Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact‐free and denoised images-Reference-Cited by-同舟云学术

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Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact‐free and denoised images

Published:2020-07-21 Issue:6 Volume:84 Page:2932-2942
ISSN:0740-3194
Container-title:Magnetic Resonance in Medicine
language:en
Short-container-title:Magn Reson Med

Author:

Torop Max¹^ORCID,Kothapalli Satya V. V. N.²,Sun Yu¹,Liu Jiaming³,Kahali Sayan²,Yablonskiy Dmitriy A.²^ORCID,Kamilov Ulugbek S.¹³^ORCID

Affiliation:

1. Department of Computer Science and Engineering Washington University in St. Louis St. Louis MO USA

2. Department of Radiology Washington University in St. Louis St. Louis MO USA

3. Department of Electrical and Systems Engineering University in St. Louis St. Louis MO USA

Funder

NSF

NIH/NIA

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28344

Reference36 articles.

1. Multicenter R2* mapping in the healthy brain

2. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation

3. Quantitation of intrinsic magnetic susceptibility-related effects in a tissue matrix. Phantom study

4. Postprocessing technique to correct for background gradients in image-basedR*2 measurements

5. R 2* mapping in the presence of macroscopic B0 field variations

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