GROG Facilitated Compressed Sensing for Radial MRI
Incoherent k-space sampling plays a critical role in Compressed Sensing MRI (CS-MRI) by facilitating efficient utilization of the limited number of acquired samples to reconstruct high-quality images with reduced acquisition time and improved signal-to-noise ratio. Non-Cartesian sampling like radial...
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2024-01-01
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| author | Yumna Bilal Ibtisam Aslam Muhammad Faisal Siddiqui Omair Inam Kashif Amjad Jawad Hasan Alkhateeb Hammad Omer |
| author_facet | Yumna Bilal Ibtisam Aslam Muhammad Faisal Siddiqui Omair Inam Kashif Amjad Jawad Hasan Alkhateeb Hammad Omer |
| author_sort | Yumna Bilal |
| collection | DOAJ |
| description | Incoherent k-space sampling plays a critical role in Compressed Sensing MRI (CS-MRI) by facilitating efficient utilization of the limited number of acquired samples to reconstruct high-quality images with reduced acquisition time and improved signal-to-noise ratio. Non-Cartesian sampling like radial, spiral, or randomly sampled trajectories are faster and provide more incoherent k-space compared to Cartesian sampling. However, maintaining the reconstruction quality at high acceleration factors is still challenging with non-Cartesian acquisitions. Addressing these challenges typically involves the use of a GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) operator generated Bunched Phase Encoding data together with a conjugate gradient (CG) reconstruction algorithm, that mimics the functionality of the oscillating gradients required for bunched phase encoding sampling. However, the CG reconstruction method using GRAPPA Operator Gridding (GROG)-generated bunched points is limited to lower acceleration factors (<inline-formula> <tex-math notation="LaTeX">$AF= 2\sim 6 $ </tex-math></inline-formula>). In this paper, a novel CS-based reconstruction framework is proposed leveraging enhanced incoherence along with added redundancy of GROG-generated BPE data, to generate artifact-free images from highly undersampled radial acquisitions. In the proposed framework, GRAPPA operator gridding is applied as a first step, on multi-coil undersampled radial data to generate randomly blipped BPE points. The GROG-generated BPE points (non-Cartesian data) are mapped to the Cartesian grid using self-calibrating GRAPPA operator gridding prior to CS-based image reconstruction. To better understand how the GROG-generated bunched points affect the reconstruction quality, sensitivity maps are not explicitly estimated during the CS reconstruction as a first choice. However, incorporating sensitivity maps of multiple channels helps balance data fidelity and regularization; therefore, the performance of the proposed method using coil sensitivity maps has also been investigated. The proposed methods have been validated with both the simulated and in-vivo multi-coil radial datasets. A comparison between the proposed methods and contemporary CS-based reconstruction methods is performed using quantifying parameters such as Artifact Power, Signal-to-Noise ratio, and Root Mean Square Error. The reconstructed images of the proposed method are also subjectively evaluated by expert radiologists. Experimental results show that the proposed methods yield superior performance, both quantitatively and qualitatively at higher acceleration factors (<inline-formula> <tex-math notation="LaTeX">$ upto~AF= 14$ </tex-math></inline-formula>) in comparison with the contemporary CS-based image reconstruction techniques. |
| format | Article |
| id | doaj-art-7fc8a615791b47df9cbf8dc317b186a6 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-7fc8a615791b47df9cbf8dc317b186a62025-08-20T02:48:46ZengIEEEIEEE Access2169-35362024-01-011217844117845910.1109/ACCESS.2024.349504410749833GROG Facilitated Compressed Sensing for Radial MRIYumna Bilal0https://orcid.org/0000-0002-4445-6195Ibtisam Aslam1Muhammad Faisal Siddiqui2https://orcid.org/0000-0003-0847-4940Omair Inam3https://orcid.org/0000-0003-0394-3533Kashif Amjad4Jawad Hasan Alkhateeb5https://orcid.org/0000-0001-7611-7887Hammad Omer6Department of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, PakistanDepartment of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, PakistanDepartment of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, PakistanDepartment of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, PakistanComputer Engineering Department, College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Dhahran, Saudi ArabiaComputer Engineering Department, College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Dhahran, Saudi ArabiaDepartment of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, PakistanIncoherent k-space sampling plays a critical role in Compressed Sensing MRI (CS-MRI) by facilitating efficient utilization of the limited number of acquired samples to reconstruct high-quality images with reduced acquisition time and improved signal-to-noise ratio. Non-Cartesian sampling like radial, spiral, or randomly sampled trajectories are faster and provide more incoherent k-space compared to Cartesian sampling. However, maintaining the reconstruction quality at high acceleration factors is still challenging with non-Cartesian acquisitions. Addressing these challenges typically involves the use of a GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) operator generated Bunched Phase Encoding data together with a conjugate gradient (CG) reconstruction algorithm, that mimics the functionality of the oscillating gradients required for bunched phase encoding sampling. However, the CG reconstruction method using GRAPPA Operator Gridding (GROG)-generated bunched points is limited to lower acceleration factors (<inline-formula> <tex-math notation="LaTeX">$AF= 2\sim 6 $ </tex-math></inline-formula>). In this paper, a novel CS-based reconstruction framework is proposed leveraging enhanced incoherence along with added redundancy of GROG-generated BPE data, to generate artifact-free images from highly undersampled radial acquisitions. In the proposed framework, GRAPPA operator gridding is applied as a first step, on multi-coil undersampled radial data to generate randomly blipped BPE points. The GROG-generated BPE points (non-Cartesian data) are mapped to the Cartesian grid using self-calibrating GRAPPA operator gridding prior to CS-based image reconstruction. To better understand how the GROG-generated bunched points affect the reconstruction quality, sensitivity maps are not explicitly estimated during the CS reconstruction as a first choice. However, incorporating sensitivity maps of multiple channels helps balance data fidelity and regularization; therefore, the performance of the proposed method using coil sensitivity maps has also been investigated. The proposed methods have been validated with both the simulated and in-vivo multi-coil radial datasets. A comparison between the proposed methods and contemporary CS-based reconstruction methods is performed using quantifying parameters such as Artifact Power, Signal-to-Noise ratio, and Root Mean Square Error. The reconstructed images of the proposed method are also subjectively evaluated by expert radiologists. Experimental results show that the proposed methods yield superior performance, both quantitatively and qualitatively at higher acceleration factors (<inline-formula> <tex-math notation="LaTeX">$ upto~AF= 14$ </tex-math></inline-formula>) in comparison with the contemporary CS-based image reconstruction techniques.https://ieeexplore.ieee.org/document/10749833/MRIimage reconstructioncompressed sensingbunch phase encodingparallel imagingGROG |
| spellingShingle | Yumna Bilal Ibtisam Aslam Muhammad Faisal Siddiqui Omair Inam Kashif Amjad Jawad Hasan Alkhateeb Hammad Omer GROG Facilitated Compressed Sensing for Radial MRI IEEE Access MRI image reconstruction compressed sensing bunch phase encoding parallel imaging GROG |
| title | GROG Facilitated Compressed Sensing for Radial MRI |
| title_full | GROG Facilitated Compressed Sensing for Radial MRI |
| title_fullStr | GROG Facilitated Compressed Sensing for Radial MRI |
| title_full_unstemmed | GROG Facilitated Compressed Sensing for Radial MRI |
| title_short | GROG Facilitated Compressed Sensing for Radial MRI |
| title_sort | grog facilitated compressed sensing for radial mri |
| topic | MRI image reconstruction compressed sensing bunch phase encoding parallel imaging GROG |
| url | https://ieeexplore.ieee.org/document/10749833/ |
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