On early brain development from MRI data Abstract: The development of ultrafast 2D acquisition sequences has led to significant improvements in the clinical utility of fetal MRI. However, the slice acquisition time is still very critical and has to be as short as possible to reduce the impact of fetal and maternal motion on the exam. Since 2005 there has been a series of techniques coming from computer vision that have been developed to address these limitations in fetal MRI.…

Building brain atlases and segmentation tools with ex vivo MRI and histology: application to hippocampus, amygdala and beyond Abstract: Ex vivo modalities make it possible to obtain manual delineations of brain anatomy with very high precision. These delineations can be used to build accurate atlases at very high resolution, which can in turn be applied to the segmentation of brain substructures in in vivo MRI scans. While analyzing the brain at the substructure level holds promise to increase our understanding…

Sandy Wells is giving a talk on Wednesday 16 Dec, 11:00, in Huxley 218. He will be talking about his registration work and will give an overview of projects in image-guided therapy in his lab.

Title: Graph-based deformable registration: slice-to-volume mapping and context specific methods Abstract: Image registration methods, which aim at aligning two or more images into one coordinate system, are among the oldest and most widely used algorithms in computer vision. A particular type of registration algorithm, known as graph-based deformable registration, has become popular during the last decade given its robustness, scalability, efficiency and theoretical simplicity. In this thesis, we extend this flexible framework to new scenarios, and propose novel methodological contributions.…

Title: The challenges of developing an active deep-learning platform Abstract: Deep learning is usually linked to Big Data. However, there are several scientific, engineering and medical imaging problems with limited data available (e.g. rare medical conditions). Can Deep Learning prove a useful tool in such cases? COSMONiO is designing NOUS, an active deep learning platform that aims to make high-accuracy predictions using significantly smaller training datasets. NOUS aims to allow experts from any field to train neural networks without any…

Personalized Blood Flow Simulation from an Image-Derived Model: Changing the Paradigm for Cardiovascular Diagnostics Abstract: Coronary heart disease is the leading cause of mortality worldwide, accounting for 1/3 of all global deaths.  Treatment of stable coronary heart disease is typically performed by medication/lifestyle for a lower disease burden or PCI (stenting) for a greater disease  burden.  The choice between these treatments is best determined by an invasive diagnostic test that measures blood flow through a diseased area.  Unfortunately, this invasive…

Improving clinical application of cardiac diffusion tensor MRI Abstract  Magnetic resonance diffusion tensor imaging (MRDTI), also known as DTI, has emerged as a powerful non-invasive tool for mapping the orientation-dependent microanatomical organization of fibrous organs such as brain white matter and myocardium. To do so, it elegantly relates the self-diffusion of water molecules that undergo Brownian motion to proton spin relaxation MR signals. In this talk it will be shown how DTI can shed some light on the left-ventricular micro-structural remodeling following…