Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study

To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC). This single-center prospective study enrolled consecutive at-risk participants who underwent 3.0 T gadoxetate disodium-enhanced MRI. Conventional DWI was acquired using parallel imaging (PI) with SENSE (PI-DWI). In CS-DWI and DL-CS-DWI, CS but not PI with SENSE was used to accelerate the scan with 2.5 as the acceleration factor. Qualitative and quantitative image quality were independently assessed by two masked reviewers, and were compared using the Wilcoxon signed-rank test. The detection rates of clinically-relevant (LR-4/5/M based on the Liver Imaging Reporting and Data System v2018) liver lesions for each DWI sequence were independently evaluated by another two masked reviewers against their consensus assessments based on all available non-DWI sequences, and were compared by the McNemar test. 67 participants (median age, 58.0 years; 56 males) with 197 clinically-relevant liver lesions were enrolled. Among the three DWI sequences, DL-CS-DWI showed the best qualitative and quantitative image qualities (p range, <0.001–0.039). For clinically-relevant liver lesions, the detection rates (91.4%–93.4%) of DL-CS-DWI showed no difference with CS-DWI (87.3%–89.8%, p = 0.230–0.231) but were superior to PI-DWI (82.7%–85.8%, p = 0.015–0.025). For lesions located in the hepatic dome, DL-CS-DWI demonstrated the highest detection rates (94.8%–97.4% vs 76.9%–79.5% vs 64.1%–69.2%, p = 0.002–0.045) among the three DWI sequences. In patients at high-risk for HCC, DL-CS-DWI improved image quality and detection for clinically-relevant liver lesions, especially for the hepatic dome.