Multi-channel Potts-based reconstruction for multi-spectral computed tomography. (1st March 2021)

• Record Type: Journal Article
• Authors: Kiefer, Lukas; Petra, Stefania; Storath, Martin; Weinmann, Andreas
• Is Part Of: Inverse problems. Volume 37:Number 4(2021)
• Publication Date: 2021-03-01
• Subjects: image reconstruction -- structural regularization -- multi-channel Potts prior -- superiorization -- Potts model -- piecewise constant Mumford–Shah model -- multi-spectral computed tomography; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/abdd45
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: We consider reconstructing multi-channel images from measurements performed by photon-counting and energy-discriminating detectors in the setting of multi-spectral x-ray computed tomography (CT). Our aim is to exploit the strong structural correlation that is known to exist between the channels of multi-spectral CT images. To that end, we adopt the multi-channel Potts prior to jointly reconstruct all channels. This nonconvex prior produces piecewise constant solutions with strongly correlated channels. In particular, edges are strictly enforced to have the same spatial position across channels which is a benefit over TV-based methods whose channel-couplings are typically less strict. We consider the Potts prior in two frameworks: (a) in the context of a variational Potts model, and (b) in a Potts-superiorization approach that perturbs the iterates of a basic iterative least squares solver. We identify an alternating direction method of multipliers approach as well as a Potts-superiorized conjugate gradient method as particularly suitable. In numerical experiments, we compare the Potts prior based approaches to existing TV-type approaches on realistically simulated multi-spectral CT data and obtain improved reconstruction for compound solid bodies.