NDLI: Retrospective data-driven respiratory gating for PET using TOF information

Content Provider	IEEE Xplore Digital Library
Author	Mengdie Wang Ning Guo Hui Zhang Elfhakri, G. Guangshu Hu Quanzheng Li
Copyright Year	2015
Description	Author affiliation: Dept. of Radiol., Massachusetts Gen. Hosp., Boston, MA, USA (Ning Guo; Elfhakri, G.; Quanzheng Li) \|\| Dept. of Biomed. Eng., Tsinghua Univ., Beijing, China (Mengdie Wang; Hui Zhang; Guangshu Hu)
Abstract	Traditional data-driven respiratory gating method is capable of detecting breathing cycles directly from positron emission tomography (PET) data, but usually fails at low SNR, particularly at low dose PET/CT study. Time-of-flight (TOF) PET has the potential to improve the SNR. In order for TOF information to reduce the statistical noise and boost the performance of respiratory gating, we present a robust data-driven respiratory gating method using TOF information, which retrospectively derived the respiratory signal from the acquired TOF-PET data. The PET data was acquired in list mode format and analyzed in sinogram space. The method was demonstrated with patient datasets acquired on a TOF PET/CT system. Data-driven gating methods by center of mass (COM) and principle component analysis (PCA) algorithm were successfully performed on nonTOF PET and TOF PET dataset. To assess the accuracy of the data-driven respiratory signal, a hardware-based signal was acquired for comparison. The study showed that retrospectively respiratory gating using TOF sinograms has improved the SNR, and outperforms the non-TOF gating under both COM and PCA algorithms.
Starting Page	4520
Ending Page	4523
File Size	1650440
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319399
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Positron emission tomography Principal component analysis Computed tomography Signal to noise ratio Three-dimensional displays Accuracy Correlation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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