Non-rigid MRI-CT image registration with unsupervised deepdlearning-based deformation prediction

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

doi:10.1117/12.2549317

10 March 2020 Non-rigid MRI-CT image registration with unsupervised deepdlearning-based deformation prediction

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

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131329 (2020) https://doi.org/10.1117/12.2549317
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

In this study, we propose to use an unsupervised deep learning-based method to directly register the MRI to CT with the help of synthetic MRI (sMRI). Our synthesis results showed that CT-generated sMRI could partially restore soft-tissue contrast in the pelvic region, which helps to improve the image registration accuracy near the prostate, rectum and bladder. Additionally, sMRI has similar intensity value to the true MRI which make it easier to register the images. The registration network was trained in an unsupervised manner, meaning ground truth DVF is not required. The trained network can predict the deformation vector field (DVF) in a single shot. After registration, the structural similarity index metric (SSIM) and peak signal to noise ratio (PSNR) between the deformed sMRI and the fixed MRI were on average 0.79 and 25.15 dB respectively.

Citation Download Citation

Yabo Fu, Yang Lei, Jun Zhou, Tonghe Wang, Ashesh Jani, Pretesh Patel, Hui Mao, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Non-rigid MRI-CT image registration with unsupervised deepdlearning-based deformation prediction", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131329 (10 March 2020); https://doi.org/10.1117/12.2549317

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