Parameter selection in limited data cone-beam CT reconstruction using edge preserving total variation algorithms

Manasavee Lohvithee, Ander Biguri, Manuchehr Soleimani

Research output: Contribution to journalArticle

5 Citations (Scopus)
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Abstract

There are a number of powerful total variation (TV) regularization methods with
 great promises in limited data cone-beam CT reconstruction with an enhancement
 of image quality. These promising TV methods require careful selection of image
 reconstruction parameters for which there is no well established criteria. This pa-
 per presents a comprehensive valuation of parameter selection in a number of major
 TV-based reconstruction algorithms. The appropriate way of selecting the values for
 each individual parameter has been suggested. Finally, the new adaptive-weighted
 projection-controlled steepest descent (AwPCSD) algorithm is presented which imple-
 ments the edge-preserving function for CBCT reconstruction with limited data. The
 proposed algorithm shows signicant robustness compared to other three existing al-
 gorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm
 is able to preserve the edges of the reconstructed images better with less sensitive pa-
 rameters to tune.
Original languageEnglish
Article number9295
JournalPhysics in Medicine and Biology
Volume62
Issue number24
Early online date16 Oct 2017
DOIs
Publication statusPublished - 21 Nov 2017

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Cone-Beam Computed Tomography

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Parameter selection in limited data cone-beam CT reconstruction using edge preserving total variation algorithms. / Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr.

In: Physics in Medicine and Biology, Vol. 62, No. 24, 9295, 21.11.2017.

Research output: Contribution to journalArticle

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