Automated parameter selection for accelerated mri reconstruction via low-rank modeling of local k-space neighborhoods

Simple item page
buir.contributor.authorIlıcak, Efe
buir.contributor.authorSarıtaş, Emine Ülkü
buir.contributor.authorÇukur, Tolga
buir.contributor.orcidSarıtaş, Emine Ülkü|0000-0001-8551-1077
buir.contributor.orcidÇukur, Tolga|0000-0002-2296-851X
dc.citation.epage17en_US
dc.citation.spage1en_US
dc.contributor.authorIlıcak, Efe
dc.contributor.authorSarıtaş, Emine Ülkü
dc.contributor.authorÇukur, Tolga
dc.date.accessioned2023-02-28T14:23:24Z
dc.date.available2023-02-28T14:23:24Z
dc.date.issued2022-02-01
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.description.abstractPurpose: 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. © 2022en_US
dc.identifier.doi10.1016/j.zemedi.2022.02.002en_US
dc.identifier.urihttp://hdl.handle.net/11693/111966
dc.language.isoEnglishen_US
dc.publisherElsevier GmbHen_US
dc.relation.isversionofhttps://dx.doi.org/10.1016/j.zemedi.2022.02.002en_US
dc.source.titleZeitschrift fur Medizinische Physiken_US
dc.subjectCompressed sensingen_US
dc.subjectLow ranken_US
dc.subjectParallel imagingen_US
dc.subjectParameter selectionen_US
dc.subjectRegularizationen_US
dc.subjectSelf tuningen_US
dc.titleAutomated parameter selection for accelerated mri reconstruction via low-rank modeling of local k-space neighborhoodsen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Automated_parameter_selection_for_accelerated_mri_reconstruction_via_low-rank_modeling_of_local_k-space_neighborhoods.pdf
Size:
3.32 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.69 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

National Magnetic Resonance Research Center (UMRAM)
Department of Electrical and Electronics Engineering