Multi-Resolution Parallel Magnetic Resonance Image Reconstruction in Mobile Computing-Based IoT
In the mobile computing-based Internet of Things, the computational complexity of applications is constrained by the capacity of the user equipment. In order to reduce the computational complexity of compressed sensing (CS)-based magnetic resonance image (MRI) reconstruction algorithms, we propose a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8630952/ |
| _version_ | 1818558599439319040 |
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| author | Yan Chen Qinglin Zhao Xiping Hu Bin Hu |
| author_facet | Yan Chen Qinglin Zhao Xiping Hu Bin Hu |
| author_sort | Yan Chen |
| collection | DOAJ |
| description | In the mobile computing-based Internet of Things, the computational complexity of applications is constrained by the capacity of the user equipment. In order to reduce the computational complexity of compressed sensing (CS)-based magnetic resonance image (MRI) reconstruction algorithms, we propose a novel multi-resolution-based parallel MRI reconstruction framework in this paper. We break down CS-based MRI reconstruction problem into four independent low-resolution image reconstruction sub-problems. Compared with the original problem, each sub-problem has a lower computational complexity. Assigned to four cores of the central processing unit (CPU), the sub-problems are solved simultaneously, and therefore the MRI reconstruction is accelerated. The combination of reconstructed low-resolution images achieves high-resolution image reconstruction. The proposed framework is applicable to the state-of-the-art CS-based MRI reconstruction algorithms to compute low-resolution images and involves multi-resolution processing. Compared with conventional serial computing, the proposed MRI reconstruction framework speeds at least four times up. Therefore, the parallel computation framework is especially suitable for widely used mobile devices with lower computational capability than workstations. To validate and evaluate the proposed scheme, when selecting the outstanding MRI reconstructing algorithm fast dictionary learning method on classified patches for numerical simulation, peak-signal-to-noise-ratio values of parallel reconstruction results are at least 0.929 dB higher than that of serial computation reconstruction results calculated by classical MRI reconstruction algorithm. |
| first_indexed | 2024-12-14T00:14:25Z |
| format | Article |
| id | doaj.art-2e041d4527f246df907bce64af85b031 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:14:25Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2e041d4527f246df907bce64af85b0312022-12-21T23:25:37ZengIEEEIEEE Access2169-35362019-01-017156231563310.1109/ACCESS.2019.28946948630952Multi-Resolution Parallel Magnetic Resonance Image Reconstruction in Mobile Computing-Based IoTYan Chen0Qinglin Zhao1Xiping Hu2Bin Hu3https://orcid.org/0000-0002-1320-1130School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaIn the mobile computing-based Internet of Things, the computational complexity of applications is constrained by the capacity of the user equipment. In order to reduce the computational complexity of compressed sensing (CS)-based magnetic resonance image (MRI) reconstruction algorithms, we propose a novel multi-resolution-based parallel MRI reconstruction framework in this paper. We break down CS-based MRI reconstruction problem into four independent low-resolution image reconstruction sub-problems. Compared with the original problem, each sub-problem has a lower computational complexity. Assigned to four cores of the central processing unit (CPU), the sub-problems are solved simultaneously, and therefore the MRI reconstruction is accelerated. The combination of reconstructed low-resolution images achieves high-resolution image reconstruction. The proposed framework is applicable to the state-of-the-art CS-based MRI reconstruction algorithms to compute low-resolution images and involves multi-resolution processing. Compared with conventional serial computing, the proposed MRI reconstruction framework speeds at least four times up. Therefore, the parallel computation framework is especially suitable for widely used mobile devices with lower computational capability than workstations. To validate and evaluate the proposed scheme, when selecting the outstanding MRI reconstructing algorithm fast dictionary learning method on classified patches for numerical simulation, peak-signal-to-noise-ratio values of parallel reconstruction results are at least 0.929 dB higher than that of serial computation reconstruction results calculated by classical MRI reconstruction algorithm.https://ieeexplore.ieee.org/document/8630952/Compressed sensingimage reconstructionmagnetic resonance imagingmobile devicesmulti-resolutionparallel processing |
| spellingShingle | Yan Chen Qinglin Zhao Xiping Hu Bin Hu Multi-Resolution Parallel Magnetic Resonance Image Reconstruction in Mobile Computing-Based IoT IEEE Access Compressed sensing image reconstruction magnetic resonance imaging mobile devices multi-resolution parallel processing |
| title | Multi-Resolution Parallel Magnetic Resonance Image Reconstruction in Mobile Computing-Based IoT |
| title_full | Multi-Resolution Parallel Magnetic Resonance Image Reconstruction in Mobile Computing-Based IoT |
| title_fullStr | Multi-Resolution Parallel Magnetic Resonance Image Reconstruction in Mobile Computing-Based IoT |
| title_full_unstemmed | Multi-Resolution Parallel Magnetic Resonance Image Reconstruction in Mobile Computing-Based IoT |
| title_short | Multi-Resolution Parallel Magnetic Resonance Image Reconstruction in Mobile Computing-Based IoT |
| title_sort | multi resolution parallel magnetic resonance image reconstruction in mobile computing based iot |
| topic | Compressed sensing image reconstruction magnetic resonance imaging mobile devices multi-resolution parallel processing |
| url | https://ieeexplore.ieee.org/document/8630952/ |
| work_keys_str_mv | AT yanchen multiresolutionparallelmagneticresonanceimagereconstructioninmobilecomputingbasediot AT qinglinzhao multiresolutionparallelmagneticresonanceimagereconstructioninmobilecomputingbasediot AT xipinghu multiresolutionparallelmagneticresonanceimagereconstructioninmobilecomputingbasediot AT binhu multiresolutionparallelmagneticresonanceimagereconstructioninmobilecomputingbasediot |