Objective. Dynamic positron emission tomography (PET) imaging, which can provide information on
dynamic changes in physiological metabolism, is now widely used in clinical diagnosis and cancer
treatment. However, the reconstruction from dynamic data is extremely challenging due to the limited
counts received in individual frame, especially in ultra short frames. Recently, the unrolled modelbased
deep learning methods have shown inspiring results for low-count PET image reconstruction
with good interpretability. Nevertheless, the existing model-based deep learning methods mainly
focus on the spatial correlations while ignore the temporal domain. Approach. In this paper, inspired
by the learned primal dual (LPD) algorithm, we propose the spatio-temporal primal dual network
(STPDnet) for dynamic low-count PET image reconstruction. Both spatial and temporal correlations
are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative
learning process of the network, which provides the physical constraints and enhances interpretability.
Main results. The experiments of both simulation data and real rat scan data have shown that the
proposed method can achieve substantial noise reduction in both temporal and spatial domains and
outperform the maximum likelihood expectation maximization, spatio-temporal kernel method,
LPD and FBPnet. Significance. Experimental results show STPDnet better reconstruction performance
in the low count situation, which makes the proposed method particularly suitable in whole-body
dynamic imaging and parametric PET imaging that require extreme short frames and usually suffer
from high level of noise.
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3D Tensor Based Nonlocal Low Rank Approximation in Dynamic PET Reconstruction
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.
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- Award ID(s):
- 1719932
- NSF-PAR ID:
- 10189169
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 19
- Issue:
- 23
- ISSN:
- 1424-8220
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s19235299
