Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

Abstract Background Liver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registration can improve image interpretation and lesion co-localization. We assessed the performance of a convolutional neural network algorithm to reg...

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Bibliographic Details
Main Authors: Kyle A. Hasenstab, Guilherme Moura Cunha, Atsushi Higaki, Shintaro Ichikawa, Kang Wang, Timo Delgado, Ryan L. Brunsing, Alexandra Schlein, Leornado Kayat Bittencourt, Armin Schwartzman, Katie J. Fowler, Albert Hsiao, Claude B. Sirlin
Format: Article
Language:English
Published: SpringerOpen 2019-10-01
Series:European Radiology Experimental
Subjects:
Gadolinium ethoxybenzyl DTPA
Image processing (computer assisted)
Liver
Magnetic resonance imaging
Neural networks (computer)
Online Access:http://link.springer.com/article/10.1186/s41747-019-0120-7

Internet

http://link.springer.com/article/10.1186/s41747-019-0120-7

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