Deep Learning-Enhanced T1-Weighted Imaging for Breast MRI at 1.5T - Susann-Cathrin Olthof; Marcel Dominik Nickel; Elisabeth Weiland; Daniel Leyhr; Saif Afat; Konstantin Nikolaou; Heike Preibsch (Diagnostics)
Background/Objectives: Assessment of a novel deep-learning (DL)-based T1w volumetric interpolated breath-hold (VIBEDL) sequence in breast MRI in comparison with standard VIBE (VIBEStd) for image quality evaluation. Methods: Prospective study of 52 breast cancer patients examined at 1.5T breast MRI with T1w VIBEStd and T1 VIBEDL sequence. T1w VIBEDL was integrated as an additional early non-contrast and a delayed post-contrast scan. Two radiologists independently scored T1w VIBE Std/DL sequences both pre- and post-contrast and their calculated subtractions (SUBs) for image quality, sharpness, (motion)–artifacts, perceived signal-to-noise and diagnostic confidence with a Likert-scale from 1: Non-diagnostic to 5: Excellent. Lesion diameter was evaluated on the SUB for T1w VIBEStd/DL. All lesions were visually evaluated in T1w VIBEStd/DL pre- and post-contrast and their subtractions. Statistics included correlation analyses and paired t-tests. Results: Significantly higher Likert scale values were detected in the pre-contrast T1w VIBEDL compared to the T1w VIBEStd for image quality (each p < 0.001), image sharpness (p < 0.001), SNR (p < 0.001), and diagnostic confidence (p < 0.010). Significantly higher values for image quality (p < 0.001 in each case), image sharpness (p < 0.001), SNR (p < 0.001), and artifacts (p < 0.001) were detected in the post-contrast T1w VIBEDL and in the SUB. SUBDL provided superior diagnostic certainty compared to SUBStd in one reader (p = 0.083 or p = 0.004). Conclusions: Deep learning-enhanced T1w VIBEDL at 1.5T breast MRI offers superior image quality compared to T1w VIBEStd.