Advancements in Medical Imaging: Deep Learning to Hybrid Systems
*Corresponding Author: Dr. Martin Kova脛聧, Department of Imaging Physics, Comenius University, Slovakia, Email: m.kovac@imgphys.skReceived Date: Aug 05, 2025 / Published Date: Aug 29, 2025
Citation: Kova膷 DM (2025) Advancements in Medical Imaging: Deep Learning to Hybrid Systems. J Radiol 14: 721.DOI: 10.4172/2167-7964.1000721
Copyright: © 2025 Dr. Martin Kova膷 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Abstract
This compilation explores cutting-edge developments in medical imaging, including AI-driven MRI reconstruction and mammography lesion detection, novel X-ray detector technologies, and improved quantitative MRI. It also covers multimodal image fusion, CT dose reduction, advanced ultrasound beamforming, radiomics for treatment response prediction, prompt gamma neutron activation analysis, and hybrid PET/CT imaging. These advancements aim to enhance diagnostic accuracy, improve patient outcomes, and optimize clinical workflows
