A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging

Abstract

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.