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
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
Weixi Yi, Yipei Wang, Natasha Thorley, Alexander Ng, Shonit Punwani, Veeru Kasivisvanathan, Dean C. Barratt, Shaheer Ullah Saeed, Yipeng Hu
Data-driven discovery of mechanical models directly from MRI spectral data
D.G.J. Heesterbeek, M.H.C. van Riel, T. van Leeuwen, C.A.T. van den Berg, A. Sbrizzi
Accelerated, Robust Lower-Field Neonatal MRI with Generative Models
Yamin Arefeen, Brett Levac, Jonathan I. Tamir
Diagnostic Performance of Deep Learning for Predicting Gliomas' IDH and 1p/19q Status in MRI: A Systematic Review and Meta-Analysis
Somayeh Farahani, Marjaneh Hejazi, Mehnaz Tabassum, Antonio Di Ieva, Neda Mahdavifar, Sidong Liu
Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement
Shuaiyu Yuan, Tristan Whitmarsh, Dimitri A Kessler, Otso Arponen, Mary A McLean, Gabrielle Baxter, Frank Riemer, Aneurin J Kennerley, William J Brackenbury, Fiona J Gilbert, Joshua D Kaggie
Synthesizing Proton-Density Fat Fraction and $R_2^*$ from 2-point Dixon MRI with Generative Machine Learning
Suma Anand, Kaiwen Xu, Colm O'Dushlaine, Sumit Mukherjee