Skeleton Graph-based Ultrasound-CT Non-rigid Registration

• URL: http://arxiv.org/abs/2305.08228v1
• Date: Sun, 14 May 2023 19:21:43 GMT
• Title: Skeleton Graph-based Ultrasound-CT Non-rigid Registration
• Authors: Zhongliang Jiang, Xuesong Li, Chenyu Zhang, Yuan Bi, Walter Stechele, Nassir Navab
• Abstract summary: We propose a skeleton graph-based non-rigid registration to adapt patient-specific properties using subcutaneous bone surface features. To validate the proposed approach, we manually extract the US cartilage point cloud from one volunteer and seven CT cartilage point clouds from different patients.
• Score: 40.40844749663152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous ultrasound (US) scanning has attracted increased attention, and it has been seen as a potential solution to overcome the limitations of conventional US examinations, such as inter-operator variations. However, it is still challenging to autonomously and accurately transfer a planned scan trajectory on a generic atlas to the current setup for different patients, particularly for thorax applications with limited acoustic windows. To address this challenge, we proposed a skeleton graph-based non-rigid registration to adapt patient-specific properties using subcutaneous bone surface features rather than the skin surface. To this end, the self-organization mapping is successively used twice to unify the input point cloud and extract the key points, respectively. Afterward, the minimal spanning tree is employed to generate a tree graph to connect all extracted key points. To appropriately characterize the rib cartilage outline to match the source and target point cloud, the path extracted from the tree graph is optimized by maximally maintaining continuity throughout each rib. To validate the proposed approach, we manually extract the US cartilage point cloud from one volunteer and seven CT cartilage point clouds from different patients. The results demonstrate that the proposed graph-based registration is more effective and robust in adapting to the inter-patient variations than the ICP (distance error mean/SD: 5.0/1.9 mm vs 8.6/6.7 mm on seven CTs).

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