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Updated: Aug 20, 2025
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Feasibility of deep learning k-space-to-image reconstruction for diffusion weighted imaging in patients with breast
Eun Ji Lee1, Yun-Woo Chang1, Jae Kon Sung2
1Department of Radiology, Soonchunhyang University Seoul Hospital, 59 Daesakwan-ro, Yongsan-ku, Seoul 04401, Korea.
Purpose:
This study aimed to evaluate the feasibility of accelerated DLR (deep learning reconstruction) single-shot echo planar imaging (ss-EPI) for diffusion-weighted image (DWI) in patients with breast cancers in comparison to conventional ss-EPI.
Methods:
Between August 2021 and February 2022, eighty-seven patients with pathologically proven breast cancer underwent DCE breast MRI including ss-EPI and DLR ss-EPI DWI sequences (TA, 3:36 min and 1:54 min, respectively) at 3 Tesla. In a randomized and blinded manner, two radiologists independently performed qualitative analyses for overall image quality using a 5-point scale of the following components: homogeneous fat suppression, image blurring, artifact, and lesion conspicuity. Quantitative analyses were performed by measurement of ADC values, SNR, CNR, and lesion contrast.
Results:
DLR ss-EPI showed better image quality scores, CNR, and lesion contrast than ss-EPI (all P < 0.05) while reducing scan time by 47.2 %. DLR ss-EPI showed no significant difference in SNR and tumor ADC values compared to -ss-EPI (P = 0.307 and P = 0.123, respectively).
Conclusions:
DLR ss-EPI showed better results in the qualitative and quantitative analysis than conventional ss-EPI despite reducing scan time by 47.2%.
