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-T 2 -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.

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Given the growing awareness of and concern for potential carcinogenic effects of exposure of children to ionizing radiation at CT, optimizing acquisition parameters is crucial to achieve diagnostically acceptable image quality at the lowest possible radiation dose. Among currently available dose reduction techniques, recent technical innovations have allowed the implementation of low tube voltage scans and iterative reconstruction (IR) techniques into daily clinical practice for pediatric CT. The benefits of lowering tube voltage include a considerable reduction in radiation dose and improved contrast on images, especially when an iodinated contrast medium is used. The increase in noise, which is attributed to decreased photon penetration, is a major drawback but is not as severe as that at adult CT because of the small body size of children. In addition, use of IR algorithms can suppress increased noise, yielding wider applicability for low tube voltage scans. However, a careful implementation strategy and methodologic approach are necessary to maximize the potential for dose reduction while preserving diagnostic image quality under each clinical condition. The potential pitfalls of and topics related to these techniques include (a) the effect of tube voltage on the surface radiation dose, (b) the effect of window settings, (c) accentuation of metallic artifacts, (d) deterioration of low contrast detectability at low-dose settings, (e) interscanner variation of x-ray spectra, and (f) a comparison with the use of a spectral shaping technique. Appropriate use of low tube voltage and IR techniques is helpful for radiation dose reduction in most applications of pediatric CT. Online DICOM image stacks are available for this article . RSNA, 2018.

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