Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images.

PURPOSE: The purpose of this study is to evaluate the influence of super-resolution deep learning-based reconstruction (SR-DLR), which utilizes k-space data, on the quality of images and the quantitation of the apparent diffusion coefficient (ADC) for diffusion-weighted images (DWI) in brain magnetic resonance imaging (MRI). METHODS: A retrospective analysis was performed on 34 patients who had undergone DWI using a 3 T MRI system with SR-DLR reconstruction based on k-space data in August 2022. DWI was reconstructed with SR-DLR (Matrix = 684 × 684) and without SR-DLR (Matrix = 228 × 228). Measurements were made of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) in white matter (WM) and grey matter (GM), and the full width at half maximum (FWHM) of the septum pellucidum. Two radiologists assessed image noise, contrast, artifacts, blur, and the overall quality of three image types using a four-point scale. Quantitative and qualitative scores between images with and without SR-DLR were compared using the Wilcoxon signed-rank test. RESULTS: Images with SR-DLR showed significantly higher SNRs and CNRs than those without SR-DLR (p < 0.001). No statistically significant variances were found in the apparent diffusion coefficients (ADCs) in WM and GM between images with and without SR-DLR (ADC in WM, p = 0.945; ADC in GM, p = 0.235). Moreover, the FWHM without SR-DLR was notably lower compared to that with SR-DLR (p < 0.001). CONCLUSION: SR-DLR has the potential to augment the quality of DWI in DL MRI scans without significantly impacting ADC quantitation.

Feasibility of high-resolution magnetic resonance imaging of the liver using deep learning reconstruction based on the deep learning denoising technique.

PURPOSE: To evaluate the feasibility of High-resolution (HR) magnetic resonance imaging (MRI) of the liver using deep learning reconstruction (DLR) based on a deep learning denoising technique compared with standard-resolution (SR) imaging. MATERIALS AND METHODS: This retrospective study included patients who underwent abdominal MRI including both HR imaging using DLR and SR imaging between April 1 and August 31, 2019. DLR was applied to all HR images using 12 different strength levels of noise reduction to determine the optimal denoised level for HR images. The mean signal-to-noise ratio (SNR) was then compared between the original HR images without DLR and the optimal denoised HR images with DLR and SR images. The mean image noise, sharpness and overall image quality were also compared. Statistical analyses were performed with the Friedman and Dunn-Bonferroni post-hoc test. RESULTS: In total, 49 patients were analyzed (median age, 71 years; 25 women). In quantitative analysis, the mean SNRs on the original HR images without DLR were significantly lower than those on the SR images in all sequences (p < 0.01). Conversely, the mean SNRs on optimal denoised HR images were significantly higher than those on the SR images in all sequences (p < 0.01). In the qualitative analysis, the mean scores for the image noise and overall image quality were significantly higher on optimal denoised HR images than on the SR images in all sequences (p < 0.01) except for the mean image noise score in in-phase (IP) images. CONCLUSIONS: The use of a deep learning-based noise reduction technique substantially and successfully improved the SNR and image quality in HR imaging of the liver. Denoised HR imaging using the DLR technique appears feasible for use in liver MR examinations compared with SR imaging.

Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers.

PURPOSE: To test whether our proposed denoising approach with deep learning-based reconstruction (dDLR) can effectively denoise brain MR images. METHODS: In an initial experimental study, we obtained brain images from five volunteers and added different artificial noise levels. Denoising was applied to the modified images using a denoising convolutional neural network (DnCNN), a shrinkage convolutional neural network (SCNN), and dDLR. Using these brain MR images, we compared the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the three denoising methods. Two neuroradiologists assessed the image quality of the three types of images. In the clinical study, we evaluated the denoising effect of dDLR in brain images with different levels of actual noise such as thermal noise. Specifically, we obtained 2D-T2-weighted image, 2D-fluid-attenuated inversion recovery (FLAIR) and 3D-magnetization-prepared rapid acquisition with gradient echo (MPRAGE) from 15 healthy volunteers at two different settings for the number of image acquisitions (NAQ): NAQ2 and NAQ5. We reconstructed dDLR-processed NAQ2 from NAQ2, then compared with SSIM and PSNR. Two neuroradiologists separately assessed the image quality of NAQ5, NAQ2 and dDLR-NAQ2. Statistical analysis was performed in the experimental and clinical study. In the clinical study, the inter-observer agreement was also assessed. RESULTS: In the experimental study, PSNR and SSIM for dDLR were statistically higher than those of DnCNN and SCNN (P < 0.001). The image quality of dDLR was also superior to DnCNN and SCNN. In the clinical study, dDLR-NAQ2 was significantly better than NAQ2 images for SSIM and PSNR in all three sequences (P < 0.05), except for PSNR in FLAIR. For all qualitative items, dDLR-NAQ2 had equivalent or better image quality than NAQ5, and superior quality to that of NAQ2 (P < 0.05), for all criteria except artifact. The inter-observer agreement ranged from substantial to near perfect. CONCLUSION: dDLR reduces image noise while preserving image quality on brain MR images.