UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Improvement of the Sign Determination Method for Data-Driven respiratory signal in TOF-PET

Bertolli, O; Arridge, S; Stearns, CW; Wollenweber, SD; Hutton, BF; Thielemans, K; (2017) Improvement of the Sign Determination Method for Data-Driven respiratory signal in TOF-PET. In: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings. IEEE Green open access

[img]
Preview
Text
Bertolli MIC2017 proceedings - DDG-TOF.pdf - Accepted version

Download (1MB) | Preview

Abstract

Respiratory gating and motion correction can increase resolution in PET chest imaging, but require a respiratory signal. Data-Driven (DD) methods aim to produce a respiratory signal from PET data, avoiding the use of external devices. Principal Component Analysis (PCA) is an easy to implement DD algorithm whose signals, however, are determined up to an arbitrary factor. The direction of the motion represented by its signal has to be determined. In this work we present the extension to TOF data of a previously presented sign-determination method. Furthermore, we propose the application of a selection process in sinogram space, to automatically select the areas of the data mostly affected by respiratory motion. The performance of the updated sign-determination method is evaluated on patient data, and the effect of TOF information and masking process is investigated also in terms of quality of the PCA respiratory signal.

Type: Proceedings paper
Title: Improvement of the Sign Determination Method for Data-Driven respiratory signal in TOF-PET
Event: IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) / 24th International Symposium on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors
Location: Atlanta, GA
Dates: 21 October 2017 - 28 October 2017
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/NSSMIC.2017.8533018
Publisher version: https://doi.org/10.1109/NSSMIC.2017.8533018
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Principal component analysis, Correlation, Signal resolution, Reliability, Medical services, Biomedical imaging
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Metabolism and Experi Therapeutics
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10066911
Downloads since deposit
48Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item