Abstract
Computed tomography is nowadays an indispensable tool in medicine used to diagnose multiple diseases. In clinical and emergency room environments, the speed of acquisition and information processing are crucial. CUDA is a software architecture used to work with NVIDIA graphics processing units. In this paper a methodology to accelerate tomographic image reconstruction based on maximum likelihood expectation maximization iterative algorithm and combined with the use of graphics processing units programmed in CUDA framework is presented. Implementations developed here are used to reconstruct images with clinical use. Timewise, parallel versions showed improvement with respect to serial implementations. These differences reached, in some cases, 2 orders of magnitude in time while preserving image quality. The image quality and reconstruction times were not affected significantly by the addition of Poisson noise to projections. Furthermore, our implementations showed good performance when compared with reconstruction methods provided by commercial software. One of the goals of this work was to provide a fast, portable, simple, and cheap image reconstruction system, and our results support the statement that the goal was achieved.
Highlights
X-ray computed tomography (CT) is a nondestructive technique in which a source of X-rays revolves around an object of interest, generating axial images of its structure
This work shows the parallel implementation of the maximum likelihood expectation maximization (MLEM) reconstruction algorithm using graphic card capabilities
Software solutions developed here were compared with reconstructions of clinical images using several image quality parameters (CNR, structural similarity index (SSIM), mean squared error (MSE), etc.)
Summary
X-ray computed tomography (CT) is a nondestructive technique in which a source of X-rays (ionizing radiation) revolves around an object of interest, generating axial images of its structure. Some of the proposed lines of work to achieve this objective include reduction of patient exposure time, reduction of acquisition and processing times, development of new techniques with new functionalities, and cost reduction [2]. Some of the solutions to these points have so far focused on the development of higher performance hardware that will lower costs for and dose delivery to patients without compromising the effectiveness of the diagnosis. Work has been done on the use of iterative methods for image reconstruction, reducing processing times and reducing the dose necessary for the acquisition of images with diagnostic value
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