Automated parameter selection for accelerated MRI reconstruction via low-rank modeling of local k-space neighborhoods
Purpose - Image quality in accelerated MRI rests on careful selection of various reconstruction parameters. A common yet tedious and error-prone practice is to hand-tune each parameter to attain visually appealing reconstructions. Here, we propose a parameter tuning strategy to automate hybrid paral...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2023
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| In: |
Zeitschrift für medizinische Physik
Year: 2023, Volume: 33, Issue: 2, Pages: 203-219 |
| ISSN: | 1876-4436 |
| DOI: | 10.1016/j.zemedi.2022.02.002 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.zemedi.2022.02.002 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0939388922000083 
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| Author Notes: | Efe Ilicak, Emine Ulku Saritas, Tolga Çukur |
MARC
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| 245 | 1 | 0 | |a Automated parameter selection for accelerated MRI reconstruction via low-rank modeling of local k-space neighborhoods |c Efe Ilicak, Emine Ulku Saritas, Tolga Çukur |
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| 520 | |a Purpose - Image quality in accelerated MRI rests on careful selection of various reconstruction parameters. A common yet tedious and error-prone practice is to hand-tune each parameter to attain visually appealing reconstructions. Here, we propose a parameter tuning strategy to automate hybrid parallel imaging (PI) - compressed sensing (CS) reconstructions via low-rank modeling of local k-space neighborhoods (LORAKS) supplemented with sparsity regularization in wavelet and total variation (TV) domains. - Methods - For low-rank regularization, we leverage a soft-thresholding operation based on singular values for matrix rank selection in LORAKS. For sparsity regularization, we employ Stein's unbiased risk estimate criterion to select the wavelet regularization parameter and local standard deviation of reconstructions to select the TV regularization parameter. Comprehensive demonstrations are presented on a numerical brain phantom and in vivo brain and knee acquisitions. Quantitative assessments are performed via PSNR, SSIM and NMSE metrics. - Results - The proposed hybrid PI-CS method improves reconstruction quality compared to PI-only techniques, and it achieves on par image quality to reconstructions with brute-force optimization of reconstruction parameters. These results are prominent across several different datasets and the range of examined acceleration rates. - Conclusion - A data-driven parameter tuning strategy to automate hybrid PI-CS reconstructions is presented. The proposed method achieves reliable reconstructions of accelerated multi-coil MRI datasets without the need for exhaustive hand-tuning of reconstruction parameters. | ||
| 650 | 4 | |a Compressed sensing | |
| 650 | 4 | |a Low rank | |
| 650 | 4 | |a Parallel imaging | |
| 650 | 4 | |a Parameter selection | |
| 650 | 4 | |a Regularization | |
| 650 | 4 | |a Self tuning | |
| 700 | 1 | |a Saritas, Emine Ulku |e VerfasserIn |0 (DE-588)1371479488 |0 (DE-627)193043748X |4 aut | |
| 700 | 1 | |a Çukur, Tolga |e VerfasserIn |0 (DE-588)137147981X |0 (DE-627)1930437889 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Zeitschrift für medizinische Physik |d Amsterdam [u.a.] : Elsevier, 1990 |g 33(2023), 2, Seite 203-219 |w (DE-627)510617662 |w (DE-600)2231492-1 |w (DE-576)272350818 |x 1876-4436 |7 nnas |a Automated parameter selection for accelerated MRI reconstruction via low-rank modeling of local k-space neighborhoods |
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| 787 | 0 | 8 | |i Errata |a Ilicak, Efe, 1992 - |t Erratum to “Automated parameter selection for accelerated MRI reconstruction via low-rank modeling of local k-space neighborhoods” [Z Med. Phys. 33 (2023) 203-219] |d 2025 |w (DE-627)1930437145 |
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