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Rationale and Objectives To assess the performance of an industry-developed deep learning (DL) algorithm to reconstruct low-resolution Cartesian T1-weighted dynamic contrast-enhanced (T1w) and T2-weighted turbo-spin-echo (T2w) sequences and compare them to standard sequences. Materials and Methods Female patients with indications for breast MRI were included in this prospective study. The study protocol at 1.5 Tesla MRI included T1w and T2w. Both sequences were acquired in standard resolution (T1 S and T2 S ) and in low-resolution with following DL reconstructions (T1 DL and T2 DL ). For DL reconstruction, two convolutional networks were used: (1) Adaptive-CS-Net for denoising with compressed sensing, and (2) Precise-Image-Net for resolution upscaling of previously downscaled images. Overall image quality was assessed using 5-point-Likert scale (from 1=non-diagnostic to 5=excellent). Apparent signal-to-noise (aSNR) and contrast-to-noise (aCNR) ratios were calculated. Breast Imaging Reporting and Data System (BI-RADS) agreement between different sequence types was assessed. Results A total of 47 patients were included (mean age, 58±11 years). Acquisition time for T1 DL and T2 DL were reduced by 51% (44 vs. 90 s per dynamic phase) and 46% (102 vs. 192 s), respectively. T1 DL and T2 DL showed higher overall image quality (e.g., 4 IQR, 4–4 for T1 S vs. 5 IQR, 5–5 for T1 DL , P<0.001). Both, T1 DL and T2 DL revealed higher aSNR and aCNR than T1 S and T2 S (e.g., aSNR: 32.35±10.23 for T2 S vs. 27.88±6.86 for T2 DL , P=0.014). Cohen k agreement by BI-RADS assessment was excellent (0.962, P<0.001). Conclusion DL for denoising and resolution upscaling reduces acquisition time and improves image quality for T1w and T2w breast MRI.
Mesropyan et al. (Thu,) studied this question.