Fast iterative image reconstruction of 3D PET data
Abstract
For count-limited PET imaging protocols, two different approaches to reducing statistical noise are volume, or 3D, imaging to increase sensitivity, and statistical reconstruction methods to reduce noise propagation. These two approaches have largely been developed independently, likely due to the perception of the large computational demands of iterative 3D reconstruction methods. We present results of combining the sensitivity of 3D PET imaging with the noise reduction and reconstruction speed of 2D iterative image reconstruction methods. This combination is made possible by using the recently-developed Fourier rebinning technique (FORE), which accurately and noiselessly rebins 3D PET data into a 2D data set. The resulting 2D sinograms are then reconstructed independently by the ordered-subset EM (OSEM) iterative reconstruction method, although any other 2D reconstruction algorithm could be used. We demonstrate significant improvements in image quality for whole-body 3D PET scans by using the FORE+OSEM approach compared with the standard 3D Reprojection (3DRP) algorithm. In addition, the FORE+OSEM approach involves only 2D reconstruction and it therefore requires considerably less reconstruction time than the 3DRP algorithm, or any fully 3D statistical reconstruction algorithm.
- Authors:
-
- Univ. of Pittsburgh, PA (United States)
- Catholic Univ. of Louvain (Belgium); and others
- Publication Date:
- OSTI Identifier:
- 513321
- Report Number(s):
- CONF-961123-
TRN: 97:014373
- Resource Type:
- Conference
- Resource Relation:
- Conference: Institute of Electrical and Electronic Engineers (IEEE) nuclear science symposium and medical imaging conference, Anaheim, CA (United States), 2-9 Nov 1996; Other Information: PBD: 1996; Related Information: Is Part Of 1996 IEEE nuclear science symposium - conference record. Volumes 1, 2 and 3; Del Guerra, A. [ed.]; PB: 2138 p.
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 55 BIOLOGY AND MEDICINE, BASIC STUDIES; 44 INSTRUMENTATION, INCLUDING NUCLEAR AND PARTICLE DETECTORS; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; POSITRON COMPUTED TOMOGRAPHY; IMAGE PROCESSING; ITERATIVE METHODS; BODY; THREE-DIMENSIONAL CALCULATIONS; NOISE; LUNGS; NEOPLASMS
Citation Formats
Kinahan, P E, Townsend, D W, and Michel, C. Fast iterative image reconstruction of 3D PET data. United States: N. p., 1996.
Web.
Kinahan, P E, Townsend, D W, & Michel, C. Fast iterative image reconstruction of 3D PET data. United States.
Kinahan, P E, Townsend, D W, and Michel, C. 1996.
"Fast iterative image reconstruction of 3D PET data". United States.
@article{osti_513321,
title = {Fast iterative image reconstruction of 3D PET data},
author = {Kinahan, P E and Townsend, D W and Michel, C},
abstractNote = {For count-limited PET imaging protocols, two different approaches to reducing statistical noise are volume, or 3D, imaging to increase sensitivity, and statistical reconstruction methods to reduce noise propagation. These two approaches have largely been developed independently, likely due to the perception of the large computational demands of iterative 3D reconstruction methods. We present results of combining the sensitivity of 3D PET imaging with the noise reduction and reconstruction speed of 2D iterative image reconstruction methods. This combination is made possible by using the recently-developed Fourier rebinning technique (FORE), which accurately and noiselessly rebins 3D PET data into a 2D data set. The resulting 2D sinograms are then reconstructed independently by the ordered-subset EM (OSEM) iterative reconstruction method, although any other 2D reconstruction algorithm could be used. We demonstrate significant improvements in image quality for whole-body 3D PET scans by using the FORE+OSEM approach compared with the standard 3D Reprojection (3DRP) algorithm. In addition, the FORE+OSEM approach involves only 2D reconstruction and it therefore requires considerably less reconstruction time than the 3DRP algorithm, or any fully 3D statistical reconstruction algorithm.},
doi = {},
url = {https://www.osti.gov/biblio/513321},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Dec 31 00:00:00 EST 1996},
month = {Tue Dec 31 00:00:00 EST 1996}
}