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Time‐efficient, high‐resolution 3T whole‐brain relaxometry using 3D‐QALAS with wave‐CAIPI readouts.

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Title: Time‐efficient, high‐resolution 3T whole‐brain relaxometry using 3D‐QALAS with wave‐CAIPI readouts.
Authors: Cho, Jaejin1,2 (AUTHOR), Gagoski, Borjan2,3 (AUTHOR) borjan.gagoski@childrens.harvard.edu, Kim, Tae Hyung4 (AUTHOR), Wang, Fuyixue1,2 (AUTHOR), Manhard, Mary Kate5 (AUTHOR), Dean, Douglas6 (AUTHOR), Kecskemeti, Steven6 (AUTHOR), Caprihan, Arvind7 (AUTHOR), Lo, Wei‐Ching8 (AUTHOR), Splitthoff, Daniel Nico9 (AUTHOR), Liu, Wei9 (AUTHOR), Polak, Daniel9 (AUTHOR), Cauley, Stephen1,2 (AUTHOR), Setsompop, Kawin10,11 (AUTHOR), Grant, Patricia Ellen2,3 (AUTHOR), Bilgic, Berkin1,2,12 (AUTHOR)
Source: Magnetic Resonance in Medicine. Feb2024, Vol. 91 Issue 2, p630-639. 10p.
Subjects: National Institute of Standards & Technology (U.S.), Volunteers, Protons, Volunteer service
Abstract: Purpose: Volumetric, high‐resolution, quantitative mapping of brain‐tissue relaxation properties is hindered by long acquisition times and SNR challenges. This study combines time‐efficient wave–controlled aliasing in parallel imaging (wave‐CAIPI) readouts with the 3D quantification using an interleaved Look‐Locker acquisition sequence with a T2 preparation pulse (3D‐QALAS), enabling full‐brain quantitative T1, T2, and proton density (PD) maps at 1.15‐mm3 isotropic voxels in 3 min. Methods: Wave‐CAIPI readouts were embedded in the standard 3D‐QALAS encoding scheme, enabling full‐brain quantitative parameter maps (T1, T2, and PD) at acceleration factors of R = 3 × 2 with minimum SNR loss due to g‐factor penalties. The quantitative parameter maps were estimated using a dictionary‐based mapping algorithm incorporating inversion efficiency and B1‐field inhomogeneity effects. The parameter maps using the accelerated protocol were quantitatively compared with those obtained from the conventional 3D‐QALAS sequence using GRAPPA acceleration of R = 2 in the ISMRM/NIST phantom, and in 10 healthy volunteers. Results: When tested in both the ISMRM/NIST phantom and 10 healthy volunteers, the quantitative maps using the accelerated protocol showed excellent agreement against those obtained from conventional 3D‐QALAS at RGRAPPA = 2. Conclusion: Three‐dimensional QALAS enhanced with wave‐CAIPI readouts enables time‐efficient, full‐brain quantitative T1, T2, and PD mapping at 1.15 mm3 in 3 min at R = 3 × 2 acceleration. The quantitative maps obtained from the accelerated wave‐CAIPI 3D‐QALAS protocol showed very similar values to those from the standard 3D‐QALAS (R = 2) protocol, alluding to the robustness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]
Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Time‐efficient, high‐resolution 3T whole‐brain relaxometry using 3D‐QALAS with wave‐CAIPI readouts.
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  Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Feb2024, Vol. 91 Issue 2, p630-639. 10p.
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  Data: <searchLink fieldCode="DE" term="%22National+Institute+of+Standards+%26+Technology+%28U%2ES%2E%29%22">National Institute of Standards & Technology (U.S.)</searchLink><br /><searchLink fieldCode="DE" term="%22Volunteers%22">Volunteers</searchLink><br /><searchLink fieldCode="DE" term="%22Protons%22">Protons</searchLink><br /><searchLink fieldCode="DE" term="%22Volunteer+service%22">Volunteer service</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Volumetric, high‐resolution, quantitative mapping of brain‐tissue relaxation properties is hindered by long acquisition times and SNR challenges. This study combines time‐efficient wave–controlled aliasing in parallel imaging (wave‐CAIPI) readouts with the 3D quantification using an interleaved Look‐Locker acquisition sequence with a T2 preparation pulse (3D‐QALAS), enabling full‐brain quantitative T1, T2, and proton density (PD) maps at 1.15‐mm3 isotropic voxels in 3 min. Methods: Wave‐CAIPI readouts were embedded in the standard 3D‐QALAS encoding scheme, enabling full‐brain quantitative parameter maps (T1, T2, and PD) at acceleration factors of R = 3 × 2 with minimum SNR loss due to g‐factor penalties. The quantitative parameter maps were estimated using a dictionary‐based mapping algorithm incorporating inversion efficiency and B1‐field inhomogeneity effects. The parameter maps using the accelerated protocol were quantitatively compared with those obtained from the conventional 3D‐QALAS sequence using GRAPPA acceleration of R = 2 in the ISMRM/NIST phantom, and in 10 healthy volunteers. Results: When tested in both the ISMRM/NIST phantom and 10 healthy volunteers, the quantitative maps using the accelerated protocol showed excellent agreement against those obtained from conventional 3D‐QALAS at RGRAPPA = 2. Conclusion: Three‐dimensional QALAS enhanced with wave‐CAIPI readouts enables time‐efficient, full‐brain quantitative T1, T2, and PD mapping at 1.15 mm3 in 3 min at R = 3 × 2 acceleration. The quantitative maps obtained from the accelerated wave‐CAIPI 3D‐QALAS protocol showed very similar values to those from the standard 3D‐QALAS (R = 2) protocol, alluding to the robustness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mrm.29865
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        Text: English
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