Journal
PHYSICS IN MEDICINE AND BIOLOGY
Volume 60, Issue 5, Pages 1741-1762Publisher
IOP PUBLISHING LTD
DOI: 10.1088/0031-9155/60/5/1741
Keywords
spectral CT; image reconstruction; total variation; nuclear norm; multi-channel; multi-spectral; photon counting
Funding
- National Institutes of Health grant [RO1CA134680]
- Toshiba Medical Research Institute USA
- NIH [S10 RRO21039, P30 CA14599]
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We explore the use of the recently proposed 'total nuclear variation' (TVN) as a regularizer for reconstructing multi-channel, spectral CT images. This convex penalty is a natural extension of the total variation (TV) to vector-valued images and has the advantage of encouraging common edge locations and a shared gradient direction among image channels. We show how it can be incorporated into a general, data-constrained reconstruction framework and derive update equations based on the first-order, primal-dual algorithm of Chambolle and Pock. Early simulation studies based on the numerical XCAT phantom indicate that the inter-channel coupling introduced by the TVN leads to better preservation of image features at high levels of regularization, compared to independent, channel-by-channel TV reconstructions.
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