The Course Will Provide an Overview of Recent Advances In Deep-learning Applied to Magnetic Resonance Imaging (mri). We Will Briefly Introduce The Theoretical and Practical Aspects of Mri. Then We Will Discuss How Deep-learning Has Been Applied to The Entire Mri Processing Chain Including Acquisition, Reconstruction, Restoration, Registration, Segmentation, and Diagnosis.coure Prerequist Include Strong Background in Deep-learning (deep Convolutional Neural Networks For Images) and The Basic Principles of Medical Imaging (mri)._ Learning Outcomes# at The End of The Course The Students Will Know# 1. Formulate a Challenge in Mri Processing Pipeline By A Deep-neural-network.__ 2. Implement a Deep-neural-network to Solve a Given Challenge Such As# . Segmentation. . Registration. Reconstruction/restoration . Disease Prediction Quantitative Mri Analysis (specifically, Diffusion-weighted Mri)u 3. Train and Evaluate a Deep-neural-network to Solve a Challenge In Mri .

Faculty: Biomedical Engineering
|Graduate Studies
Pre-required courses

46211 - Deep Learning or 46831 - Introduction to Medical Imaging or 236781 - Deep Learning On Computation or 336502 - Principles of Medical Imaging or 336504 - Principles of Mri

Course with no extra credit

336028 - Deep Learning Applications in Mri