Improving phase‐based conductivity reconstruction by means of deep learning–based denoising of phase data for 3T MRI-Reference-Cited by-同舟云学术

Improving phase‐based conductivity reconstruction by means of deep learning–based denoising of phase data for 3T MRI

Published:2021-05-05 Issue:4 Volume:86 Page:2084-2094
ISSN:0740-3194
Container-title:Magnetic Resonance in Medicine
language:en
Short-container-title:Magn. Reson. Med.

Author:

Jung Kyu‐Jin¹^ORCID,Mandija Stefano²³^ORCID,Kim Jun‐Hyeong¹^ORCID,Ryu Kanghyun¹⁴^ORCID,Jung Soozy¹^ORCID,Cui Chuanjiang¹,Kim Soo‐Yeon⁵,Park Mina⁶,den Berg Cornelis A. T.²³,Kim Dong‐Hyun¹

Affiliation:

1. Department of Electrical and Electronic Engineering Yonsei University Seoul Republic of Korea

2. Computational Imaging Group for MR Diagnostic & Therapy Center for Image Sciences University Medical Center Utrecht Utrecht the Netherlands

3. Department of Radiotherapy Division of Imaging & Oncology University Medical Center Utrecht Utrecht the Netherlands

4. Department of Radiology Stanford University Stanford California USA

5. Department of Radiology Seoul National University Hospital Seoul Republic of Korea

6. Department of Radiology Gangnam Severance HospitalYonsei University College of Medicine Seoul Republic of Korea

Funder

Ministry of Science and ICT, South Korea

National Research Foundation of Korea

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28826

Reference63 articles.

1. Extraction of conductivity and permittivity using magnetic resonance imaging

2. Determination of Electric Conductivity and Local SAR Via B1 Mapping

3. Quantitative conductivity and permittivity imaging of the human brain using electric properties tomography

4. B 1+ Phase mapping at 7 T and its application for in vivo electrical conductivity mapping

5. Patient-individual local SAR determination: In vivo measurements and numerical validation

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