PET image reconstruction with anatomical prior using multiphase level set method

Jinxiu Liao, Jinyi Qi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Acquiring both anatomical and functional images during one scan, PET/CT systems improve the ability to distinguish pathology from normal uptake and to precisely localize abnormal loci. Researchers have investigated using anatomical boundary information in CT to regularize PET images. Here we propose a novel approach to maximum a posteriori (MAP) reconstruction of PET images with CT-based prior information. The image prior models both the smoothness of PET images and the similarity between functional boundaries in PET and anatomical boundaries in CT. Minimal smoothing is applied across functional boundaries to preserve shape edges. Level set functions are use to describe the anatomical boundaries from CT and to track the evolution of the functional boundaries in PET. The proposed method does not assume an exact match between PET and CT boundaries, but rather maximizes the similarity between the two boundaries, while allowing different region definition in the PET image. This is an important feature as mismatches between anatomical and functional boundaries have been observed in clinical images that could be caused either by inherent difference in the contrast mechanisms and/or subject motion. We conducted computer simulations to evaluate the performance of the proposed method. A digital phantom was built based on a PET/CT scan of a mouse. Two anatomical priors are obtained by modifying the segmented CT boundaries. The proposed method is compared with other methods including ML-EM, MAP with spatial-invariant and spatial-variant smoothing. The reconstructed images of the proposed method are locally smooth with sharp functional boundaries, while blurred boundaries appears in the results of other methods. The region of interest quantification study shows that the proposed method achieves less bias at the same noise level compared to the existing methods.

Original languageEnglish (US)
Title of host publicationIEEE Nuclear Science Symposium Conference Record
Pages4163-4168
Number of pages6
Volume6
DOIs
StatePublished - 2007
Event2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC - Honolulu, HI, United States
Duration: Oct 27 2007Nov 3 2007

Other

Other2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC
CountryUnited States
CityHonolulu, HI
Period10/27/0711/3/07

Fingerprint

Computerized tomography
Pathology
Image reconstruction
Computer simulation

Keywords

  • Anatomical prior
  • Image reconstruction
  • Level set method
  • PET/CT
  • Positron emission tomography
  • Shape prior

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Industrial and Manufacturing Engineering

Cite this

Liao, J., & Qi, J. (2007). PET image reconstruction with anatomical prior using multiphase level set method. In IEEE Nuclear Science Symposium Conference Record (Vol. 6, pp. 4163-4168). [4437036] https://doi.org/10.1109/NSSMIC.2007.4437036

PET image reconstruction with anatomical prior using multiphase level set method. / Liao, Jinxiu; Qi, Jinyi.

IEEE Nuclear Science Symposium Conference Record. Vol. 6 2007. p. 4163-4168 4437036.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liao, J & Qi, J 2007, PET image reconstruction with anatomical prior using multiphase level set method. in IEEE Nuclear Science Symposium Conference Record. vol. 6, 4437036, pp. 4163-4168, 2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC, Honolulu, HI, United States, 10/27/07. https://doi.org/10.1109/NSSMIC.2007.4437036
Liao J, Qi J. PET image reconstruction with anatomical prior using multiphase level set method. In IEEE Nuclear Science Symposium Conference Record. Vol. 6. 2007. p. 4163-4168. 4437036 https://doi.org/10.1109/NSSMIC.2007.4437036
Liao, Jinxiu ; Qi, Jinyi. / PET image reconstruction with anatomical prior using multiphase level set method. IEEE Nuclear Science Symposium Conference Record. Vol. 6 2007. pp. 4163-4168
@inproceedings{b4a54cc398db4a42bd47c765ffc0127d,
title = "PET image reconstruction with anatomical prior using multiphase level set method",
abstract = "Acquiring both anatomical and functional images during one scan, PET/CT systems improve the ability to distinguish pathology from normal uptake and to precisely localize abnormal loci. Researchers have investigated using anatomical boundary information in CT to regularize PET images. Here we propose a novel approach to maximum a posteriori (MAP) reconstruction of PET images with CT-based prior information. The image prior models both the smoothness of PET images and the similarity between functional boundaries in PET and anatomical boundaries in CT. Minimal smoothing is applied across functional boundaries to preserve shape edges. Level set functions are use to describe the anatomical boundaries from CT and to track the evolution of the functional boundaries in PET. The proposed method does not assume an exact match between PET and CT boundaries, but rather maximizes the similarity between the two boundaries, while allowing different region definition in the PET image. This is an important feature as mismatches between anatomical and functional boundaries have been observed in clinical images that could be caused either by inherent difference in the contrast mechanisms and/or subject motion. We conducted computer simulations to evaluate the performance of the proposed method. A digital phantom was built based on a PET/CT scan of a mouse. Two anatomical priors are obtained by modifying the segmented CT boundaries. The proposed method is compared with other methods including ML-EM, MAP with spatial-invariant and spatial-variant smoothing. The reconstructed images of the proposed method are locally smooth with sharp functional boundaries, while blurred boundaries appears in the results of other methods. The region of interest quantification study shows that the proposed method achieves less bias at the same noise level compared to the existing methods.",
keywords = "Anatomical prior, Image reconstruction, Level set method, PET/CT, Positron emission tomography, Shape prior",
author = "Jinxiu Liao and Jinyi Qi",
year = "2007",
doi = "10.1109/NSSMIC.2007.4437036",
language = "English (US)",
isbn = "1424409233",
volume = "6",
pages = "4163--4168",
booktitle = "IEEE Nuclear Science Symposium Conference Record",

}

TY - GEN

T1 - PET image reconstruction with anatomical prior using multiphase level set method

AU - Liao, Jinxiu

AU - Qi, Jinyi

PY - 2007

Y1 - 2007

N2 - Acquiring both anatomical and functional images during one scan, PET/CT systems improve the ability to distinguish pathology from normal uptake and to precisely localize abnormal loci. Researchers have investigated using anatomical boundary information in CT to regularize PET images. Here we propose a novel approach to maximum a posteriori (MAP) reconstruction of PET images with CT-based prior information. The image prior models both the smoothness of PET images and the similarity between functional boundaries in PET and anatomical boundaries in CT. Minimal smoothing is applied across functional boundaries to preserve shape edges. Level set functions are use to describe the anatomical boundaries from CT and to track the evolution of the functional boundaries in PET. The proposed method does not assume an exact match between PET and CT boundaries, but rather maximizes the similarity between the two boundaries, while allowing different region definition in the PET image. This is an important feature as mismatches between anatomical and functional boundaries have been observed in clinical images that could be caused either by inherent difference in the contrast mechanisms and/or subject motion. We conducted computer simulations to evaluate the performance of the proposed method. A digital phantom was built based on a PET/CT scan of a mouse. Two anatomical priors are obtained by modifying the segmented CT boundaries. The proposed method is compared with other methods including ML-EM, MAP with spatial-invariant and spatial-variant smoothing. The reconstructed images of the proposed method are locally smooth with sharp functional boundaries, while blurred boundaries appears in the results of other methods. The region of interest quantification study shows that the proposed method achieves less bias at the same noise level compared to the existing methods.

AB - Acquiring both anatomical and functional images during one scan, PET/CT systems improve the ability to distinguish pathology from normal uptake and to precisely localize abnormal loci. Researchers have investigated using anatomical boundary information in CT to regularize PET images. Here we propose a novel approach to maximum a posteriori (MAP) reconstruction of PET images with CT-based prior information. The image prior models both the smoothness of PET images and the similarity between functional boundaries in PET and anatomical boundaries in CT. Minimal smoothing is applied across functional boundaries to preserve shape edges. Level set functions are use to describe the anatomical boundaries from CT and to track the evolution of the functional boundaries in PET. The proposed method does not assume an exact match between PET and CT boundaries, but rather maximizes the similarity between the two boundaries, while allowing different region definition in the PET image. This is an important feature as mismatches between anatomical and functional boundaries have been observed in clinical images that could be caused either by inherent difference in the contrast mechanisms and/or subject motion. We conducted computer simulations to evaluate the performance of the proposed method. A digital phantom was built based on a PET/CT scan of a mouse. Two anatomical priors are obtained by modifying the segmented CT boundaries. The proposed method is compared with other methods including ML-EM, MAP with spatial-invariant and spatial-variant smoothing. The reconstructed images of the proposed method are locally smooth with sharp functional boundaries, while blurred boundaries appears in the results of other methods. The region of interest quantification study shows that the proposed method achieves less bias at the same noise level compared to the existing methods.

KW - Anatomical prior

KW - Image reconstruction

KW - Level set method

KW - PET/CT

KW - Positron emission tomography

KW - Shape prior

UR - http://www.scopus.com/inward/record.url?scp=48149083142&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=48149083142&partnerID=8YFLogxK

U2 - 10.1109/NSSMIC.2007.4437036

DO - 10.1109/NSSMIC.2007.4437036

M3 - Conference contribution

AN - SCOPUS:48149083142

SN - 1424409233

SN - 9781424409235

VL - 6

SP - 4163

EP - 4168

BT - IEEE Nuclear Science Symposium Conference Record

ER -