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
Deep Learning-Driven Segmentation of Ischemic Stroke Lesions Using Multi-Channel MRI
Ashiqur Rahman, Muhammad E. H. Chowdhury, Md Sharjis Ibne Wadud, Rusab Sarmun, Adam Mushtak, Sohaib Bassam Zoghoul, Israa Al-Hashimi
Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN
Yanxi Chen, Yi Su, Celine Dumitrascu, Kewei Chen, David Weidman, Richard J Caselli, Nicholas Ashton, Eric M Reiman, Yalin Wang
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