Magnetic Resonance Imaging
Magnetic Resonance Imaging (MRI) is a crucial medical imaging technique aiming to produce high-resolution images of the body's internal structures for diagnostic purposes. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), vision transformers (ViTs), generative adversarial networks (GANs), and diffusion models to improve image quality, accelerate acquisition times, automate analysis (e.g., lesion segmentation, disease classification), and enable multi-modal data integration. These advancements are significantly impacting healthcare by improving diagnostic accuracy, enabling personalized treatment planning, and potentially reducing the need for invasive procedures.
Papers
Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT
Jue Jiang, Jun Hong, Kathryn Tringale, Marsha Reyngold, Christopher Crane, Neelam Tyagi, Harini Veeraraghavan
A deep learning approach for brain tumor detection using magnetic resonance imaging
Al-Akhir Nayan, Ahamad Nokib Mozumder, Md. Rakibul Haque, Fahim Hossain Sifat, Khan Raqib Mahmud, Abul Kalam Al Azad, Muhammad Golam Kibria