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
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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 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 > Department of Imaging 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 |
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