Abstract
Joint image reconstruction for multiphase CT can potentially improve image quality and reduce dose by leveraging the shared information among the phases. Multiphase CT scans are acquired sequentially. Inter-scan patient breathing causes small organ shifts and organ boundary misalignment among different phases. Existing multi-channel regularizers such as the joint total variation (TV) can introduce artifacts at misaligned organ boundaries. We propose a multi-channel regularizer using the infimal convolution (inf-conv) between a joint TV and a separable TV. It is robust against organ misalignment; it can work like a joint TV or a separable TV depending on a parameter setting. The effects of the parameter in the inf-conv regularizer are analyzed in detail. The properties of the inf-conv regularizer are then investigated numerically in a multi-channel image denoising setting. For algorithm implementation, the inf-conv regularizer is nonsmooth; inverse problems with the inf-conv regularizer can be solved using a number of primal-dual algorithms from nonsmooth convex minimization. Our numerical studies using synthesized 2-phase patient data and phantom data demonstrate that the inf-conv regularizer can largely maintain the advantages of the joint TV over the separable TV and reduce image artifacts of the joint TV due to organ misalignment.
Original language | English (US) |
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Article number | 8968617 |
Pages (from-to) | 2327-2338 |
Number of pages | 12 |
Journal | IEEE transactions on medical imaging |
Volume | 39 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2020 |
Keywords
- Huber function
- infimal convolution
- multi-channel image reconstruction
- nuclear norm
- total variation
ASJC Scopus subject areas
- Software
- Radiological and Ultrasound Technology
- Computer Science Applications
- Electrical and Electronic Engineering