4D computed tomography reconstruction from few-projection data via temporal non-local regularization

Authors:
Xun Jia;Yifei Lou;Bin Dong;Zhen Tian;Steve Jiang
Affiliations:
Department of Radiation Oncology University of California, San Diego, La Jolla, CA;Department of Mathematics, University of California, Los Angeles, Los Angeles, CA;Department of Mathematics, University of California, San Diego, La Jolla, CA;Department of Radiation Oncology University of California, San Diego, La Jolla, CA and Department of Biomedical Engineering, Graduate School at Tsinghua University, Shenzhen, Guangdong, China;Department of Radiation Oncology University of California, San Diego, La Jolla, CA
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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Abstract

4D computed tomography (4D-CT) is an important modality in medical imaging due to its ability to resolve patient anatomy motion in each respiratory phase. Conventionally 4D-CT is accomplished by performing the reconstruction for each phase independently as in a CT reconstruction problem. We propose a new 4D-CT reconstruction algorithm that explicitly takes into account the temporal regularization in a non-local fashion. By imposing a regularization of a temporal nonlocal means (TNLM) form, 4D-CT images at all phases can be reconstructed simultaneously based on extremely under-sampled x-ray projections. Our algorithm is validated in one digital NCAT thorax phantom and two real patient cases. It is found that our TNLM algorithm is capable of reconstructing the 4D-CT images with great accuracy. The experiments also show that our approach outperforms standard 4D-CT reconstruction methods with spatial regularization of total variation or tight frames.