دانلود مقاله انگلیسی رایگان:بازسازی تکرار تصویر تکرار شده برای توموگرافی محاسبه شده با پرتو مخروطی از طریق چارچوب داده های بزرگ - 2020
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  • Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworks Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworks
    Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworks

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworks


    ترجمه فارسی عنوان مقاله:

    بازسازی تکرار تصویر تکرار شده برای توموگرافی محاسبه شده با پرتو مخروطی از طریق چارچوب داده های بزرگ


    منبع:

    Sciencedirect - Elsevier - Future Generation Computer Systems, Accepted manuscript: doi:10:1016/j:future:2019:12:042


    نویسنده:

    Estefania Serrano, Javier Garcia-Blas, Jesus Carretero, Manuel Desco, Monica Abella


    چکیده انگلیسی:

    One of the latest trends in Computed Tomography (CT) is the reduction of the radiation dose delivered to patients through the decrease of the amount of acquired data. This reduction results in artifacts in the final images if conventional reconstruction methods are used, making it advisable to employ iterative algorithms to enhance image quality. Most approaches are built around two main operators, backprojection and projection, which are computationally expensive. In this work, we present an implementation of those operators for iterative reconstruction methods exploiting the Big Data paradigm. We define an architecture based on Apache Spark that supports both Graphical Processing Units (GPU) and CPU-based architectures. The aforementioned are parallelized using a partitioning scheme based on the division of the volume and irregular data structures in order to reduce the cost of communication and computation of the final images. Our solution accelerates the execution of the two most computational expensive components with Apache Spark, improving the programming experience of new iterative reconstruction algorithms and the maintainability of the source code increasing the level of abstraction for non-experienced high performance programmers. Through an experimental evaluation, we show that we can obtain results up to 10× faster for projection and 21× faster for backprojection when using a GPUbased cluster compared to a traditional multi-core version. Although a linear speed up was not reached, the proposed approach can be a good alternative for porting previous medical image reconstruction applications already implemented in C/C++ or even with CUDA or OpenCL programming models. Our solution enables the automatic detection of the GPU devices and execution on CPU and GPU tasks at the same time under the same system, using all the available resources
    Keywords : Apache | Spark | GPU | Medical | image | processing | Computed | tomography | Iterative | reconstruction | algorithms


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 36
    حجم فایل: 4045 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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