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Proceedings Paper

Intensity non-uniformity correction in MR imaging using deep learning
Author(s): Xianjin Dai; Yang Lei; Yingzi Liu; Tonghe Wang; Walter J. Curran; Pretesh Patel; Tian Liu; Xiaofeng Yang
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Paper Abstract

Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative MR image analysis in daily clinical practice. Although having no severe impact on visual diagnosis, the INU artifact, can highly degrade the performance of automatic quantitative analysis such as feature extraction and radiomics. In this study, we present a deep learning-based approach for MR image INU correction. Particularly, a cycle generative adversarial network (GAN) was trained and tested using a cohort of 25 abdominal patients with T1-weighted MR INU images. The results show that our cycle GAN-based method achieves a higher accuracy than the most commonly used algorithm N4ITK, and highly speeds up the correction without any unintuitive parameter tuning process.

Paper Details

Date Published: 28 February 2020
PDF: 8 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131727 (28 February 2020); doi: 10.1117/12.2549017
Show Author Affiliations
Xianjin Dai, Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Yingzi Liu, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Pretesh Patel, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)

Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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