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      Reconstruction by calibration over tensors for multi-coil multi-acquisition balanced SSFP imaging

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      Embargo Lift Date: 2019-02-20
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      Author
      Biyik, Erdem
      Ilicak, Efe
      Çukur, Tolga
      Date
      2018
      Source Title
      Magnetic Resonance in Medicine
      Print ISSN
      0740-3194
      Electronic ISSN
      1522-2594
      Publisher
      John Wiley & Sons
      Volume
      79
      Issue
      5
      Pages
      2542 - 2554
      Language
      English
      Type
      Article
      Item Usage Stats
      135
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      97
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      Abstract
      Purpose: To develop a rapid imaging framework for balanced steady-state free precession (bSSFP) that jointly reconstructs undersampled data (by a factor of R) across multiple coils (D) and multiple acquisitions (N). To devise a multi-acquisition coil compression technique for improved computational efficiency. Methods: The bSSFP image for a given coil and acquisition is modeled to be modulated by a coil sensitivity and a bSSFP profile. The proposed reconstruction by calibration over tensors (ReCat) recovers missing data by tensor interpolation over the coil and acquisition dimensions. Coil compression is achieved using a new method based on multilinear singular value decomposition (MLCC). ReCat is compared with iterative self-consistent parallel imaging (SPIRiT) and profile encoding (PE-SSFP) reconstructions. Results: Compared to parallel imaging or profile-encoding methods, ReCat attains sensitive depiction of high-spatial-frequency information even at higher R. In the brain, ReCat improves peak SNR (PSNR) by 1.1 ± 1.0 dB over SPIRiT and by 0.9 ± 0.3 dB over PE-SSFP (mean ± SD across subjects; average for N = 2-8, R = 8-16). Furthermore, reconstructions based on MLCC achieve 0.8 ± 0.6 dB higher PSNR compared to those based on geometric coil compression (GCC) (average for N = 2-8, R = 4-16). Conclusion: ReCat is a promising acceleration framework for banding-artifact-free bSSFP imaging with high image quality; and MLCC offers improved computational efficiency for tensor-based reconstructions. Magn Reson Med 79:2542-2554, 2018.
      Keywords
      bSSFP
      Accelerated MRI
      Joint reconstruction
      Tensor
      Encoding
      Coil compression
      Permalink
      http://hdl.handle.net/11693/49962
      Published Version (Please cite this version)
      https://doi.org/10.1002/mrm.26902
      Collections
      • Aysel Sabuncu Brain Research Center (BAM) 197
      • Department of Electrical and Electronics Engineering 3608
      • National Magnetic Resonance Research Center (UMRAM) 197
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