Improved PET/CT Respiratory Motion Compensation by Incorporating Changes in Lung Density

Journal article


Emond, E.C., Bousse, A., Brusaferri, L., Hutton, B.F. and Thielemans, K. (2020). Improved PET/CT Respiratory Motion Compensation by Incorporating Changes in Lung Density. IEEE Transactions on Radiation and Plasma Medical Sciences. pp. 594 - 602. https://doi.org/10.1109/trpms.2020.3001094
AuthorsEmond, E.C., Bousse, A., Brusaferri, L., Hutton, B.F. and Thielemans, K.
Abstract

Positron emission tomography/computed tomography (PET/CT) lung imaging is highly sensitive to motion. Although several techniques exist to diminish motion artifacts, a few accounts for both tissue displacement and changes in density due to the compression and dilation of the lungs, which cause quantification errors. This article presents an experimental framework for joint activity image reconstruction and motion estimation in PET/CT, where the PET image and the motion are directly estimated from the raw data. Direct motion estimation methods for motion-compensated PET/CT are preferable as they require a single attenuation map only and result in optimal signal-to-noise ratio (SNR). Previous implementations, however, failed to address changes in density during respiration. We propose to account for such changes using the Jacobian determinant of the deformation fields. In a feasibility study, we demonstrate on a modified extended cardiac-torso (XCAT) phantom with breathing motion-where the lung density and activity vary-that our approach achieved better quantification in the lungs than conventional PET/CT joint activity image reconstruction and motion estimation that does not account for density changes. The proposed method resulted in lower bias and variance in the activity images, reduced mean relative activity error in the lung at the reference gate (-4.84% to -3.22%) and more realistic Jacobian determinant values.

Year2020
JournalIEEE Transactions on Radiation and Plasma Medical Sciences
Journal citationpp. 594 - 602
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN2469-7303
Digital Object Identifier (DOI)https://doi.org/10.1109/trpms.2020.3001094
Web address (URL)https://doi.org/10.1109/TRPMS.2020.3001094
Publication dates
Online09 Jul 2020
Publication process dates
Deposited16 Feb 2024
Publisher's version
License
File Access Level
Open
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