Deep learning-based deformable registration of dynamic contrast enhanced MR images of the kidney

James Huang; Junyu Guo; Ivan Pedrosa; Baowei Fei

doi:10.1117/12.2611768

4 April 2022 Deep learning-based deformable registration of dynamic contrast enhanced MR images of the kidney

James Huang, Junyu Guo, Ivan Pedrosa, Baowei Fei

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 1203410 (2022) https://doi.org/10.1117/12.2611768
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

James Huang, Junyu Guo, Ivan Pedrosa, and Baowei Fei "Deep learning-based deformable registration of dynamic contrast enhanced MR images of the kidney", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 1203410 (4 April 2022); https://doi.org/10.1117/12.2611768

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