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Open AccessArticle

3D Tensor Based Nonlocal Low Rank Approximation in Dynamic PET Reconstruction

1
State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
2
Department of Mathematics, University of Florida Gainesville, Gainesville, FL 118105, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5299; https://doi.org/10.3390/s19235299
Received: 10 October 2019 / Revised: 24 November 2019 / Accepted: 26 November 2019 / Published: 1 December 2019
(This article belongs to the Special Issue Compressed Sensing in Biomedical Signal and Image Analysis)
Reconstructing images from multi-view projections is a crucial task both in the computer vision community and in the medical imaging community, and dynamic positron emission tomography (PET) is no exception. Unfortunately, image quality is inevitably degraded by the limitations of photon emissions and the trade-off between temporal and spatial resolution. In this paper, we develop a novel tensor based nonlocal low-rank framework for dynamic PET reconstruction. Spatial structures are effectively enhanced not only by nonlocal and sparse features, but momentarily by tensor-formed low-rank approximations in the temporal realm. Moreover, the total variation is well regularized as a complementation for denoising. These regularizations are efficiently combined into a Poisson PET model and jointly solved by distributed optimization. The experiments demonstrated in this paper validate the excellent performance of the proposed method in dynamic PET. View Full-Text
Keywords: dynamic positron emission tomography (PET); non-local; tensor decomposition; low-rank approximation; compressed sensing; reconstruction; distributed optimization dynamic positron emission tomography (PET); non-local; tensor decomposition; low-rank approximation; compressed sensing; reconstruction; distributed optimization
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Xie, N.; Chen, Y.; Liu, H. 3D Tensor Based Nonlocal Low Rank Approximation in Dynamic PET Reconstruction. Sensors 2019, 19, 5299.

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