期刊
PHYSICS IN MEDICINE AND BIOLOGY
卷 61, 期 18, 页码 6833-6855出版社
IOP PUBLISHING LTD
DOI: 10.1088/0031-9155/61/18/6833
关键词
positron emission tomography; image reconstruction; anatomical prior; hybrid PET-MR; patch representation; sparsity; re-parameterization
资金
- Natural Sciences and Engineering Research Council (NSERC) [RGPIN 387067-10]
- Canada Research Chairs program
- UK Engineering and Physical Sciences Research Council [EP/M020142/1]
- Engineering and Physical Sciences Research Council [EP/M020142/1] Funding Source: researchfish
- EPSRC [EP/M020142/1] Funding Source: UKRI
In PET image reconstruction, regularization is often needed to reduce the noise in the resulting images. Patch-based image processing techniques have recently been successfully used for regularization in medical image reconstruction through a penalized likelihood framework. Re-parameterization within reconstruction is another powerful regularization technique in which the object in the scanner is re-parameterized using coefficients for spatially-extensive basis vectors. In this work, a method for extracting patch-based basis vectors from the subject's MR image is proposed. The coefficients for these basis vectors are then estimated using the conventional MLEM algorithm. Furthermore, using the alternating direction method of multipliers, an algorithm for optimizing the Poisson log-likelihood while imposing sparsity on the parameters is also proposed. This novel method is then utilized to find sparse coefficients for the patch-based basis vectors extracted from the MR image. The results indicate the superiority of the proposed methods to patch-based regularization using the penalized likelihood framework.
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