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Magnetic resonance image analysis of thalamic nuclei at high and ultra-high fields
Meritxell Bach Cuadra
Medical Image Analysis Laboratory (MIAL), Radiology Department, Center for BioMedical Imaging (CIBM)
University Hospital Center (CHUV) and University of Lausanne (UNIL)

The thalamus is a highly complex brain structure built from numerous small nuclei differing between them in both histology and function. Each nucleus projects to different parts of the cortex and therefore, is associated with different neurological function. For this reason, the thalamic nuclei are implicated in a wide range of functional impairments and, as consequence, are of central interest in many neurodegenerative studies and clinical applications. In this talk I will present our recent works on imaging and segmentation of in-vivo human thalamic nuclei at high and ultra-high MR structural imaging. Our segmentation techniques are developed for diffusion MRI (dMRI) at 3T and susceptibility weighted imaging (SWI) at 7T. Our research is done in the clinical context of radio-surgical planning for the treatment of essential tremor.

MR FINGERPRINTING PARAMETER ESTIMATION VIA INVERSE REGRESSION
Florence Forbes
Inria Montbonnot

Joint work with: Fabien BOUX – Julyan ARBEL – Emmanuel BARBIER
Magnetic resonance imaging (MRI) can map a wide range of tissue properties but is often limited to observe a single parameter at a time. In order to overcome this problem, Ma et al.1 introduced magnetic resonance fingerprinting (MRF), a procedure based on a dictionary of simulated couples (Xn, Yn) of signals and parameters. Acquired signals called fingerprints are then matched to the closest signal in the dictionary in order to estimate parameters. This requires an exhaustive search in the dictionary which, even for moderately sized problems, becomes costly and possibly intractable2. In this work, we propose an alternative approach to estimate more parameters at a time. Instead of an exhaustive search for every signal, we use the dictionary to learn the functional relationship between signals and parameters. This allows the direct estimation of parameters without the need of searching through the dictionary. The comparison between a standard grid search and the proposed approach suggest that MR Fingerprinting could benefit from a regression approach to limit dictionary size and fasten computation

Toward smart and automated cortical stimulation mapping
Sylvain Harquel
LPNC-IRMaGE
Cortical stimulation established itself as a powerful tool for probing and treating the human brain. Among existing techniques, transcranial magnetic stimulation (TMS) allows for non-invasive stimulation of the cortex in vivo. As the latest technological advance, robotized and neuronavigated TMS paves the way for new acquisition protocols such as smart and automated cortical mapping procedures. The aim of such procedures is to build fast and reliable functional maps, e.g. in the context of diagnosis aid or pre-surgical mapping, and to find the optimal stimulation target maximizing a specific effect on behavior and / or brain state. Its principle relies on a continuous closed-loop interaction between the stimulation of specific areas / networks (e.g. sensorimotor, vision, language systems) and the analysis of external readouts (e.g. electrophysiological recordings, behavioral scores). The use of automated mapping procedures could facilitate their handling for research and clinical routine, while increasing the reliability and reproducibility of its outcomes.