CDNet: Complex-Valued Deep Unrolled Composite Denoising Network for Accelerated Magnetic Resonance Image Reconstruction

Abstract

Magnetic resonance imaging (MRI) is an essential modality in contemporary medical diagnosis, offering high-resolution images with excellent soft-tissue contrast of internal organs in a non-invasive, non-ionizing, and non-carcinogenic manner. However, the primary drawback of this technique is a lengthy data-acquisition process, which raises the possibility of picking up artefacts as well as discomfort in patients. To accelerate it, data is acquired at sub-Nyquist rates, and the image is recovered from undersampled, noisy measurements using the compressed sensing (CS) technique. Composite priors in CS–MRI have demonstrated superior performance compared to single priors, despite having limited image priors, which also leads to slower reconstruction speed. To date, deep unfolded networks (DUNs), which integrate the powerful data-driven prior of traditional deep learning (DL) with the strong interpretability of optimization algorithms, are the most outstanding reconstruction technology in MRI acceleration, yielding superior and interpretable results. This study proposes the iterative three-operator splitting (TOS) method to solve the composite regularization problem and a composite denoising network (CDNet) that maintains consistent arithmetic structures with it. The CDNet is implemented using complex-valued DL strategies for richer representation of MRI. Extensive experiments using brain and knee raw k-space from the FastMRI dataset with acceleration factors (AF) ranging from ×2 to ×10 and assessed using peak signal–to–noise ratio (PSNR), structural similarity index (SSIM), and normalized root–mean–squared error (NRMSE) validate the CDNet. Across AFs, CDNet achieved dataset-averaged PSNR/SSIM/NRMSE of 26.50–33.47dB/0.7787–0.8992/0.2562–0.1303 for brain MRI and 28.08–35.52dB/0.7069–0.8734/0.1929–0.0950 for knee MRI. The proposed network yields superior reconstruction accuracy and faster reconstruction speed compared to the iterative TOS for composite regularization, demonstrating the effectiveness of deep unrolling in accelerating iterative optimization. Furthermore, CDNet outperforms other state-of-the-art DUNs in reconstruction accuracy at comparable inference speed, demonstrating its vast potential for improving patient care, scanner throughput, and healthcare economics.