High-performance reconstruction of CT medical images by using out-of-core methods in GPU
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Title
High-performance reconstruction of CT medical images by using out-of-core methods in GPUDate
2022-03-02Publisher
ElsevierBibliographic citation
QUINTANA-ORTÍ, Gregorio, et al. High-performance reconstruction of CT medical images by using out-of-core methods in GPU. Computer Methods and Programs in Biomedicine, 2022, vol. 218, p. 106725.Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
Background and objective:Since Computed Tomography (CT) is one of the most widely used medical imaging tests, it is essential to work on methods that reduce the radiation the patient is exposed to. Although there are ... [+]
Background and objective:Since Computed Tomography (CT) is one of the most widely used medical imaging tests, it is essential to work on methods that reduce the radiation the patient is exposed to. Although there are several possible approaches to achieve this, we focus on reducing the exposure time through sparse sampling. With this approach, efficient algebraic methods are needed to be able to generate the images in real time, and since their computational cost is high, using high-performance computing is essential. Methods:In this paper we present a GPU (Graphics Processing Unit) software for solving the CT image reconstruction problem using the QR factorization performed with out-of-core (OOC) techniques. This implementation is optimized to reduce the data transfer times between disk, CPU, and GPU, as well as to overlap input/output operations and computations. Results:The experimental study shows that a block cache stored on main page-locked memory is more efficient than using a cache on GPU memory or mirroring it in both GPU and CPU memory. Compared to a CPU version, this implementation is up to 6.5 times faster, providing an improved image quality when compared to other reconstruction methods. Conclusions:The software developed is an optimized version of the QR factorization for GPU that allows the algebraic reconstruction of CT images with high quality and resolution, with a performance that can be compared with state-of-the-art methods used in clinical practice. This approach allows reducing the exposure time of the patient and thus the radiation dose. [-]
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Computer Methods and Programs in Biomedicine, Volume 218, May 2022Funder Name
Universitat Politècnica de València | Generalitat Valenciana | Ministerio de Ciencia, Innovación y Universidades (Spain) | FEDER funds.
Project code
PROMETEO/2018/035 | ACIF/2017/075 | RTI2018-098156-B-C54
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© 2022 The Author(s). Published by Elsevier B.V
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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