Abstract:
:A common challenge when performing surface-based registration of images is ensuring that the surfaces accurately represent consistent anatomical boundaries. Image segmentation may be difficult in some regions due to either poor contrast, low slice resolution, or tissue ambiguities. To address this, we present a novel non-rigid surface registration method designed to register two partial surfaces, capable of ignoring regions where the anatomical boundary is unclear. Our probabilistic approach incorporates prior geometric information in the form of a statistical shape model (SSM), and physical knowledge in the form of a finite element model (FEM). We validate results in the context of prostate interventions by registering pre-operative magnetic resonance imaging (MRI) to 3D transrectal ultrasound (TRUS). We show that both the geometric and physical priors significantly decrease net target registration error (TRE), leading to TREs of 2.35 ± 0.81 mm and 2.81 ± 0.66 mm when applied to full and partial surfaces, respectively. We investigate robustness in response to errors in segmentation, varying levels of missing data, and adjusting the tunable parameters. Results demonstrate that the proposed surface registration method is an efficient, robust, and effective solution for fusing data from multiple modalities.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Khallaghi S,Sánchez CA,Rasoulian A,Nouranian S,Romagnoli C,Abdi H,Chang SD,Black PC,Goldenberg L,Morris WJ,Spadinger I,Fenster A,Ward A,Fels S,Abolmaesumi Pdoi
10.1109/TMI.2015.2443978subject
Has Abstractpub_date
2015-12-01 00:00:00pages
2535-49issue
12eissn
0278-0062issn
1558-254Xjournal_volume
34pub_type
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