Evaluation of deep learning-based super-resolution reconstruction in fetal brain T2-weighted single-shot fast spin-echo imaging

The study aimed to evaluate the efficacy of a deep learning-based Precise IQ Engine (PIQE) in enhancing image sharpness and quality during MRI reconstruction to enhance the image quality of fetal brain single-shot fast spin-echo (SSFSE) imaging.

Fetal brain MRI scans of 14 patients, including an SSFSE sequence, acquired between January 2024 and February 2025, were analyzed. Images were reconstructed with and without PIQE (zoom ratio of 3 from low-resolution matrices). We quantified the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and image sharpness using the full width at half maximum (FWHM) of the fetal brain. Two board-certified radiologists assessed the image noise, contrast, sharpness, artifacts, and overall image quality of the two image types on a 4-point scale. We compared the quantitative and qualitative scores between the images with and without PIQE using the Wilcoxon signed-rank test.

SNRs, CNRs, and FWHM were significantly enhanced with PIQE compared to those without PIQE (SNR, CNR: p < 0.001, FWHM : p < 0.01). Qualitative scores for noise (p < 0.001), contrast (p < 0.001), sharpness (p < 0.001), artifacts (p < 0.001), and overall image quality (p < 0.001) were superior in images obtained with PIQE compared to those without it. Kappa analysis revealed moderate-to- excellent agreement for different image quality aspects: noise (κ = 0.52), contrast (κ = 0.74), sharpness (κ = 0.82), artifacts (κ = 0.61), and overall image quality (κ = 0.80).

PIQE potentially enhances the image quality of fetal brain MRI using an SSFSE sequence.