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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/551
DC FieldValueLanguage
dc.contributor.authorDinh Buu Sonen_US
dc.date.accessioned2020-03-12T17:03:47Z-
dc.date.available2020-03-12T17:03:47Z-
dc.date.issued2018-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/551-
dc.description.abstractMagnetic resonance imaging (MRI) is commonly used in radiology to form the pictures of the anatomy in order to probe the structure and physical function of biological tissues in both clinical and research field. In years, MRI is widely used to detect cancer and monitor treatment, but lengthy acquisition time limits its use due to cost and considerations of patient comfort and compliance. At present, many MRI experiments are specially designed to find the effective way to reconstruct images quickly and clearly from less substantial acquired data using sparse sampling model, thus, reducing scan acquisition times To address these limitations, Liang and Lauterbur developed the generalized series (GS) model, using a high-resolution image as reference for reconstruction. The theory argues that high definition image can be recovered from signal information in a small portion of voxels acquired in sparsity. However, high quality image always comes with short reconstruction and quick scanning time, which results a huge amount of computational load that cannot be solved effectively by CPU. GPU is designed to solve the problem like this. In this thesis, the GS reconstruction was implemented and evaluated in both CPU and GPU. Experiment results implied computational acceleration on GPU could make advanced MRI attractive in clinical settings, thereby improving the quality of MR images across a broad range of applications. The GPU-based GS reconstruction method gained up to 32 times speed-up factor compared to the CPU-based method in two different datasets: DCE-MRI and fMRI data. Moreover, the GPU-based reconstructed image quality could be considered to be acceptable since it was hard to reveal the differences between GPU-based images to the ground truth by the human eye inspection. Relative to a conventional reconstruction, the advanced GPU reconstruction reduces the error in the reconstructed images from 14% to 3%en_US
dc.language.isoenen_US
dc.publisherVietnamese-German Universityen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectMagnetic resonance imaging (MRI)en_US
dc.subjectGS reconstructionen_US
dc.subjectBiological tissuesen_US
dc.titleAccelerating advanced under-sampled MRI reconstruction on cudaen_US
dc.typeThesisen_US
item.fulltextWith Fulltext-
item.languageiso639-1other-
item.grantfulltextrestricted-
Appears in Collections:Electrical Engineering and Information Technology (EEIT)
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