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

Objectives: To develop a real-time abdominal T mapping method without requiring breath-holding or respiratory-gating.

Methods: The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T 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.

Results: The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T mapping. The average NMSE and R values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T and reference T in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T 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 T 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.

Conclusions: Free-breathing abdominal T mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T mapping in the abdomen.

Key Points: • MOLED achieves free-breathing abdominal T mapping in about 100 ms, enabling real-time capture of T value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.