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Abstract #0872

Fully automated registration of cross-sectional liver images using fully convolutional and affine transformation networks

Kyle A Hasenstab1, Guilherme M Cunha1, Kang Wang1, Brian Hurt1, Alexandra Schlein1, Timoteo Delgado1, Ryan L Brunsing2, Armin Schwartzman3, Kathryn Fowler1, Albert Hsiao1, and Claude B Sirlin1

1Radiology, University of California, San Diego, San Diego, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3Family Medicine and Public Health, University of California, San Diego, San Diego, CA, United States

Proper spatial alignment of anatomical landmarks during and between liver imaging exams is challenging due to the dynamic morphology of the liver. Liver-focused registration algorithms have been developed but are typically semiautomatic. We propose a fully-automated pipeline for affine-based registration of inter- and intra-exam liver images and assess performance on clinical liver MRI exams at 1.5T and 3T. The proposed pipeline achieved comparable or superior accuracy and scalability to that reported for previously proposed algorithms. Retrospective image review by an expert abdominal radiologist confirmed subjective improvement in anatomic registration and lesion co-localization. Proof of concept of multimodal scalability was demonstrated.

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