Shearlet-based regularized reconstruction in region-of-interest computed tomography

Tatiana Bubba, Demetrio Labate, Gaetano Zanghirati, Silvia Bonettini

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)

Abstract

Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruction problem has unique solution. To address this problem, both ad hoc analytic formulas and iterative numerical schemes have been proposed in the literature. In this paper, we introduce a novel approach for ROI tomographic reconstruction, formulated as a convex optimization problem with a regularized term based on shearlets. Our numerical implementation consists of an iterative scheme based on the scaled gradient projection method and it is tested in the context of fan-beam CT. Our results show that our approach is essentially insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.
Original languageEnglish
Article number34
JournalMathematical Modelling of Natural Phenomena
Volume13
Issue number4
Early online date21 May 2018
DOIs
Publication statusPublished - 31 Dec 2018

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