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

Siavash Khallaghi, Parvin Mousavi, Ren Hui Gong, Sean Gill, Jonathan Boisvert, Gabor Fichtinger, David Pichora, Dan Borschneck, Purang Abolmaesumi

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages68-75
Number of pages8
Volume13
EditionPt 2
StatePublished - 2010
Externally publishedYes

Fingerprint

Statistical Models
Spine
Needles
Fiducial Markers
Anatomic Models
Spinal Injections
Cone-Beam Computed Tomography
Atlases
Imagery (Psychotherapy)
Safety

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Khallaghi, S., Mousavi, P., Gong, R. H., Gill, S., Boisvert, J., Fichtinger, G., ... Abolmaesumi, P. (2010). Registration of a statistical shape model of the lumbar spine to 3D ultrasound images. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 2 ed., Vol. 13, pp. 68-75)

Registration of a statistical shape model of the lumbar spine to 3D ultrasound images. / Khallaghi, Siavash; Mousavi, Parvin; Gong, Ren Hui; Gill, Sean; Boisvert, Jonathan; Fichtinger, Gabor; Pichora, David; Borschneck, Dan; Abolmaesumi, Purang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 2. ed. 2010. p. 68-75.

Research output: Chapter in Book/Report/Conference proceedingChapter

Khallaghi, S, Mousavi, P, Gong, RH, Gill, S, Boisvert, J, Fichtinger, G, Pichora, D, Borschneck, D & Abolmaesumi, P 2010, Registration of a statistical shape model of the lumbar spine to 3D ultrasound images. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 edn, vol. 13, pp. 68-75.
Khallaghi S, Mousavi P, Gong RH, Gill S, Boisvert J, Fichtinger G et al. Registration of a statistical shape model of the lumbar spine to 3D ultrasound images. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 ed. Vol. 13. 2010. p. 68-75
Khallaghi, Siavash ; Mousavi, Parvin ; Gong, Ren Hui ; Gill, Sean ; Boisvert, Jonathan ; Fichtinger, Gabor ; Pichora, David ; Borschneck, Dan ; Abolmaesumi, Purang. / Registration of a statistical shape model of the lumbar spine to 3D ultrasound images. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 13 Pt 2. ed. 2010. pp. 68-75
@inbook{68a9972a89bb402bbd9bad42f9d158f3,
title = "Registration of a statistical shape model of the lumbar spine to 3D ultrasound images.",
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.",
author = "Siavash Khallaghi and Parvin Mousavi and Gong, {Ren Hui} and Sean Gill and Jonathan Boisvert and Gabor Fichtinger and David Pichora and Dan Borschneck and Purang Abolmaesumi",
year = "2010",
language = "English (US)",
volume = "13",
pages = "68--75",
booktitle = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",
edition = "Pt 2",

}

TY - CHAP

T1 - Registration of a statistical shape model of the lumbar spine to 3D ultrasound images.

AU - Khallaghi, Siavash

AU - Mousavi, Parvin

AU - Gong, Ren Hui

AU - Gill, Sean

AU - Boisvert, Jonathan

AU - Fichtinger, Gabor

AU - Pichora, David

AU - Borschneck, Dan

AU - Abolmaesumi, Purang

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84866559110&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866559110&partnerID=8YFLogxK

M3 - Chapter

C2 - 20879300

AN - SCOPUS:84866559110

VL - 13

SP - 68

EP - 75

BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

ER -