Detection and Quantification of Acute Ischemic Lesions using Deep Learning-Based Super-resolution Portable Low-Field-Strength MRI

Motivation: The diagnostic performance of portable low-field-strength MRI (LF-MRI) is constrained by low spatial-resolution and signal-to-noise ratio. Goal(s): To evaluate the performance in detecting and quantifying ischemic lesions among SynthMRI, LF-MRI and real high-field-strength MRI (HF-MRI). Approach: We created a deep learning-based model to generate the synthetic super-resolution (3T) MRI (SynthMRI) based on LF-MRI (0.23T). We evaluated the performance in detecting and quantifying ischemic lesions among SynthMRI, LF-MRI and HF-MRI. Results: SynthMRI demonstrated high sensitivity in detecting the number and locations of ischemic lesions. Moreover, SynthMRI exhibited strong correlations with HF-MRI in the quantitative assessment of ischemic lesions, and significantly higher than portable LF-MRI. Impact: Synthetic super-resolution MRI images overcome the limitations of low spatial resolution and signal-to-noise ratio in portable low-field-strength MRI. It has the potential to replace high-field-strength MRI images in the neuroimaging of AIS, enabling portable low-field-strength MRI examinations with comparable performance.