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High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction.
Liao, Congyu; Yarach, Uten; Cao, Xiaozhi; Iyer, Siddharth Srinivasan; Wang, Nan; Kim, Tae Hyung; Tian, Qiyuan; Bilgic, Berkin; Kerr, Adam B; Setsompop, Kawin.
Afiliação
  • Liao C; Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Yarach U; Radiologic Technology Department, Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand.
  • Cao X; Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA. Electronic address: xiaozhic@stanford.edu.
  • Iyer SS; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Wang N; Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Kim TH; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Department of Computer Engineering, Hongik University, Seoul, South Korea.
  • Tian Q; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
  • Bilgic B; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
  • Kerr AB; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Stanford Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, USA.
  • Setsompop K; Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Neuroimage ; 275: 120168, 2023 07 15.
Article em En | MEDLINE | ID: mdl-37187364
PURPOSE: To develop a high-fidelity diffusion MRI acquisition and reconstruction framework with reduced echo-train-length for less T2* image blurring compared to typical highly accelerated echo-planar imaging (EPI) acquisitions at sub-millimeter isotropic resolution. METHODS: We first proposed a circular-EPI trajectory with partial Fourier sampling on both the readout and phase-encoding directions to minimize the echo-train-length and echo time. We then utilized this trajectory in an interleaved two-shot EPI acquisition with reversed phase-encoding polarity, to aid in the correction of off-resonance-induced image distortions and provide complementary k-space coverage in the missing partial Fourier regions. Using model-based reconstruction with structured low-rank constraint and smooth phase prior, we corrected the shot-to-shot phase variations across the two shots and recover the missing k-space data. Finally, we combined the proposed acquisition/reconstruction framework with an SNR-efficient RF-encoded simultaneous multi-slab technique, termed gSlider, to achieve high-fidelity 720 µm and 500 µm isotropic resolution in-vivo diffusion MRI. RESULTS: Both simulation and in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide distortion-corrected diffusion imaging at the mesoscale with markedly reduced T2*-blurring. The in-vivo results of 720 µm and 500 µm datasets show high-fidelity diffusion images with reduced image blurring and echo time using the proposed approaches. CONCLUSIONS: The proposed method provides high-quality distortion-corrected diffusion-weighted images with ∼40% reduction in the echo-train-length and T2* blurring at 500µm-isotropic-resolution compared to standard multi-shot EPI.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Imagem Ecoplanar Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Imagem Ecoplanar Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article