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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. Conventi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5255548/ https://www.ncbi.nlm.nih.gov/pubmed/28112222 http://dx.doi.org/10.1038/srep41107 |
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author | Kather, Jakob Nikolas Weidner, Anja Attenberger, Ulrike Bukschat, Yannick Weis, Cleo-Aron Weis, Meike Schad, Lothar R. Zöllner, Frank Gerrit |
author_facet | Kather, Jakob Nikolas Weidner, Anja Attenberger, Ulrike Bukschat, Yannick Weis, Cleo-Aron Weis, Meike Schad, Lothar R. Zöllner, Frank Gerrit |
author_sort | Kather, Jakob Nikolas |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5255548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-52555482017-01-24 Color-coded visualization of magnetic resonance imaging multiparametric maps Kather, Jakob Nikolas Weidner, Anja Attenberger, Ulrike Bukschat, Yannick Weis, Cleo-Aron Weis, Meike Schad, Lothar R. Zöllner, Frank Gerrit Sci Rep Article 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. Nature Publishing Group 2017-01-23 /pmc/articles/PMC5255548/ /pubmed/28112222 http://dx.doi.org/10.1038/srep41107 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Kather, Jakob Nikolas Weidner, Anja Attenberger, Ulrike Bukschat, Yannick Weis, Cleo-Aron Weis, Meike Schad, Lothar R. Zöllner, Frank Gerrit Color-coded visualization of magnetic resonance imaging multiparametric maps |
title | Color-coded visualization of magnetic resonance imaging multiparametric maps |
title_full | Color-coded visualization of magnetic resonance imaging multiparametric maps |
title_fullStr | Color-coded visualization of magnetic resonance imaging multiparametric maps |
title_full_unstemmed | Color-coded visualization of magnetic resonance imaging multiparametric maps |
title_short | Color-coded visualization of magnetic resonance imaging multiparametric maps |
title_sort | color-coded visualization of magnetic resonance imaging multiparametric maps |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5255548/ https://www.ncbi.nlm.nih.gov/pubmed/28112222 http://dx.doi.org/10.1038/srep41107 |
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