ArticleIEEE transactions on medical imaging2020
A Robust Regularizer for Multiphase CT.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Convex optimization algorithms in medical image reconstruction-in the age of AI.Physics in medicine and biology · 2022Review
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2 authors at 2 institutions in 1 country.
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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.
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