TIBM: Change detection and prediction

Automatic change detection is a tool of great interest for monitoring evolving pathologies. The aim is to identify changes over time between two exams of a given subject. This may allow a better care of the patient (personalized medicine), either for diagnosis, prognosis or evaluation of therapeutic response. Thus, novel methods have been developed to analyze longitudinal diffusion MRI sequences, that: use statistical tests adapted to several representations of the diffusion process 2-BNHR12 take into account the positive definite property of diffusion tensor 2-GNHB12 or the geometry of the white matter fiber bundles 2-GNBH13. In addition, work has been conducted in the project ERC FBrain for the temporal modeling of brain maturation 5-PRSS12 and gyrification 2-PRSK16.

PhD theses

• C. Heimburger, Evaluation précoce de la croissance tumorale des résidus post-chirurgicaux des glioblastomes par IRM-ITD., en cours
• J. Pontabry, Construction d'atlas en IRM de diffusion : application à l'étude de la maturation cérébrale, october 2013
• A. Grigis, Approches statistiques pour la détection de changements en IRM de diffusion. Application au suivi longitudinal de pathologies neurodégénératives, september 2012
• H. Boisgontier, Détection automatique de changements en IRM de diffusion. Application à la sclérose en plaques, june 2010
• M. Bosc, Contribution à la détection de changements dans des séquences IRM 3D multimodales, december 2003

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