Intensity non-uniformity correction in MR imaging using deep learning

Xianjin Dai; Yang Lei; Yingzi Liu; Tonghe Wang; Walter J. Curran; Pretesh Patel; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2549017

28 February 2020 Intensity non-uniformity correction in MR imaging using deep learning

Xianjin Dai, Yang Lei, Yingzi Liu, Tonghe Wang, Walter J. Curran, Pretesh Patel, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1131727 (2020) https://doi.org/10.1117/12.2549017
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Citation Download Citation

Xianjin Dai, Yang Lei, Yingzi Liu, Tonghe Wang, Walter J. Curran, Pretesh Patel, Tian Liu, and Xiaofeng Yang "Intensity non-uniformity correction in MR imaging using deep learning", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131727 (28 February 2020); https://doi.org/10.1117/12.2549017

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