Microwave seminar | 24 June 2024

In this talk will explore the role of AI in various aspects of the MRI workflow, including image reconstruction, segmentation, RF coil design, and pulse sequence design. We will discuss how AI-driven techniques can improve image quality, reduce scan times, and streamline the segmentation of anatomical structures and pathologies. We will also explore how AI can optimize RF coil designs for better signal-to-noise ratios and refine pulse sequences for more efficient imaging protocols.

Main paper/arXiv, related to the seminar, and other references:

1. Deep Learning-Based Quasi-Automatic Tool for Regional Quantitative 17-Segment Analysisof Myocardial Fibrosis
2. Deep Learning -Based Algorithm for MRI Lumbar Vertebrae Instance Segmentation
3. CNN‐based fully automatic wrist cartilage volume quantification in MR images: A comparative analysis between different CNN architectures
4. A Deep Learning Framework for Cardiac MR Under-Sampled Image Reconstruction with a Hybrid Spatial and k-Space Loss Function