期刊
出版社
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2611768
关键词
deformable image registration; deep learning; convolutional neural network ( CNN); dynamic contrast enhanced (DCE) MRI; kidney
资金
- U.S. National Institutes of Health (NIH) [R01CA156775, R01CA204254, R01HL140325, R01CA154475, R21CA231911]
- Cancer Prevention and Research Institute of Texas (CPRIT) [RP190588]
- Career Enhancement Program - UT Southwestern SPORE
Respiratory motion is a major source of error in quantitative analysis of MRI data. This study proposes a deep learning approach to correct motion effects and applies it to kidney MRI imaging applications.
Respiratory motion is a major contributor to bias in quantitative analysis of magnetic resonance imaging (MRI) acquisitions. Deformable registration of three-dimensional (3D) dynamic contrast-enhanced (DCE) MRI data improves estimation of kidney kinetic parameters. In this study, we proposed a deep learning approach with two steps: a convolutional neural network (CNN) based affine registration network, followed by a U-Net trained for deformable registration between two MR images. The proposed registration method was applied successively across consecutive dynamic phases of the 3D DCE-MRI dataset to reduce motion effects in the different kidney compartments (i.e., cortex, medulla). Successful reduction in the motion effects caused by patient respiration during image acquisition allows for improved kinetic analysis of the kidney. Original and registered images were analyzed and compared using dynamic intensity curves of the kidney compartments, target registration error of anatomical markers, image subtraction, and simple visual assessment. The proposed deep learning-based approach to correct motion effects in abdominal 3D DCE-MRI data can be applied to various kidney MR imaging applications.
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