Deep learning-based deformable MRI-CBCT registration of male pelvic region

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

doi:10.1117/12.2581096

15 February 2021 Deep learning-based deformable MRI-CBCT registration of male pelvic region

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

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115970H (2021) https://doi.org/10.1117/12.2581096
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

In this study, we propose a novel unsupervised deep learning-based method to register pelvic MRI and CBCT images. No ground truth deformation vector field (DVF) is needed during training. To perform registration between CBCT and MRI, a self-similarity image similarity loss, called as self-correlation descriptor, is used as loss function to learn the trainable parameters in the unsupervised deep neural networks. After training, for a new patient’s CBCT and MRI, the deformed MRI is obtained via first feeding the MRI and CBCT into the unsupervised deep neural networks to derive the DVF, then deformed via spatial transformation on MRI and DVF. Our results show that the proposed method has outperformed manual rigid registration. Target registration error calculated between CBCT and deformed MRI is used for evaluation. The average TRE is 2.95±0.94 mm among the 20 prostate patients we retrospectively investigated. The proposed method has great potential in providing accurate image registration and potentially facilitating adaptive radiation therapy by multi-imaging modality.

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Shadab Momin, Yang Lei, Tonghe Wang, Yabo Fu, Pretesh Patel, Ashesh B. Jani, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Deep learning-based deformable MRI-CBCT registration of male pelvic region", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970H (15 February 2021); https://doi.org/10.1117/12.2581096

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KEYWORDS

Image registration

Magnetic resonance imaging

Neural networks

Prostate cancer

Radiotherapy

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