TY - JOUR
T1 - A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging
AU - Kim, Byungjai
AU - Schär, Michael
AU - Park, Hyun Wook
AU - Heo, Hye Young
N1 - Funding Information:
This work was supported in part by grants from the Rad BriteStar Award from the Department of Radiology Johns Hopkins University School of Medicine and by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute ( KHIDI ), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI14C1135 ).
Publisher Copyright:
© 2020 The Author(s)
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Semisolid magnetization transfer contrast (MTC) and chemical exchange saturation transfer (CEST) MRI based on MT phenomenon have shown potential to evaluate brain development, neurological, psychiatric, and neurodegenerative diseases. However, a qualitative MT ratio (MTR) metric commonly used in conventional MTC imaging is limited in the assessment of quantitative semisolid macromolecular proton exchange rates and concentrations. In addition, CEST signals measured by MTR asymmetry analysis are unavoidably contaminated by upfield nuclear Overhauser enhancement (NOE) signals of mobile and semisolid macromolecules. To address these issues, we developed an MTC-MR fingerprinting (MTC-MRF) technique to quantify tissue parameters, which further allows an estimation of accurate MTC signals at a certain CEST frequency offset. A pseudorandomized RF saturation scheme was used to generate unique MTC signal evolutions for different tissues and a supervised deep neural network was designed to extract tissue properties from measured MTC-MRF signals. Through detailed Bloch equation-based digital phantom and in vivo studies, we demonstrated that the MTC-MRF can quantify MTC characteristics with high accuracy and computational efficiency, compared to a conventional Bloch equation fitting approach, and provide baseline reference signals for CEST and NOE imaging. For validation, MTC-MRF images were synthesized using the tissue parameters estimated from the deep-learning method and compared with experimentally acquired MTC-MRF images as the reference standard. The proposed MTC-MRF framework can provide quantitative 3D MTC, CEST, and NOE imaging of the human brain within a clinically acceptable scan time.
AB - Semisolid magnetization transfer contrast (MTC) and chemical exchange saturation transfer (CEST) MRI based on MT phenomenon have shown potential to evaluate brain development, neurological, psychiatric, and neurodegenerative diseases. However, a qualitative MT ratio (MTR) metric commonly used in conventional MTC imaging is limited in the assessment of quantitative semisolid macromolecular proton exchange rates and concentrations. In addition, CEST signals measured by MTR asymmetry analysis are unavoidably contaminated by upfield nuclear Overhauser enhancement (NOE) signals of mobile and semisolid macromolecules. To address these issues, we developed an MTC-MR fingerprinting (MTC-MRF) technique to quantify tissue parameters, which further allows an estimation of accurate MTC signals at a certain CEST frequency offset. A pseudorandomized RF saturation scheme was used to generate unique MTC signal evolutions for different tissues and a supervised deep neural network was designed to extract tissue properties from measured MTC-MRF signals. Through detailed Bloch equation-based digital phantom and in vivo studies, we demonstrated that the MTC-MRF can quantify MTC characteristics with high accuracy and computational efficiency, compared to a conventional Bloch equation fitting approach, and provide baseline reference signals for CEST and NOE imaging. For validation, MTC-MRF images were synthesized using the tissue parameters estimated from the deep-learning method and compared with experimentally acquired MTC-MRF images as the reference standard. The proposed MTC-MRF framework can provide quantitative 3D MTC, CEST, and NOE imaging of the human brain within a clinically acceptable scan time.
KW - APT
KW - CEST
KW - Deep learning
KW - MR fingerprinting (MRF)
KW - MTC
UR - http://www.scopus.com/inward/record.url?scp=85088033944&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088033944&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2020.117165
DO - 10.1016/j.neuroimage.2020.117165
M3 - Article
C2 - 32679254
AN - SCOPUS:85088033944
SN - 1053-8119
VL - 221
JO - NeuroImage
JF - NeuroImage
M1 - 117165
ER -