GPU-enabled PET motion compensation using sparse and low-rank decomposition

Jingyu Cui; Jaewon Yang; Edward Graves; Craig S. Levin

doi:10.1109/NSSMIC.2012.6551767

GPU-enabled PET motion compensation using sparse and low-rank decomposition

Jingyu Cui, Jaewon Yang, Edward Graves, Craig S. Levin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

The resolution and signal-to-noise ratio (SNR) of motion-compensated PET images depend highly on the motion estimation accuracy. However, in many practical settings, the estimated motion information contains noise. In this work, we propose a fast and accurate method to incorporate noisy motion estimation into PET image reconstruction in a systematic framework using multiscale sliding time window reconstruction followed by matrix decomposition.

Original language	English (US)
Title of host publication	2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
Pages	3367-3370
Number of pages	4
DOIs	https://doi.org/10.1109/NSSMIC.2012.6551767
State	Published - 2012
Externally published	Yes
Event	2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012 - Anaheim, CA, United States Duration: Oct 29 2012 → Nov 3 2012

Publication series

Name	IEEE Nuclear Science Symposium Conference Record
ISSN (Print)	1095-7863

Other

Other	2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
Country/Territory	United States
City	Anaheim, CA
Period	10/29/12 → 11/3/12

ASJC Scopus subject areas

Radiation
Nuclear and High Energy Physics
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/NSSMIC.2012.6551767

Cite this

GPU-enabled PET motion compensation using sparse and low-rank decomposition. / Cui, Jingyu; Yang, Jaewon; Graves, Edward et al.
2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012. 2012. p. 3367-3370 6551767 (IEEE Nuclear Science Symposium Conference Record).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cui, J, Yang, J, Graves, E & Levin, CS 2012, GPU-enabled PET motion compensation using sparse and low-rank decomposition. in 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012., 6551767, IEEE Nuclear Science Symposium Conference Record, pp. 3367-3370, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012, Anaheim, CA, United States, 10/29/12. https://doi.org/10.1109/NSSMIC.2012.6551767

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abstract = "The resolution and signal-to-noise ratio (SNR) of motion-compensated PET images depend highly on the motion estimation accuracy. However, in many practical settings, the estimated motion information contains noise. In this work, we propose a fast and accurate method to incorporate noisy motion estimation into PET image reconstruction in a systematic framework using multiscale sliding time window reconstruction followed by matrix decomposition.",

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AU - Levin, Craig S.

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AB - The resolution and signal-to-noise ratio (SNR) of motion-compensated PET images depend highly on the motion estimation accuracy. However, in many practical settings, the estimated motion information contains noise. In this work, we propose a fast and accurate method to incorporate noisy motion estimation into PET image reconstruction in a systematic framework using multiscale sliding time window reconstruction followed by matrix decomposition.

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ER -

GPU-enabled PET motion compensation using sparse and low-rank decomposition

Abstract

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ASJC Scopus subject areas

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