Free-breathing and instantaneous abdominal T₂ mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction.

Abstract

To develop a real-time abdominal T₂ mapping method without requiring breath-holding or respiratory-gating. The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T₂ mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T₂ mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set. The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T₂ mapping. The average NMSE and R² values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T₂ and reference T₂ in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T₂ value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T₂ values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively. Free-breathing abdominal T₂ mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T₂ mapping in the abdomen. • MOLED achieves free-breathing abdominal T₂ mapping in about 100 ms, enabling real-time capture of T₂ value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.