A learning-based nonrigid MRI-CBCT image registration method for MRI-guided prostate cancer radiotherapy

Yabo Fu; Yang Lei; Tonghe Wang; Pretesh Patel; Ashesh B. Jani; Hui Mao; Walter J. Curran; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2580786

15 February 2021 A learning-based nonrigid MRI-CBCT image registration method for MRI-guided prostate cancer radiotherapy

Yabo Fu, Yang Lei, Tonghe Wang, Pretesh Patel, Ashesh B. Jani, Hui Mao, Walter J. Curran, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116001J (2021) https://doi.org/10.1117/12.2580786
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Radiation dose escalation to the dominant intraprostatic lesions in prostate cancer could improve tumor control. However, it is difficult to identify the dominant intraprostatic lesions on CT images. Multiparametric MRI has superior soft tissue contrast and is often used to detect the intraprostatic lesions. In this study, we developed a deep learning-based point cloud matching network to register the multiparametric MRI to the CBCT images for dominant lesion identification for prostate cancer radiotherapy. Prostate in both the CBCT and MRI was first automatically contoured and then meshed in to point clouds. A point cloud matching network was trained using point cloud pairs that were generated using finite element analysis. The trained network was able to perform MRI-CBCT prostate image registration with inherent biomechanical constraints. The mean and standard deviation of our method were 0.93±0.01, 1.66±0.10mm and 2.68±1.91mm for Dice similarity coefficient, mean surface distance and target registration error, respectively.

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Yabo Fu, Yang Lei, Tonghe Wang, Pretesh Patel, Ashesh B. Jani, Hui Mao, Walter J. Curran, Tian Liu, and Xiaofeng Yang "A learning-based nonrigid MRI-CBCT image registration method for MRI-guided prostate cancer radiotherapy", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116001J (15 February 2021); https://doi.org/10.1117/12.2580786

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