Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework

Abstract

Low-dose computed tomography (LDCT) imaging is a critical tool in medical diagnostics due to its reduced radiation exposure. However, this reduction often results in increased noise levels, compromising image quality and diagnostic accuracy. Despite advancements in denoising techniques, a robust method that effectively balances noise reduction and detail preservation remains a significant need. Current denoising algorithms frequently fail to maintain the necessary balance between suppressing noise and preserving crucial diagnostic details. Addressing this gap, our study focuses on developing a deep learning-based denoising algorithm that enhances LDCT image quality without losing essential diagnostic information. Here we present a novel supervised learning-based LDCT denoising algorithm that employs innovative noise extraction and denoising techniques. Our method significantly enhances LDCT image quality by incorporating multiple attention mechanisms within a U-Net-like architecture. Our approach includes a noise extraction network designed to capture diverse noise patterns precisely. This network is integrated into a comprehensive denoising system consisting of a generator network, a discriminator network, and a feature extraction AutoEncoder network. The generator network removes noise and produces high-quality CT images, while the discriminator network differentiates real images from denoised ones, improving the realism of the outputs. The AutoEncoder network ensures the preservation of image details and diagnostic integrity. Our method improves the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 7.777 and 0.128 compared to LDCT, by 0.483 and 0.064 compared to residual encoder–decoder convolutional neural network (RED-CNN), by 4.101 and 0.017 compared to Wasserstein generative adversarial network–visual geometry group (WGAN-VGG), and by 3.895 and 0.011 compared to Wasserstein generative adversarial network–autoencoder (WGAN-AE). This demonstrates that our method has a significant advantage in enhancing the signal-to-noise ratio of images. Extensive experiments on multiple standard datasets demonstrate our method’s superior performance in noise suppression and image quality enhancement compared to existing techniques. Our findings significantly impact medical imaging, particularly improving LDCT scan diagnostic accuracy. The enhanced image clarity and detail preservation offered by our method open new avenues for clinical applications and research. This improvement in LDCT image quality promises substantial contributions to clinical diagnostics, disease detection, and treatment planning, ensuring high-quality diagnostic outcomes while minimizing patient radiation exposure.