In this article, I will discuss different approaches to CT image denoising with CNN and some traditional approaches as well.
Denoising CT images with Convolutional Neural Networks (CNNs) represents a significant advancement in medical imaging technology. CT (Computed Tomography) scans are invaluable for diagnosing and monitoring various medical conditions, but they often suffer from noise due to low-dose radiation used to minimize patient exposure. This noise can obscure important details and affect diagnostic accuracy. CNNs, a class of deep-learning neural networks, have proven exceptionally effective in addressing this issue. These networks are trained on large datasets of noisy and clean images, learning to identify and eliminate noise while preserving critical anatomical details. To get more ideas on how to do the denoising in CT images for image quality improvement you can read this paper, which contains lots of information and hands-on example implementation with dataset.
The process involves passing the noisy CT images through multiple layers of the CNN, each designed to extract features and reduce noise incrementally. As a result, the output images are clearer, allowing for more precise diagnoses. Moreover, CNN-based denoising operates faster than traditional methods, enabling real-time processing in clinical settings. This technology not only enhances the quality of medical imaging but also has the potential to significantly improve patient outcomes by aiding in early and accurate disease detection.
In the suggested paper you can find all types of necessary datasets and lots of reference works for medical image denoising tasks.
