Statistical Biomechanical Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions.

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 Imaging

authors

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 P

doi

10.1109/TMI.2015.2443978

subject

Has Abstract

pub_date

2015-12-01 00:00:00

pages

2535-49

issue

12

eissn

0278-0062

issn

1558-254X

journal_volume

34

pub_type

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