Evaluation Performance of Iterative Algorithms for 3D Image Reconstruction in Cone Beam Geometry

  Algebraic reconstruction technique (ART) is iterative reconstruction algorithm using for reconstructing the two dimension (2D) and three dimension (3D) images. In this research  different algorithms of ART were used to reconstruction : (simple ART, Simultaneous ART, and Multiplicative ART) for reconstruction 3D image using multi slice scanner in cone beam geometry. To perform the time reconstruction of ART algorithms, use the Maximum-Likelihood Expectation Maximization (ML-EM) algorithm to fast ART algorithm. Multi slice Computed Tomography  CT scanner newly discovered and used widely in the medical field for diagnosis and radiographic to its benefit from the speed of scanner and quality of image reconstruction comparing with single slice scanner. In simulation result the Multiplicative ART (MART) algorithm with suitable relaxation paramete

Fast GPU-Based Approach to Branchless Distance-Driven Projection and Back-Projection in Cone Beam CT

Modern CT image reconstruction algorithms rely on projection and back-projection operations to refine an image estimate in iterative image reconstruction. A widely-used state-of-the-art technique is distance-driven projection and back-projection. While the distance-driven technique yields superior image quality in iterative algorithms, it is a computationally demanding process. This has a detrimental effect on the relevance of the algorithms in clinical settings. A few methods have been proposed for enhancing the distance-driven technique in order to take advantage of modern computer hardware. This study explores a two-dimensional extension of the branchless method, which is a technique that does not compromise image quality. The extension of the branchless method is named “pre-projection integration” because it gets a performance boost by integrating the data before the projection and back-projection operations. It was written with Nvidia’s CUDA framework and carefully designed for massively parallel graphics processing units (GPUs). The performance and the image quality of the pre-projection integration method were analyzed. Both projection and back-projection are significantly faster with pre-projection integration. The image quality was analyzed using cone beam CT image reconstruction algorithms within Jeffrey Fessler’s Image Reconstruction Toolbox. Images produced from regularized, iterative image reconstruction algorithms using the pre-projection integration method show no significant artifacts

Applications in GNSS water vapor tomography

Algebraic reconstruction algorithms are iterative algorithms that are used in many area including medicine, seismology or meteorology. These algorithms are known to be highly computational intensive. This may be especially troublesome for real-time applications or when processed by conventional low-cost personnel computers. One of these real time applications is the reconstruction of water vapor images from Global Navigation Satellite System (GNSS) observations. The parallelization of algebraic reconstruction algorithms has the potential to diminish signi cantly the required resources permitting to obtain valid solutions in time to be used for nowcasting and forecasting weather models. The main objective of this dissertation was to present and analyse diverse shared memory libraries and techniques in CPU and GPU for algebraic reconstruction algorithms. It was concluded that the parallelization compensates over sequential implementations. Overall the GPU implementations were found to be only slightly faster than the CPU implementations, depending on the size of the problem being studied. A secondary objective was to develop a software to perform the GNSS water vapor reconstruction using the implemented parallel algorithms. This software has been developed with success and diverse tests were made namely with synthetic and real data, the preliminary results shown to be satisfactory. This dissertation was written in the Space & Earth Geodetic Analysis Laboratory (SEGAL) and was carried out in the framework of the Structure of Moist convection in high-resolution GNSS observations and models (SMOG) (PTDC/CTE-ATM/119922/2010) project funded by FCT.Algoritmos de reconstrução algébrica são algoritmos iterativos que são usados em muitas áreas incluindo medicina, sismologia ou meteorologia. Estes algoritmos são conhecidos por serem bastante exigentes computacionalmente. Isto pode ser especialmente complicado para aplicações de tempo real ou quando processados por computadores pessoais de baixo custo. Uma destas aplicações de tempo real é a reconstrução de imagens de vapor de água a partir de observações de sistemas globais de navegação por satélite. A paralelização dos algoritmos de reconstrução algébrica permite que se reduza significativamente os requisitos computacionais permitindo obter soluções válidas para previsão meteorológica num curto espaço de tempo. O principal objectivo desta dissertação é apresentar e analisar diversas bibliotecas e técnicas multithreading para a reconstrução algébrica em CPU e GPU. Foi concluído que a paralelização compensa sobre a implementações sequenciais. De um modo geral as implementações GPU obtiveram resultados relativamente melhores que implementações em CPU, isto dependendo do tamanho do problema a ser estudado. Um objectivo secundário era desenvolver uma aplicação que realizasse a reconstrução de imagem de vapor de água através de sistemas globais de navegação por satélite de uma forma paralela. Este software tem sido desenvolvido com sucesso e diversos testes foram realizados com dados sintéticos e dados reais, os resultados preliminares foram satisfatórios. Esta dissertação foi escrita no Space & Earth Geodetic Analysis Laboratory (SEGAL) e foi realizada de acordo com o projecto Structure 01' Moist convection in high-resolution GNSS observations and models (SMOG) (PTDC / CTE-ATM/ 11992212010) financiado pelo FCT.Fundação para a Ciência e a Tecnologia (FCT

3D Alternating Direction TV-Based Cone-Beam CT Reconstruction with Efficient GPU Implementation

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In order to improve the performance for iterative reconstruction, an efficient IIR algorithm for cone-beam computed tomography (CBCT) with GPU implementation has been proposed in this paper. In the first place, an algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction. The applied proximal technique avoids the horrible pseudoinverse computation of big matrix which makes the proposed algorithm applicable and efficient for CBCT imaging. The iteration for this algorithm is simple but convergent. The simulation and real CT data reconstruction results indicate that the proposed algorithm is both fast and accurate. The GPU implementation shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy. The runtime for the new 3D algorithm is about 6.8 seconds per loop with the image size of 256×256×256 and 36 projections of the size of 512×512

High-Speed Reconstruction of Low-Dose CT Using Iterative Techniques for Image-Guided Interventions

Minimally invasive image-guided interventions(IGIs) lead to improved treatment outcomes while significantly reducing patient trauma. Because of features such as fast scanning, high resolution, three-dimensional view and ease of operation, Computed-Tomography(CT) is increasingly the choice for IGIs. The risk of radiation exposure, however, limits its current and future use. We perform ultra low-dose scanning to overcome this limitation. To address the image quality problem at low doses, we reconstruct images using the iterative Paraboloidal Surrogate(PS) algorithm. Using actual scanner data, we demonstrate improvement in the quality of reconstructed images using the iterative algorithm at low doses as compared to the standard Filtered Back Projection(FBP) technique. We also accelerate the PS algorithm on a cluster of 32 processors and a GPU. We demonstrate approximately 20 times speedup for the cluster and two orders of improvement in speed for the GPU, while maintaining comparable image quality to the traditional uni-processor implementation