Color-coded visualization of magnetic resonance imaging multiparametric maps

被引:12
作者
Kather, Jakob Nikolas [1 ,2 ]
Weidner, Anja [3 ]
Attenberger, Ulrike [3 ]
Bukschat, Yannick [2 ]
Weis, Cleo-Aron [4 ]
Weis, Meike [3 ]
Schad, Lothar R. [2 ]
Zoellner, Frank Gerrit [2 ]
机构
[1] Univ Heidelberg Hosp, Dept Med Oncol & Internal Med 6, Natl Ctr Tumor Dis, Heidelberg, Germany
[2] Heidelberg Univ, Med Fac Mannheim, Comp Assisted Clin Med, Mannheim, Germany
[3] Heidelberg Univ, Univ Med Ctr Mannheim, Inst Clin Radiol & Nucl Med, Mannheim, Germany
[4] Heidelberg Univ, Univ Med Ctr Mannheim, Inst Pathol, Mannheim, Germany
来源
SCIENTIFIC REPORTS | 2017年 / 7卷
关键词
COMPUTER-AIDED DIAGNOSIS; VISUAL-SEARCH; DATA SYSTEM; MRI; CLASSIFICATION; DISEASE; FUSION; CT;
D O I
10.1038/srep41107
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:11
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