Assessment of AI-accelerated T2-weighted brain MRI, based on clinical ratings and image quality evaluation

To compare clinical ratings and signal-to-noise ratio (SNR) measures of a commercially available Deep Learning-based MRI reconstruction method (T2_(DR)) against conventional T2- turbo spin echo brain MRI (T2_(CN)).

100 consecutive patients with various neurological conditions underwent both T2_(DR) and T2_(CN) on a Siemens Vida 3 T scanner with a 64-channel head coil in the same examination. Acquisition times were 3.33 min for T2_(CN) and 1.04 min for T2_(DR). Four neuroradiologists evaluated overall image quality (OIQ), diagnostic safety (DS), and image artifacts (IA), blinded to the acquisition mode. SNR and SNR_eff (adjusted for acquisition time) were calculated for air, grey- and white matter, and cerebrospinal fluid.

The mean patient age was 43.6 years (SD 20.3), with 54 females. The distribution of non-diagnostic ratings did not differ significantly between T2_(CN) and T2_(DR) (IA p = 0.108; OIQ: p = 0.700 and DS: p = 0.652). However, when considering the full spectrum of ratings, significant differences favouring T2_(CN) emerged in OIQ (p = 0.003) and IA (p < 0.001). T2_(CN) had higher SNR (157.9, SD 123.4) than T2_(DR) (112.8, SD 82.7), p < 0.001, but T2_(DR) demonstrated superior SNR_eff (14.1, SD 10.3) compared to T2_(CN) (10.8, SD 8.5), p < 0.001.

Our results suggest that while T2_(DR) may be clinically applicable for a diagnostic setting, it does not fully match the quality of high-standard conventional T2_(CN), MRI acquisitions.