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
The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)
Santiago Cepeda, Sergio Garcia-Garcia, Ignacio Arrese, Francisco Herrero, Trinidad Escudero, Tomas Zamora, Rosario Sarabia
Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI
Sheng Chen, Zihao Tang, Dongnan Liu, Ché Fornusek, Michael Barnett, Chenyu Wang, Mariano Cabezas, Weidong Cai
Machine learning-based spin structure detection
Isaac Labrie-Boulay, Thomas Brian Winkler, Daniel Franzen, Alena Romanova, Hans Fangohr, Mathias Kläui
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging
Qi Wang, Lucas Mahler, Julius Steiglechner, Florian Birk, Klaus Scheffler, Gabriele Lohmann