One-Shot Learning for CEST-Centered Multiparametric MRI: Training Neural Network with One Single Scan

Motivation: Multiparametric imaging offers comprehensive information. However, its practical application is hindered by extended scanning times. Goal(s): To develop a CEST-centered multiparametric approach capable of producing multiple quantitative maps. Approach: ResNet was utilized to simultaneously quantify amide, NOE, MT, DS, B0, T1 and T2. By incorporating a reweighting scheme in conjunction with transfer learning, we demonstrate one single scan is adequate to train a well-performing neural network. The robustness and generalizability of the proposed method were validated using multicenter data. Results: The proposed method outperformed state-of-the-art CEST deep learning method, providing more accurate quantification results, all while requiring a limited amount of training data. Impact: The proposed method has the potential to establish a CEST-centered multiparametric approach, eliminating the need for multiple scanning protocols and, consequently, reducing scan time.