Complex Deep Neural Networks for Denoising Ultra-Fast Submillimeter T2*-weighted Imaging and Quantitative...

The 2024 revisions of the McDonald diagnostic criteria for multiple sclerosis (MS) have incorporated susceptibility-based magnetic resonance imaging (MRI) biomarkers to improve diagnostic sensitivity and specificity. However, the addition of imaging sequences used to visualize these biomarkers (Time of Acquisition, TA: ~6 minutes) increases the overall patient scan time. This study addresses this by combining parallel imaging (TA: ~2 minutes) with our proposed deep learning-based image denoising method, complex-valued denoising convolutional neural network (ℂDnCNN), to generate high quality data. Network layers used in the denoising convolutional neural network (DnCNN) were extended to the complex domain for learning complex-valued MR image features in the image domain. Four real-valued and complex-valued versions of this network (2D DnCNN, 2D ℂDnCNN, 3D DnCNN, and 3D ℂDnCNN) were developed for testing on a simulated noise testing set and a real-world noise testing set. In the simulated noise testing set, 3D ℂ DnCNN outperformed the other approaches for denoising magnitude and complex MRI data across all levels of simulated noise. In the real-world noise testing set, 2D DnCNN yielded the highest increases for NRMSE, PSNR and CNR measures, while 3D DnCNN yielded the highest improvement for SSIM when denoising T2*-weighted magnitude images at the highest acceleration factor. For the complex-valued data, 3D ℂDnCNN outperformed 2D ℂDnCNN to produce high quality magnitude and phase data at all acceleration factors in all image quality metrics except for CNR measures. Overall, our study demonstrates the capability of deep learning-based image denoising methods to efficiently denoise ultra-fast submillimeter isotropic data. Our proposed ℂDnCNN is able to denoise complex-valued MRI data which further enables the generation of high-quality quantitative susceptibility mapping (QSM). Besides that, our experimental results using different denoising models indicate that CNNs should be designed to learn 3-dimensional image features in the complex domain to achieve optimal performance on MRI data.