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Greg Padiasek

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Proceedings Article | 3 April 2023 Poster + Paper

Deformable registration of 3D CT images with partial liver

Giovanni Palma, Greg Padiasek, Arkadiusz Sitek, Paul Dufort, Pedro Esquinas, Marc-Michel Rohé

Proceedings Volume 12464, 124642V (2023) https://doi.org/10.1117/12.2654220

KEYWORDS: Liver, Image registration, Computed tomography, Deformation, 3D image processing, Tumors, Deep learning, Image segmentation, Image processing

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Multiphase contrast-enhanced computed tomography (CT) is a popular method used by radiologists to diagnose patients with liver diseases. The analysis of multiphase images may involve image segmentation and registration. While the latter can be addressed using traditional registration techniques, it can be proven difficult when dealing with sub-optimal acquisitions. In real conditions, a registration process not only needs to accommodate patient’s motion occurring between acquisitions but also the variation in the scanner field of view that changes between the acquisitions. Actually, each image acquisition acquired during an individual exam may cover different portions of the liver, resulting in full or partial liver scans. Partial liver scans present a challenge to registration methods that expect to see the same portion of the body in the input data. Such methods would tend to register partial liver volumes to full liver volumes, and as a result, misplace locations of internal liver landmarks. In this work we present a method that registers multiphase CT scans with partial and full liver while ignoring portions of liver that are represented only in one of the scans. We assess its performance and discuss its capacity to improve tracking of liver lesions across phases.

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