Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme
Compressed Sensing (CS) theory breaks the Nyquist theorem through random under-sampling and enables us to reconstruct a signal from 10%-50% samples. Magnetic Resonance Imaging (MRI) is a good candidate for application of compressed sensing techniques due to i) implicit sparsity in MR images and ii)...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9123887/ |
| _version_ | 1818433272080760832 |
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| author | Maria Murad Muhammad Bilal Abdul Jalil Ahmad Ali Khizer Mehmood Baber Khan |
| author_facet | Maria Murad Muhammad Bilal Abdul Jalil Ahmad Ali Khizer Mehmood Baber Khan |
| author_sort | Maria Murad |
| collection | DOAJ |
| description | Compressed Sensing (CS) theory breaks the Nyquist theorem through random under-sampling and enables us to reconstruct a signal from 10%-50% samples. Magnetic Resonance Imaging (MRI) is a good candidate for application of compressed sensing techniques due to i) implicit sparsity in MR images and ii) inherently slow data acquisition process. In multi-slice MRI, strong inter-slice correlation has been exploited for further scan time reduction through interpolated compressed sensing (iCS). In this paper, a novel fast interpolated compressed sensing (FiCS) technique is proposed based on 2D variable density under-sampling (VRDU) scheme. The 2D-VRDU scheme improves the result by sampling the high energy central part of the k-space slices. The novel interpolation technique takes two consecutive slices and estimates the missing samples of the target slice (T slice) from its left slice (L slice). Compared to the previous methods, slices recovered with the proposed FiCS technique have a maximum correlation with their corresponding original slices. The proposed FiCS technique is evaluated by using both subjective and objective assessment. In subjective assessment, our proposed technique shows less partial volume loss compared to existing techniques. For objective assessment different performance metrics, such as structural similarity index measurement (SSIM), peak signal to noise ratio (PSNR), mean square error (MSE) and correlation, are used and compared with existing interpolation techniques. Simulation results on knee and brain dataset shows that the proposed FiCS technique has improved image quality and performance with even reduced scan time, lower computational complexity and maximum information content. |
| first_indexed | 2024-12-14T16:18:27Z |
| format | Article |
| id | doaj.art-01449287dffc4fe49c5746537368c025 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:18:27Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-01449287dffc4fe49c5746537368c0252022-12-21T22:54:52ZengIEEEIEEE Access2169-35362020-01-01811745211746610.1109/ACCESS.2020.30047319123887Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling SchemeMaria Murad0https://orcid.org/0000-0002-9633-9927Muhammad Bilal1https://orcid.org/0000-0001-8768-3526Abdul Jalil2https://orcid.org/0000-0001-7408-5922Ahmad Ali3https://orcid.org/0000-0002-5420-1743Khizer Mehmood4https://orcid.org/0000-0002-4278-6166Baber Khan5https://orcid.org/0000-0002-1402-2433Department of Electrical Engineering, International Islamic University Islamabad, Islamabad, PakistanDepartment of Electrical Engineering, International Islamic University Islamabad, Islamabad, PakistanDepartment of Electrical Engineering, International Islamic University Islamabad, Islamabad, PakistanDepartment of Software Engineering, Bahria University, Islamabad, PakistanDepartment of Electrical Engineering, International Islamic University Islamabad, Islamabad, PakistanDepartment of Electrical Engineering, International Islamic University Islamabad, Islamabad, PakistanCompressed Sensing (CS) theory breaks the Nyquist theorem through random under-sampling and enables us to reconstruct a signal from 10%-50% samples. Magnetic Resonance Imaging (MRI) is a good candidate for application of compressed sensing techniques due to i) implicit sparsity in MR images and ii) inherently slow data acquisition process. In multi-slice MRI, strong inter-slice correlation has been exploited for further scan time reduction through interpolated compressed sensing (iCS). In this paper, a novel fast interpolated compressed sensing (FiCS) technique is proposed based on 2D variable density under-sampling (VRDU) scheme. The 2D-VRDU scheme improves the result by sampling the high energy central part of the k-space slices. The novel interpolation technique takes two consecutive slices and estimates the missing samples of the target slice (T slice) from its left slice (L slice). Compared to the previous methods, slices recovered with the proposed FiCS technique have a maximum correlation with their corresponding original slices. The proposed FiCS technique is evaluated by using both subjective and objective assessment. In subjective assessment, our proposed technique shows less partial volume loss compared to existing techniques. For objective assessment different performance metrics, such as structural similarity index measurement (SSIM), peak signal to noise ratio (PSNR), mean square error (MSE) and correlation, are used and compared with existing interpolation techniques. Simulation results on knee and brain dataset shows that the proposed FiCS technique has improved image quality and performance with even reduced scan time, lower computational complexity and maximum information content.https://ieeexplore.ieee.org/document/9123887/Compressed sensingcompressed sensing reconstructioninterpolated compressed sensingmagnetic resonance imagingmulti-slice MRInonlinear conjugate gradient |
| spellingShingle | Maria Murad Muhammad Bilal Abdul Jalil Ahmad Ali Khizer Mehmood Baber Khan Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme IEEE Access Compressed sensing compressed sensing reconstruction interpolated compressed sensing magnetic resonance imaging multi-slice MRI nonlinear conjugate gradient |
| title | Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme |
| title_full | Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme |
| title_fullStr | Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme |
| title_full_unstemmed | Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme |
| title_short | Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme |
| title_sort | efficient reconstruction technique for multi slice cs mri using novel interpolation and 2d sampling scheme |
| topic | Compressed sensing compressed sensing reconstruction interpolated compressed sensing magnetic resonance imaging multi-slice MRI nonlinear conjugate gradient |
| url | https://ieeexplore.ieee.org/document/9123887/ |
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