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Proceedings Paper

Motion correction of respiratory-gated PET image using deep learning based image registration framework
Author(s): Tiantian Li; Mengxi Zhang; Wenyuan Qi; Evren Asma; Jinyi Qi
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Paper Abstract

Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating has been proposed to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of respiratory-gated PET images can reduce the motion artifacts and preserve the count statistics, but it is time consuming. In this work, we propose an unsupervised non-rigid image registration framework using deep learning. We use a differentiable spatial transformer layer to warp the source image to the target image and use a stacked structure for deformation field refinement. Estimated deformation fields were incorporated into an iterative image reconstruction algorithm to perform motion compensated PET image reconstruction. We validated the proposed method using simulation and clinical data and showed its ability to reduce the motion artifact in PET images.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110720Q (28 May 2019); doi: 10.1117/12.2534851
Show Author Affiliations
Tiantian Li, Univ. of California, Davis (United States)
Mengxi Zhang, Univ. of California, Davis (United States)
Wenyuan Qi, Canon Medical Research USA, Inc. (United States)
Evren Asma, Canon Medical Research USA, Inc. (United States)
Jinyi Qi, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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