Abstract #1396

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

Yueyan Bian¹, Long Wang², Jin Li¹, Xiaoxu Yang¹, Erling Wang¹, Yingying Li¹, Chen Zhang³, Lei Xiang⁴, and Qi Yang^1,5

¹Department of Radiology, Beijing Chaoyang Hospital, Beijing, China, ²Subtle Medical, Shanghai, China, ³MR Research Collaboration, Siemens Healthineers, Beijing, China, ⁴Department of Radiology, Beijing Chaoyang Hospital, Shanghai, China, ⁵Laboratory for Clinical Medicine, Capital Medical University, Beijing, China

Synopsis

Keywords: AI/ML Image Reconstruction, Ischemia

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.

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