Deep learning reconstruction for accelerating synthetic magnetic resonance imaging of the breast: a comparative analysis of accelerated and standard protocols.

Background: Synthetic magnetic resonance imaging (SyMRI) offers synchronized acquisition of multiparametric maps and contrast-weighted images, showing promise in breast cancer diagnosis. However, its relatively long scan time limits broader clinical application. The aim of this study was to evaluate the feasibility of deep learning reconstruction (DLR) in accelerating SyMRI and to compare its image quality, quantitative accuracy, and diagnostic performance in breast imaging with those of standard SyMRI. Methods: Fifty-eight female patients (mean age 50.38±1.34 years) with suspected breast cancer underwent three SyMRI protocols: standard 2× SyMRI, accelerated 3× SyMRI, and 3× SyMRI with DLR (3× SyMRI-DLR). Quantitative parameters [T1, T2, and proton density (PD)], signal-to-noise ratio (SNR), and subjective image quality (e.g., overall image quality, anatomical clarity, diagnostic information, tissue contrast, uniformity, and artifacts) were evaluated. Receiver operating characteristic (ROC) analysis and the area under the curve (AUC) were used to evaluate the diagnostic performance in identifying breast lesions and distinguishing triple-negative breast cancer (TNBC) from non-TNBC. Results: The 3× SyMRI and 3× SyMRI-DLR protocols reduced the scan time compared to 2× SyMRI. No significant differences were observed in T1, T2, or PD values across the three protocols (all P values >0.05), and there were strong correlations between 3× SyMRI-DLR and 2× SyMRI (r Conclusions: Integrating DLR into accelerated SyMRI for breast imaging yielded a 37.46% reduction in scan time compared to that of the standard SyMRI while maintaining quantitative accuracy and image quality.