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
Exploiting XAI maps to improve MS lesion segmentation and detection in MRI
Federico Spagnolo, Nataliia Molchanova, Mario Ocampo Pineda, Lester Melie-Garcia, Meritxell Bach Cuadra, Cristina Granziera, Vincent Andrearczyk, Adrien Depeursinge
Optimizing Transmit Field Inhomogeneity of Parallel RF Transmit Design in 7T MRI using Deep Learning
Zhengyi Lu, Hao Liang, Xiao Wang, Xinqiang Yan, Yuankai Huo
HAMIL-QA: Hierarchical Approach to Multiple Instance Learning for Atrial LGE MRI Quality Assessment
K M Arefeen Sultan, Md Hasibul Husain Hisham, Benjamin Orkild, Alan Morris, Eugene Kholmovski, Erik Bieging, Eugene Kwan, Ravi Ranjan, Ed DiBella, Shireen Elhabian
MRI Volume-Based Robust Brain Age Estimation Using Weight-Shared Spatial Attention in 3D CNNs
Vamshi Krishna Kancharla, Neelam Sinha
INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D accelerated MRI reconstruction
Yamin Arefeen, Brett Levac, Zach Stoebner, Jonathan Tamir
Enhance the Image: Super Resolution using Artificial Intelligence in MRI
Ziyu Li, Zihan Li, Haoxiang Li, Qiuyun Fan, Karla L. Miller, Wenchuan Wu, Akshay S. Chaudhari, Qiyuan Tian