A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging
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 con...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-11-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920306510 |
| _version_ | 1818352666494894080 |
|---|---|
| author | Byungjai Kim Michael Schär HyunWook Park Hye-Young Heo |
| author_facet | Byungjai Kim Michael Schär HyunWook Park Hye-Young Heo |
| author_sort | Byungjai Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T18:57:16Z |
| format | Article |
| id | doaj.art-3b0c671b9382482999601b1dec0f035f |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-13T18:57:16Z |
| publishDate | 2020-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-3b0c671b9382482999601b1dec0f035f2022-12-21T23:34:46ZengElsevierNeuroImage1095-95722020-11-01221117165A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imagingByungjai Kim0Michael Schär1HyunWook Park2Hye-Young Heo3Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaDivision of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USADepartment of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea; Corresponding author. Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of KoreaDivision of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Corresponding author. Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 334, Baltimore, MD, 21287, USA.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.http://www.sciencedirect.com/science/article/pii/S1053811920306510APTCESTDeep learningMR fingerprinting (MRF)MTC |
| spellingShingle | Byungjai Kim Michael Schär HyunWook Park Hye-Young Heo A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging NeuroImage APT CEST Deep learning MR fingerprinting (MRF) MTC |
| title | A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging |
| title_full | A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging |
| title_fullStr | A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging |
| title_full_unstemmed | A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging |
| title_short | A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging |
| title_sort | deep learning approach for magnetization transfer contrast mr fingerprinting and chemical exchange saturation transfer imaging |
| topic | APT CEST Deep learning MR fingerprinting (MRF) MTC |
| url | http://www.sciencedirect.com/science/article/pii/S1053811920306510 |
| work_keys_str_mv | AT byungjaikim adeeplearningapproachformagnetizationtransfercontrastmrfingerprintingandchemicalexchangesaturationtransferimaging AT michaelschar adeeplearningapproachformagnetizationtransfercontrastmrfingerprintingandchemicalexchangesaturationtransferimaging AT hyunwookpark adeeplearningapproachformagnetizationtransfercontrastmrfingerprintingandchemicalexchangesaturationtransferimaging AT hyeyoungheo adeeplearningapproachformagnetizationtransfercontrastmrfingerprintingandchemicalexchangesaturationtransferimaging AT byungjaikim deeplearningapproachformagnetizationtransfercontrastmrfingerprintingandchemicalexchangesaturationtransferimaging AT michaelschar deeplearningapproachformagnetizationtransfercontrastmrfingerprintingandchemicalexchangesaturationtransferimaging AT hyunwookpark deeplearningapproachformagnetizationtransfercontrastmrfingerprintingandchemicalexchangesaturationtransferimaging AT hyeyoungheo deeplearningapproachformagnetizationtransfercontrastmrfingerprintingandchemicalexchangesaturationtransferimaging |