Super-Resolution Deep Learning Reconstruction for 3-Dimensional T1-Weighted Gradient-Echo Imaging in Contrast-Enhanced Liver MRI: Comparison With Conventional and Standard Deep Learning Reconstructions
Kentaro Nishiuchi, Keitaro Sofue, Kazuma Tsukamoto, Yuichiro Somiya, Tomonari Ishida, Ryutaro Yano, Akihiko Umeno, Ryohei Kozuki, Takeru Yamaguchi, Eisuke Ueshima, Yoshiko Ueno, Takahiro Tsuboyama, Atsuhiro Masuda, Izumi Imaoka, Takamichi MurakamiPurpose:
To evaluate the image quality of super-resolution deep learning reconstruction (SR-DLR) for 3-dimensional (3D) T1-weighted gradient-echo (GRE) imaging in contrast-enhanced MRI, compared with conventional reconstruction (Conv.) and standard deep learning reconstruction (DLR).
Materials and Methods:
This retrospective study included 50 patients (mean age: 71.5 y) with 76 hepatic lesions who underwent contrast-enhanced dynamic liver MRI at 3T. Portal venous phase images were reconstructed using Conv., DLR, and SR-DLR. Quantitative analyses measured liver signal-to-noise ratio (SNR), contrast ratio (CR) between liver parenchyma and lesions, edge rise distance (ERD), and edge rise slope (ERS). Qualitative assessments of image noise, sharpness, contrast, motion artifacts, overall image quality, and lesion conspicuity were performed independently by 2 radiologists using a 5-point scale. Coronal multiplanar reformatted (MPR) images were also evaluated. Statistical comparisons were performed using the Friedman test with Bonferroni correction.
Results:
Liver SNR was comparable between SR-DLR and DLR. SR-DLR produced sharper edges (lower ERD, higher ERS) and higher lesion-to-liver CR than Conv. and DLR (
Conclusion:
SR-DLR improved spatial resolution, sharpness, and lesion conspicuity in contrast-enhanced MRI compared with Conv. and DLR, indicating potential to improve the diagnostic performance of dynamic liver MRI.