Shortening MRI scanning time for acute ischemic stroke: analysis of the effect of 3.0T MRI compressed sensing deep learning reconstruction

Background Acute ischemic stroke requires rapid and accurate MRI diagnosis. This study aimed to evaluate whether 3.0T brain MRI with compressed sensing deep learning reconstruction (CS‑DLR) can reduce scanning time while maintaining diagnostic image quality. Methods We retrospectively enrolled 69 patients with acute ischemic stroke who underwent 3.0T MRI. Conventional images and CS‑DLR reconstructed images at three acceleration rates (R3, R4, R5) were compared for AX T2WI, AX T1WI, and AX T2W FLAIR sequences. Two radiologists performed qualitative evaluation (5‑point scale: overall quality, noise, clarity). Quantitative assessment included signal‑to‑noise ratio (SNR) and contrast‑to‑noise ratio (CNR). Statistical analysis was performed using generalized estimating equation (GEE). Results CS‑DLR significantly reduced scanning time. Quantitative analysis showed that SNR and CNR were significantly higher in the CS‑DLR groups than in the conventional group ( P < 0.05). Qualitative scores for overall quality, noise, and clarity were significantly better in CS‑DLR R3 and R4 than in the conventional group ( P < 0.05). CS‑DLR R5 showed similar overall image quality ( P > 0.05) but significantly lower clarity ( P < 0.05). Conclusion CS‑DLR markedly shortens 3.0T MRI scanning time for acute ischemic stroke. CS‑DLR R3 and R4 provide superior image quality compared with conventional MRI, whereas R5 achieves comparable overall quality. CS‑DLR is clinically feasible and valuable for rapid emergency neuroimaging.