Color-coded visualization of magnetic resonance imaging multiparametric maps

Multiparametric magnetic resonance imaging (mpMRI) data are emergingly used in the clinic e.g. for the diagnosis of prostate cancer. In contrast to conventional MR imaging data, multiparametric data typically include functional measurements such as diffusion and perfusion imaging sequences. Conventionally, these measurements are visualized with a one-dimensional color scale, allowing only for one-dimensional information to be encoded. Yet, human perception places visual information in a three-dimensional color space. In theory, each dimension of this space can be utilized to encode visual information. We addressed this issue and developed a new method for tri-variate color-coded visualization of mpMRI data sets. We showed the usefulness of our method in a preclinical and in a clinical setting: In imaging data of a rat model of acute kidney injury, the method yielded characteristic visual patterns. In a clinical data set of N = 13 prostate cancer mpMRI data, we assessed diagnostic performance in a blinded study with N = 5 observers. Compared to conventional radiological evaluation, color-coded visualization was comparable in terms of positive and negative predictive values. Thus, we showed that human observers can successfully make use of the novel method. This method can be broadly applied to visualize different types of multivariate MRI data.

[1]  Michael F. McNitt-Gray,et al.  Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features , 2016, IEEE Transactions on Medical Imaging.

[2]  Manuel Menezes de Oliveira Neto,et al.  A Physiologically-based Model for Simulation of Color Vision Deficiency , 2009, IEEE Transactions on Visualization and Computer Graphics.

[3]  Algis J. Vingrya,et al.  Diagnosis of Defective Colour Vision , 1994 .

[4]  P. Lambin,et al.  Machine Learning methods for Quantitative Radiomic Biomarkers , 2015, Scientific Reports.

[5]  Lothar R. Schad,et al.  An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited , 2016, BMC Medical Imaging.

[6]  Carl-Fredrik Westin,et al.  Coloring of DT-MRI Fiber Traces Using Laplacian Eigenmaps , 2003, EUROCAST.

[7]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[8]  L. Schad,et al.  Functional imaging of acute kidney injury at 3 Tesla: investigating multiple parameters using DCE-MRI and a two-compartment filtration model. , 2015, Zeitschrift fur medizinische Physik.

[9]  H. Hricak,et al.  Interactive dedicated training curriculum improves accuracy in the interpretation of MR imaging of prostate cancer , 2010, European Radiology.

[10]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[11]  Lothar R. Schad,et al.  UMMPerfusion: an Open Source Software Tool Towards Quantitative MRI Perfusion Analysis in Clinical Routine , 2013, Journal of Digital Imaging.

[12]  Filippo Alberghina,et al.  Learning curve for coronary CT angiography: what constitutes sufficient training? , 2009, Radiology.

[13]  W. Cowan,et al.  Visual search for colour targets that are or are not linearly separable from distractors , 1996, Vision Research.

[14]  P. Choyke,et al.  Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies , 2008, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[15]  Axel Saalbach,et al.  Image fusion for dynamic contrast enhanced magnetic resonance imaging , 2004, Biomedical engineering online.

[16]  Jasjit S Suri,et al.  Carotid artery dissection on non-contrast CT: does color improve the diagnostic confidence? , 2014, European journal of radiology.

[17]  J. Kekäläinen,et al.  Lectin staining and flow cytometry reveals female-induced sperm acrosome reaction and surface carbohydrate reorganization , 2015, Scientific Reports.

[18]  Yi Wang,et al.  3D multi-parametric breast MRI segmentation using hierarchical support vector machine with coil sensitivity correction. , 2013, Academic radiology.

[19]  Joseph O. Deasy,et al.  Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images , 2015, Proceedings of the National Academy of Sciences.

[20]  Timo Ropinski,et al.  Survey of glyph-based visualization techniques for spatial multivariate medical data , 2011, Comput. Graph..

[21]  Michael D'Zmura,et al.  Color in visual search , 1991, Vision Research.

[22]  H. Huisman,et al.  Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging. , 2016, Radiology.

[23]  Jakob Nikolas Kather,et al.  New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images , 2015, PloS one.

[24]  B. Cole.  Assessment of inherited colour vision defects in clinical practice , 2007, Clinical & experimental optometry.

[25]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[26]  S. Ferrari,et al.  Author contributions , 2021 .

[27]  Sidra Nawaz,et al.  Computational pathology: Exploring the spatial dimension of tumor ecology. , 2016, Cancer letters.

[28]  Bradford A Moffat,et al.  Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[29]  D. Margolis,et al.  PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. , 2016, European urology.

[30]  Tom Kimpe,et al.  Increasing the Number of Gray Shades in Medical Display Systems—How Much is Enough? , 2007, Journal of Digital Imaging.

[31]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[32]  A. Marx,et al.  Value of multiparametric prostate MRI of the peripheral zone. , 2011, Zeitschrift fur medizinische Physik.

[33]  Angela M. Brown,et al.  Color Channels, Not Color Appearance or Color Categories, Guide Visual Search for Desaturated Color Targets , 2010, Psychological science.

[34]  Bevil R. Conway,et al.  Advances in Color Science: From Retina to Behavior , 2010, The Journal of Neuroscience.

[35]  J. Lebensohn.  Color in Business, Science, and Industry , 1952 .

[36]  Francesco Bianconi,et al.  Multi-class texture analysis in colorectal cancer histology , 2016, Scientific Reports.

[37]  J. Babb,et al.  Comparison of interreader reproducibility of the prostate imaging reporting and data system and likert scales for evaluation of multiparametric prostate MRI. , 2013, AJR. American journal of roentgenology.

[38]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[39]  Markus A. Maier,et al.  Color psychology: effects of perceiving color on psychological functioning in humans. , 2014, Annual review of psychology.

[40]  H. Huisman,et al.  Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance. , 2013, Radiology.