Abstract #4474

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

Zhekai Chen¹, Tao Gong², Jianfeng Bao³, Liangjie Lin⁴, and Lin Chen¹

¹Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China, ²Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China, ³Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China, ⁴Clinical and Technical Support, Philips Healthcare, Beijing, China

Synopsis

Keywords: CEST / APT / NOE, CEST & MT

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.

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