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
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
Kimberly Helm, Tejas Sudharshan Mathai, Boah Kim, Pritam Mukherjee, Jianfei Liu, Ronald M. Summers
Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning
Douwe J. Spaanderman, Martijn P. A. Starmans, Gonnie C. M. van Erp, David F. Hanff, Judith H. Sluijter, Anne-Rose W. Schut, Geert J. L. H. van Leenders, Cornelis Verhoef, Dirk J. Grunhagen, Wiro J. Niessen, Jacob J. Visser, Stefan Klein
Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumors
Shahinur Alam, Jinsoo Uh, Alexander Dresner, Chia-ho Hua, Khaled Khairy
Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI
Annesha Ghosh, Gordon Wetzstein, Mert Pilanci, Sara Fridovich-Keil