e3785.
Bozgeyik Z, Onur MR, Poyraz AK. The role of diffusion weighted magnetic resonance imaging in oncologic settings. Quant Imaging Med Surg 2013;3(5):269‐278.
Bernarding J, Braun J, Hohmann J, et al. Histogram‐based characterization of healthy and ischemic brain tissues using multiparametric MR imaging including apparent diffusion coefficient maps and relaxometry. Magn Reson Med 2000;43(1):52‐61.
Sullivan JJ, Zekelman LR, Zhang F, et al. Directionally encoded color track density imaging in brain tumor patients: A potential application to neuro‐oncology surgical planning. Neuroimage Clin 2023;38:103412.
Field AS, Wu YC, Alexander AL. Principal diffusion direction in peritumoral fiber tracts: Color map patterns and directional statistics. Ann N Y Acad Sci 2005;1064:193‐201.
Stieltjes B, Kaufmann WE, van Zijl PC, et al. Diffusion tensor imaging and axonal tracking in the human brainstem. Neuroimage 2001;14(3):723‐735.
Jellison BJ, Field AS, Medow J, Lazar M, Salamat MS, Alexander AL. Diffusion tensor imaging of cerebral white matter: A pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR Am J Neuroradiol 2004;25(3):356‐369.
Alexander AL, Hurley SA, Samsonov AA, et al. Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain Connect 2011;1(6):423‐446.
Camins A, Naval‐Baudin P, Majos C, et al. Inferior fronto‐occipital fascicle displacement in temporoinsular gliomas using diffusion tensor imaging. J Neuroimaging 2022;32(4):638‐646.
Sollmann N, Zhang H, Fratini A, et al. Risk assessment by presurgical tractography using navigated TMS maps in patients with highly motor‐ or language‐eloquent brain tumors. Cancers (Basel) 2020;12(5).
Zhylka A, Sollmann N, Kofler F, et al. Reconstruction of the corticospinal tract in patients with motor‐eloquent high‐grade gliomas using multilevel fiber tractography combined with functional motor cortex mapping. AJNR Am J Neuroradiol 2023;44(3):283‐290.
Zhylka A, Sollmann N, Kofler F, et al. Tracking the Corticospinal Tract in Patients With High‐Grade Glioma: Clinical Evaluation of Multi‐Level Fiber Tracking and Comparison to Conventional Deterministic Approaches. Front Oncol 2021;11:761169.
Rosazza C, Deleo F, D'Incerti L, et al. Tracking the re‐organization of motor functions after disconnective surgery: A longitudinal fMRI and DTI study. Front Neurol 2018;9:400.
Negwer C, Beurskens E, Sollmann N, et al. Loss of subcortical language pathways correlates with surgery‐related aphasia in patients with brain tumor: An investigation via repetitive navigated transcranial magnetic stimulation‐based diffusion tensor imaging fiber tracking. World Neurosurg 2018;111:e806‐e818.
Ippolito D, Drago SG, Pecorelli A, et al. Role of dynamic perfusion magnetic resonance imaging in patients with local advanced rectal cancer. World J Gastroenterol 2020;26(20):2657‐2668.
Ippolito D, Lombardi S, Talei Franzesi C, et al. Dynamic contrast‐enhanced MR with quantitative perfusion analysis of small bowel in vascular assessment between inflammatory and fibrotic lesions in Crohn's disease: A feasibility study. Contrast Media Mol Imaging 2019;2019:1767620.
Liang P, Yang Y, Chen W, Duan Y, Wang H, Wang X. Magnetic resonance perfusion imaging evaluation in perfusion abnormalities of the cerebellum after supratentorial unilateral hyperacute cerebral infarction. Neural Regen Res 2012;7(12):906‐911.
Law M, Yang S, Babb JS, et al. Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast‐enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 2004;25(5):746‐755.
Hsu LY, Groves DW, Aletras AH, Kellman P, Arai AE. A quantitative pixel‐wise measurement of myocardial blood flow by contrast‐enhanced first‐pass CMR perfusion imaging: Microsphere validation in dogs and feasibility study in humans. JACC Cardiovasc Imaging 2012;5(2):154‐166.
Sayin ES, Sobczyk O, Poublanc J, Mikulis DJ, Fisher JA, Duffin J. Transfer function analysis assesses resting cerebral perfusion metrics using hypoxia‐induced deoxyhemoglobin as a contrast agent. Front Physiol 2023;14:1167857.
Rosenkrantz AB, Sabach A, Babb JS, Matza BW, Taneja SS, Deng FM. Prostate cancer: Comparison of dynamic contrast‐enhanced MRI techniques for localization of peripheral zone tumor. AJR Am J Roentgenol 2013;201(3):W471‐W478.
Albano D, Bruno F, Agostini A, et al. Dynamic contrast‐enhanced (DCE) imaging: State of the art and applications in whole‐body imaging. Jpn J Radiol 2022;40(4):341‐366.
Kuhl CK, Schrading S, Strobel K, Schild HH, Hilgers RD, Bieling HB. Abbreviated breast magnetic resonance imaging (MRI): First postcontrast subtracted images and maximum‐intensity projection‐a novel approach to breast cancer screening with MRI. J Clin Oncol 2014;32(22):2304‐2310.
Guglielmo FF, Venkatesh SK, Mitchell DG. Liver MR Elastography technique and image interpretation: Pearls and pitfalls. Radiographics 2019;39(7):1983‐2002.
Ozturk A, Olson MC, Samir AE, Venkatesh SK. Liver fibrosis assessment: MR and US elastography. Abdom Radiol (NY) 2022;47(9):3037‐3050.
Patel BK, Pepin K, Brandt KR, et al. Association of breast cancer risk, density, and stiffness: Global tissue stiffness on breast MR elastography (MRE). Breast Cancer Res Treat 2022;194(1):79‐89.
Lilaj L, Herthum H, Meyer T, et al. Inversion‐recovery MR elastography of the human brain for improved stiffness quantification near fluid‐solid boundaries. Magn Reson Med 2021;86(5):2552‐2561.
Drakonaki EE, Allen GM. Magnetic resonance imaging, ultrasound and real‐time ultrasound elastography of the thigh muscles in congenital muscle dystrophy. Skeletal Radiol 2010;39(4):391‐396.
Idilman IS, Li J, Yin M, Venkatesh SK. MR elastography of liver: Current status and future perspectives. Abdom Radiol (NY) 2020;45(11):3444‐3462.
Feng Y, Murphy MC, Hojo E, Li F, Roberts N. Magnetic resonance elastography in the study of neurodegenerative diseases. J Magn Reson Imaging 2023;59:82‐96.
Weingartner S, Desmond KL, Obuchowski NA, et al. Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group. Magn Reson Med 2022;87(3):1184‐1206.
Korinek R, Pfleger L, Eckstein K, et al. Feasibility of hepatic fat quantification using proton density fat fraction by multi‐echo chemical‐shift‐encoded MRI at 7T. Front Phys 2021;9:665562.
Loomba R, Kayali Z, Noureddin M, et al. GS‐0976 reduces hepatic steatosis and fibrosis markers in patients with nonalcoholic fatty liver disease. Gastroenterology 2018;155(5):1463‐1473.e6.
Wildman‐Tobriner B, Middleton MM, Moylan CA, et al. Association between magnetic resonance imaging‐proton density fat fraction and liver histology features in patients with nonalcoholic fatty liver disease or nonalcoholic steatohepatitis. Gastroenterology 2018;155(5):1428‐1435.e2.
Jung M, Rospleszcz S, Loffler MT, et al. Association of lumbar vertebral bone marrow and paraspinal muscle fat composition with intervertebral disc degeneration: 3T quantitative MRI findings from the population‐based KORA study. Eur Radiol 2023;33(3):1501‐1512.
Sollmann N, Bonnheim NB, Joseph GB, et al. Paraspinal muscle in chronic low Back pain: Comparison between standard parameters and chemical shift encoding‐based water‐fat MRI. J Magn Reson Imaging 2022;56:1600‐1608.
Sollmann N, Loffler MT, Kronthaler S, et al. MRI‐based quantitative osteoporosis imaging at the spine and femur. J Magn Reson Imaging 2021;54(1):12‐35.
Albakheet SS, Yoon H, Shin HJ, Koh H, Kim S, Lee MJ. Bone marrow fat change in pediatric patients with non‐alcoholic fatty liver disease. PLoS One 2020;15(6):e0234096.
Crameri F. Scientific color maps. Zenodo 2023. https://doi.org/10.5281/zenodo.1243862.
Fuderer M. Resources for application of Lipari and Navia color maps. Zenodo 2023. https://doi.org/10.5281/zenodo.8268885.