365-06-002 ISMRM Abstract

Quantitative Evaluation of Deep Learning–Based Diffusion MRI Reconstruction in Neuro MRI

Primary: Diffusion - Diffusion Reconstruction

Secondary: Neuro

365-06-002 · Diffusion MRI Reconstruction Methods · Monday, 11 May, 5:05 PM–6:00 PM · Digital Posters Row F

Keywords: Deep learning reconstruction Diffusion-Weighted Imaging

Accepted

Edward J Peake ¹, Ilse Patterson¹

¹MRI, Cambridge University Hospital, Cambridge, United Kingdom

Presenting Author: Edward J Peake

Synopsis

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References

1. Knoll F, Murrell T, Sriram A, et al. Advancing machine learning for MR image reconstruction. Magn Reson Med. 2020; 84(6): 3054–3070. doi:10.1002/mrm.28351 (PMID: 32304689) [doi] [pmid]

2. Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 2018; 555(7697): 487–492. doi:10.1038/nature25988 (PMID: 29539638) [doi] [pmid]

3. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986; 161(2): 401–407. doi:10.1148/radiology.161.2.3763909 (PMID: 3763909) [doi] [pmid]

4. Brusatori MA, Cronin MJ, Hardy CJ, et al. Validation of deep-learning–based MR image reconstruction for diffusion-weighted imaging. J Magn Reson Imaging. 2022; 55(3): 749–760. doi:10.1002/jmri.27906 (PMID: 34782909) [doi] [pmid]

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http://echo.ismrm.org/p/ISMRM2026/365-06-002