Low-Dose Medical Image Reconstruction Using Residual U-Net:A Hybrid Approach Integrating Compressed Sensing and Deep Learning

Low-dose computed tomography (LDCT) and otherlow-dose medical imaging modalities are critically important for reducing patient radiation exposure andminimizing cancer risks. However, image reconstruction from severely undersampled or noisy measurements presents a significant ill-posed inverse problem, leading to degraded image quality characterizedby quantum noise, streaking artifacts, and loss ofstructural detail. This paper proposes a novel hybridframework that synergistically combines the theoretical foundations of Compressed Sensing (CS) with therepresentational power of deep learning, specificallythrough a Residual U-Net architecture. The proposed model operates within an iterative reconstruction paradigm, where a CS-based data fidelity step isalternated with a deep learning-based prior step, implemented by the Residual U-Net, to progressivelyrefine the image. The Residual U-Net, enhancedwith squeeze-and-excitation blocks, is designed tolearn the mapping from artifact-corrupted, low-doseimages to their high-dose counterparts, effectivelymodeling the complex noise and artifact distributions while preserving anatomical structures. We formulate the reconstruction as an optimization problem, solved via the Learned Primal-Dual algorithm.Extensive quantitative and qualitative evaluationsare performed on the publicly available Mayo ClinicLDCT dataset and a simulated undersampled MRIdataset (k-space data). Results demonstrate thatour proposed Residual U-Net integrated iterative reconstruction (RN-IR) method significantly outperforms traditional methods like Filtered Back Projection (FBP), total variation (TV)-based CS, andstandalone deep learning models (such as a vanilla UNet) in terms of Peak Signal-to-Noise Ratio (PSNR),Structural Similarity Index (SSIM), and visual quality. Our model achieves a PSNR of 42.3 dB and SSIMof 0.985 on the LDCT dataset, effectively suppressing noise and preserving fine details, thus providing arobust and efficient solution for high-quality low-dose medical image reconstruction.