Siemens Healthcare Gmbh

Centre Hospitalier Universitaire Vaudois

Siemens Healthineers Ag

Jonas Richiardi

Tobias Kober

Benedicte Marechal

Bénédicte Maréchal

Ricardo Alberto Corredor Jerez

Veronica Ravano

Julius Popp

Ricardo Corredor Jerez

A system and a method for mapping lesions or damage instances of a brain. The method includes receiving a lesion segmentation mask for the brain and receiving a tractography atlas. A connectivity damage brain map is constructed from (i) superimposing the lesion segmentation mask and a tractography atlas-based image, and (ii) combining information from the lesion segmentation mask with information from the tractography atlas-based image. The tractography atlas-based image is an image obtained from the tractography atlas, and the tractography atlas-based image and the lesion segmentation mask are registered to a common space.

Method and system for characterizing an impact of brain lesions on brain connectivity using MRI

A system and a method measure volumetric changes of brain structures. The method includes initializing an intensity value of all voxels of a 3D voxel dataset representing the brain of a subject to an initial value preferentially equal to 0. For all voxels that belong to a segmented brain structure for which reference data of a longitudinal reference model exists, automatically executing the following steps: calculating a deviation of a volume change for the segmented brain structure from the longitudinal reference model, normalizing the deviation to obtain a quantitative value of the volume change on a same scale for voxel's belonging to different brain structures; and setting the intensity value of the voxels to the previously obtained quantitative value Q. The voxels of the 3D voxel dataset are displayed in a form of a longitudinal deviation map.

Method and system for fast assessment of brain change normality

A system and method automatically predicts an evolution of a cognitive score for a subject by classifying a cognitive data set for the subject into a first or second class by determining the cognitive set of data for each subject of a group, acquiring for each subject a neuropsychological score used for classifying each subject in the first or second class, and training a two-class machine learning classification algorithm on the cognitive data sets of all subjects. For each subject, the cognitive data set is used as input of the algorithm and the obtained classification of the subject as output target of the algorithm. The algorithm classifies each cognitive data set in the first or second class. The evolution of the cognitive score of a subject is predicted by the trained algorithm for automatically classifying a new cognitive dataset for the subject into the first or second class.

Integrative prediction of a cognitive evolution of a subject

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Geneva, Switzerland

Jonas Richiardi