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Proceedings Paper

Non-rigid MRI-CT image registration with unsupervised deepdlearning-based deformation prediction
Author(s): Yabo Fu; Yang Lei; Jun Zhou; Tonghe Wang; Ashesh Jani; Pretesh Patel; Hui Mao; Walter J. Curran; Tian Liu; Xiaofeng Yang
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Paper 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.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131329 (10 March 2020); doi: 10.1117/12.2549317
Show Author Affiliations
Yabo Fu, Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Jun Zhou, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Ashesh Jani, Emory Univ. (United States)
Pretesh Patel, Emory Univ. (United States)
Hui Mao, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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