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Accelerated multi-shell diffusion MRI with Gaussian process estimated reconstruction of multi-band imaging

Abstract:

Purpose: This work aims to propose a robust reconstruction method exploiting shared information across shells to increase the acquisition speed of multi-shell diffusion MRI, enabling rapid tissue microstructure mapping.

Theory and Methods: Local q-space points share similar information. Gaussian Process can exploit the q-space smoothness in a data-driven way and provide q-space signal estimation based on the signals from a q-space neighborhood. The Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) method uses the signal estimation from Gaussian process as a prior in a joint k-q reconstruction and improves image quality under high acceleration factors compared to conventional (k-only) reconstruction. In this work, we extend the DAGER method by introducing a multi-shell covariance function and correcting for Rician noise distribution in magnitude data when fitting the Gaussian process model. The method was evaluated with both simulation and in vivo data.

Results:> Simulated and in-vivo results demonstrate that the proposed method can significantly improve the image quality of reconstructed dMRI data with high acceleration both in-plane and slice-wise, achieving a total acceleration factor of 12. The improvement of image quality allows more robust diffusion model fitting compared to conventional reconstruction methods, enabling advanced multi-shell diffusion analysis within much shorter scan time.

Conclusion: The proposed method enables highly accelerated dMRI which can shorten the scan time of multi-shell dMRI without sacrificing quality compared to conventional practice. This may facilitate a wider application of advanced dMRI models in basic and clinical neuroscience.

Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/mrm.30518

Authors

More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Oxford college:
St Edmund Hall
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author
ORCID:
0000-0002-5020-5165


More from this funder
Funder identifier:
https://ror.org/029chgv08
Grant:
203139/A/16/Z
224573/Z/21/Z
203139/Z/16/Z
More from this funder
Funder identifier:
https://ror.org/0526snb40
Grant:
RF\201819\18\92
More from this funder
Funder identifier:
https://ror.org/0187kwz08
Grant:
NIHR203316


Publisher:
Wiley
Journal:
Magnetic Resonance in Medicine More from this journal
Volume:
94
Issue:
2
Pages:
694-712
Publication date:
2025-04-06
Acceptance date:
2025-03-13
DOI:
EISSN:
1522-2594
ISSN:
0740-3194


Language:
English
Keywords:
Pubs id:
2095334
Local pid:
pubs:2095334
Deposit date:
2025-03-20
ARK identifier:

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