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GPU‐accelerated regularized iterative reconstruction for few‐view cone beam CT
Author(s) -
Matenine Dmitri,
Goussard Yves,
Després Philippe
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4914143
Subject(s) - iterative reconstruction , computer science , image quality , regularization (linguistics) , algorithm , computer vision , image guided radiation therapy , convex optimization , mathematical optimization , artificial intelligence , regular polygon , medical imaging , mathematics , image (mathematics) , geometry
Purpose: The present work proposes an iterative reconstruction technique designed for x‐ray transmission computed tomography (CT). The main objective is to provide a model‐based solution to the cone‐beam CT reconstruction problem, yielding accurate low‐dose images via few‐views acquisitions in clinically acceptable time frames. Methods: The proposed technique combines a modified ordered subsets convex (OSC) algorithm and the total variation minimization (TV) regularization technique and is called OSC‐TV. The number of subsets of each OSC iteration follows a reduction pattern in order to ensure the best performance of the regularization method. Considering the high computational cost of the algorithm, it is implemented on a graphics processing unit, using parallelization to accelerate computations. Results: The reconstructions were performed on computer‐simulated as well as human pelvic cone‐beam CT projection data and image quality was assessed. In terms of convergence and image quality, OSC‐TV performs well in reconstruction of low‐dose cone‐beam CT data obtained via a few‐view acquisition protocol. It compares favorably to the few‐view TV‐regularized projections onto convex sets (POCS‐TV) algorithm. It also appears to be a viable alternative to full‐dataset filtered backprojection. Execution times are of 1–2 min and are compatible with the typical clinical workflow for nonreal‐time applications. Conclusions: Considering the image quality and execution times, this method may be useful for reconstruction of low‐dose clinical acquisitions. It may be of particular benefit to patients who undergo multiple acquisitions by reducing the overall imaging radiation dose and associated risks.