DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model-Reference-Cited by-同舟云学术

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DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model

Published:2024-05-27 Issue: Volume:34 Page:1-4
ISSN:
Container-title:2024 IEEE International Symposium on Biomedical Imaging (ISBI)
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Author:

Hu Yuyang¹,Kothapalli Satya V. V. N.²,Gan Weijie³,Sukstanskii Alexander L.²,Wu Gregory F.⁴,Goyal Manu²,Yablonskiy Dmitriy A.²,Kamilov Ulugbek S.¹

Affiliation:

1. Washington University in St. Louis,Department of Electrical and Systems Engineering,St. Louis,MO,USA,63130

2. Washington University in St. Louis,Mallinckrodt Institute of Radiology,St. Louis,MO,USA,63110

3. Washington University in St. Louis,Department of Computer Science and Engineering,St. Louis,MO,USA,63130

4. Washington University in St. Louis School of Medicine,Department of Neurology,St. Louis,MO,USA,63130

Funder

Health

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635694.pdf?arnumber=10635694

Reference20 articles.

1. Magnetic resonance fingerprinting – An overview

2. Gradient Echo Plural Contrast Imaging — Signal model and derived contrasts: T2*, T1, Phase, SWI, T1f, FST2*and T2*-SWI

3. Fluid-Attenuated Inversion Recovery Magnetic Resonance Imaging Detects Cortical and Juxtacortical Multiple Sclerosis Lesions

4. Rapid three-dimensional T1-weighted MR imaging with the MP-RAGE sequence

5. Gradient-echo MR imaging: techniques and acronyms.

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