Image quality and diagnostic performance of deep learning reconstruction for diffusion- weighted imaging in 3 T breast MRI.

PURPOSE

This study aimed to assess the image quality and the diagnostic value of deep learning reconstruction (DLR) for diffusion-weighted imaging (DWI) compared with conventional single-shot echo-planar imaging (ss-EPI) in 3 T breast MRI.

METHODS

Between January and July 2023, this single-center prospective study involved patients who underwent both clinical breast MRI and additional DWIs including accelerated (fast DLR) and high-resolution (HR DLR) for the research purpose. Two radiologists independently evaluated image quality, including fat suppression homogeneity, image blurring, artifacts, and lesion conspicuity. The optimal cutoff value of the ADC value was determined based on a separate dataset comprising 98 breast lesions in 81 patients from a previous retrospective study. ADC values from 62 breast lesions (55 malignant, 7 benign) in 50 patients were analyzed to compare diagnostic performance across three DWI datasets.

RESULTS

The study cohort included 50 patients (median age, 55.3 years). Fast DLR and HR DLR showed significantly better image quality compared to ss-EPI (P < 0.05), with no significant difference between two DLR methods (P > 0.05). DLR protocols consistently outperform ss-EPI for reducing artifacts across all lesion types and lesion size (P < 0.05). Mean ADC values measured in the phantom and clinical images were not significantly different across DWI protocols (P > 0.05). No significant difference in the diagnostic performance with the AUC of 0.846 in ss-EPI, 0.828 in fast DLR and 0.855 in HR DLR (P > 0.05). Fast DLR showed a significantly lower standard deviation of ADC values compared to ss-EPI in malignant, mass-type lesions and those smaller than 2 cm (P < 0.05).

CONCLUSIONS

DLR DWI in 3T breast MRI improves image quality in both accelerated and high-resolution acquisition settings without compromising diagnostic performance. The use of DLR in DWI of breast MRI could enhance the efficiency and versatility of imaging protocols, offering significant clinical value.