Abstract

In medicine, the acquisition process in Computed Tomography Images (CT) is obtained by a reconstruction algorithm. The classical method for image reconstruction is the Filtered Back Projection (FBP). This method is fast and simple but does not use any statistical information about the measurements. The appearance of artifacts and its low spatial resolution in reconstructed images must be considered. Furthermore, the FBP requires of optimal conditions of the projections and complete sets of data. In this paper a methodology to accelerate acquisition process for CT based on the Maximum Likelihood Estimation Method (MLEM) algorithm is presented. This statistical iterative reconstruction algorithm uses a GPU Programming Paradigms and was compared with sequential algorithms in which the reconstruction time was reduced by up to 3 orders of magnitude while preserving image quality. Furthermore, they showed a good performance when compared with reconstruction methods provided by commercial software. The system, which would consist exclusively of a commercial laptop and GPU could be used as a fast, portable, simple and cheap image reconstruction platform in the future.

Highlights

  • X-ray (XR) Computed Tomography (CT) is a nondestructive technique in which an XR source rotates around an object of interest generating axial slices of its internal structure

  • [4] we examined the use of a portable head Computed Tomography Images (CT) scanner

  • The line of work presented in this study consisted of: First, the Maximum Likelihood Estimation Method (MLEM) algorithm implementation was solved in parallel using Graphic Processing Unit (GPU) with Compute Unified Device Architecture (CUDA)

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Summary

Introduction

X-ray (XR) Computed Tomography (CT) is a nondestructive technique in which an XR source rotates around an object of interest generating axial slices of its internal structure. Che et al [23] presented a review on the general-purpose applications of graphics processors using CUDA As it could be expected, medical image processing was one of the first fields in which NVIDIA GPUs and CUDA were used [24], [25] mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. Our method combines direct volume rendering via ray-casting with a novel approach for isosurface extraction and re-use directly on graphics processing units The main disadvantage of MLEM algorithms is their high computational and time costs, a reason why FBP algorithms are still the golden standard in the field These algorithms and their applications to image reconstruction have the advantage that they can be parallelized. Methodology to accelerate tomographic image analysis based on iterative algorithms (MLEM), combined with the use of NVIDIA GPU programmed in the CUDA architecture is presented

Methods
The MLEM Algorithm
Hardware and Software
Results and Conclusions
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