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Detection of Lung Density Variations With Principal Component Analysis in PET

Bertolli, O; Cuplov, V; Arridge, S; Stearns, CW; Wollenweber, SD; Hutton, BF; Thielemans, K; (2017) Detection of Lung Density Variations With Principal Component Analysis in PET. In: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings. IEEE Green open access

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Abstract

Respiratory motion generates lung volume changes during the breathing cycle. These affect the lung tissue density and therefore influence both the attenuation effect and the radiotracer concentration in PET imaging. To detect and correct for these effects could improve the quantitative accuracy of lung PET imaging. In this work we propose the use of Principal Component Analysis (PCA) to detect respiratory-induced lung density changes in the upper lung, where motion is expected to be minimal. The method is firstly applied to simulation data, specifically generated to simulate density changes only and no motion. Secondly, it is applied on the upper lung bed position of 15 lung cancer patients datasets. The total number of counts in time is also evaluated. The results show that the PCA signal is highly correlated to the respiratory trace obtained from an external device, and also to the variation of total counts in time. As the bed positions taken into account do not include moving organs, the results suggest that PCA is successful in detecting respiratory-induced density changes in the upper lung.

Type: Proceedings paper
Title: Detection of Lung Density Variations With Principal Component Analysis in 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.2017.8532667
Publisher version: https://doi.org/10.1109/NSSMIC.2017.8532667
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: Lung, Principal component analysis, Logic gates, Attenuation, Data models, Computed tomography, Image reconstruction
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/10066912
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