• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • National Magnetic Resonance Research Center (UMRAM)
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • National Magnetic Resonance Research Center (UMRAM)
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

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

      Thumbnail
      View / Download
      3.3 Mb
      Author(s)
      Ilıcak, Efe
      Sarıtaş, Emine Ülkü
      Çukur, Tolga
      Date
      2022-02-01
      Source Title
      Zeitschrift fur Medizinische Physik
      Publisher
      Elsevier GmbH
      Pages
      1 - 17
      Language
      English
      Type
      Article
      Item Usage Stats
      7
      views
      5
      downloads
      Abstract
      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. © 2022
      Keywords
      Compressed sensing
      Low rank
      Parallel imaging
      Parameter selection
      Regularization
      Self tuning
      Permalink
      http://hdl.handle.net/11693/111966
      Published Version (Please cite this version)
      https://dx.doi.org/10.1016/j.zemedi.2022.02.002
      Collections
      • Department of Electrical and Electronics Engineering 4011
      • National Magnetic Resonance Research Center (UMRAM) 301
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCoursesThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCourses

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 2976
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy