Registration of a statistical shape model of the lumbar spine to 3D ultrasound images

Authors:
Siavash Khallaghi;Parvin Mousavi;Ren Hui Gong;Sean Gill;Jonathan Boisvert;Gabor Fichtinger;David Pichora;Dan Borschneck;Purang Abolmaesumi
Affiliations:
Queen's University, Kingston, ON, Canada;Queen's University, Kingston, ON, Canada;Queen's University, Kingston, ON, Canada;Queen's University, Kingston, ON, Canada;National Research Council, Ottawa, ON, Canada;Queen's University, Kingston, ON, Canada;Kingston General Hospital, ON, Canada;Kingston General Hospital, ON, Canada;University of British Columbia, Vancouver, BC, Canada
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

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Abstract

Motivation: Spinal needle injections are technically demanding procedures. The use of ultrasound image guidance without prior CT and MR imagery promises to improve the efficacy and safety of these procedures in an affordable manner. Methodology: We propose to create a statistical shape model of the lumbar spine and warp this atlas to patient-specific ultrasound images during the needle placement procedure. From CT image volumes of 35 patients, statistical shape model of the L3 vertebra is built, including mean shape and main modes of variation. This shape model is registered to the ultrasound data by simultaneously optimizing the parameters of the model and its relative pose. Ground-truth data was established by printing 3D anatomical models of 3 patients using a rapid prototyping. CT and ultrasound data of these models were registered using fiducial markers. Results: Pairwise registration of the statistical shape model and 3D ultrasound images led to a mean target registration error of 3.4 mm, while 81% of all cases yielded clinically acceptable accuracy below the 3.5 mm threshold.