Deep Learning-Based Image Reconstruction Techniques for Improved Medical Imaging Quality

Abstract

This paper presents a novel approach to improving medical imaging quality through deep learning-based image reconstruction techniques. The proposed methods leverage the power of deep neural networks to enhance the resolution, clarity, and overall quality of medical images, thereby aiding in more accurate diagnosis and treatment planning. We explore various deep learning architectures and training strategies tailored to medical imaging, highlighting their effectiveness in improving image reconstruction compared to traditional methods. Additionally, we discuss the challenges and future directions of deep learning in medical imaging, emphasizing the potential impact on healthcare practices.