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Find here all the news linked to RRI IMPACT (publications, seminars, events, AAP...)

Scientific events

Conférence - "Subjective cognitive decline, white matter hyper intensities and neurodegeneration", Pr. Louis Collins (McGill, Montréal, CA), LaBRI, 18 juin

Subjective cognitive decline, white matter hyper intensities and neurodegeneration

Alzheimer's disease (AD) pathology may be present in the brain as many as 10-15 years before symptoms occur. As in most diseases, early treatment, before too much brain damage has been done, is likely to be more effective. However, accurately identifying people at risk of dementia due to AD early, before symptoms appear, is extremely difficult.  We have studied people with subjective cognitive decline - they have issues with memory or cognition, but not enough to be captured by standard cognitive tests. We will also study people with mild cognitive impairment - their memory issues can be measured with standard tests.  Both groups have significantly increased risk of later dementia, making them very interesting to study for early AD. In this talk, I will describe some of the methods we have developed to segment the hippocampus and to identify white matter hyper intensities and then use this data to study subjective cognitive decline and mild cognitive impairment.
Prof. Louis Collins joined McGill’s Faculty of Medicine with joint appointments in the Department of Neurology and Neurosurgery and the Department of Biomedical Engineering in 1999 as an Assistant professor, promoted to Associate Professor in 2006 and to Full Professor (professeur titulaire) in 2011 and named James McGill Professor in 2019 in recognition of his research, training, and teaching. He was named to Royal Society of Canada as fellow to the Academy of Science in recognition of the innovative development of medical image processing and analysis tools.
He heads the Neuro Imaging and Surgical Technologies (NIST) laboratory at the Brain Imaging Center of the Montreal Neurological Institute. His team of ~15 trainees and engineers develop and use computerized image processing techniques such as non-linear image registration and model-based segmentation to automatically identify structures within the human brain. His group has published over 390 peer reviewed papers and over 380 peer reviewed conference papers and abstracts  yielding a Google h-index=121. His research is detailed at . 

IMPACT Mid-term Scientific Day - 24 juin 2024, 8h30, Centre Broca Nouvelle Aquitaine

Le RRI IMPACT organise le 24 juin prochain au Centre Broca Nouvelle Aquitaine sa journée scientifique mi-parcours.

Son but est à la fois de réunir la communauté IMPACT pour faire un point sur les avancées et les perspectives scientifiques du programme mais également de présenter à l'ensemble de la communauté du département Sciences et Technologies pour la Santé ainsi qu'aux autres départements partenaires du projet, les objets et les enjeux d'IMPACT.

Deux conférenciers invités nous présenteront également leur travaux de recherche sur des objets croisant ceux d'IMPACT ce qui viendra alimenter les discussions et renforcer les collaborations internationales du projet.

Elle sera enfin l'occasion de réfléchir à la promotion et à la valorisation des résultats du programme.

Inscription, soumission de poster et programme => c'est ici

Plus d'infos => c'est ici

MIDL 2024 Congress (Medical Imaging with Deep Learning), Paris, 3-5 july 2024

The MIDL conference aims to be a forum for deep learning researchers, clinicians and health-care companies to take a leap in the application of deep learning based automatic image analysis in disease screening, diagnosis, prognosis, treatment selection and treatment monitoring. The conference will have a broad scope and include topics such as computer-aided screening and diagnosis, detection, segmentation, (multi-modal) registration, image reconstruction and synthesis. Furthermore, we discuss issues such as the need for large curated and annotated datasets, noisy reference standards, and the high-dimensionality of medical data. Software demonstrations, presentation of medical data sets and innovative clinical applications are also covered as focus points for integration of deep learning algorithms in clinical practice.

MIDL currently offers a three-day program with keynote presentations from invited speakers, oral presentations, posters, and live demonstrations of deep learning algorithms from academia and industry.

More information:

Contact : Pierrick Coupé -

Sponsored by RRI IMPACT

Call for projects


No AAP campaign for now. See you soon !

Crédits photo - jannoon028 on Freepik

New publications

Publication - Accelerated 3D multi-echo spin-echo sequence with a subspace constrained reconstruction for whole mouse brain T2 mapping by Trotier AJ, Corbin N, Miraux S. and Ribot EJ., Magn Reson Med.


Purpose: To accelerate whole-brain quantitative T2 mapping in preclinical
imaging setting.

Methods: A three-dimensional (3D) multi-echo spin echo sequence was highly undersampled with a variable density Poisson distribution to reduce the acquisition time. Advanced iterative reconstruction based on linear subspace constraints was employed to recover high-quality raw images. Different subspaces, generated using exponential or extended-phase graph (EPG) simulations or from low-resolution calibration images, were compared. The subspace dimension was investigated in terms of T2 precision. The method was validated on a phantom containing a wide range of T2 and was then applied to monitor metastasis growth in the mouse brain at 4.7T. Image quality and T2 estimation were assessed for 3 acceleration factors (6/8/10).

Results: The EPG-based dictionary gave robust estimations of a large range of T2. A subspace dimension of 6 was the best compromise between T2 precision and image quality. Combining the subspace constrained reconstruction with a highly undersampled dataset enabled the acquisition of whole-brain T2 maps, the detection and the monitoring of metastasis growth of less than 500 𝜇m3.

Conclusion: Subspace-based reconstruction is suitable for 3D T2 mapping. This method can be used to reach an acceleration factor up to 8, corresponding to an acquisition time of 25 min for an isotropic 3D acquisition of 156 𝜇m on the mouse brain, used here for monitoring metastases growth.

Keywords : compressed sensing, multi-echo spin-echo, small animal, subspace reconstruction, T2 mapping

Publication - Dynamic Evolution of Infarct Volumes at MRI in Ischemic Stroke Due to Large Vessel Occlusion by Fanny Munsch, David Planes, Hikaru Fukutomi, Gaultier Marnat, Thomas Courret, Emilien Micard, Bailiang Chen, Bertrand Lapergue, Jean Marc Olivot, Vincent Dousset, Igor Sibon, Michel Thiebaut de Schotten, Thomas Tourdias, Neurology

Abstract :

Background and objectives: The typical infarct volume trajectories in stroke patients, categorized as slow or fast progressors, remain largely unknown. This study aimed to reveal the characteristic spatiotemporal evolutions of infarct volumes caused by large vessel occlusion (LVO) and show that such growth charts help anticipate clinical outcomes.

Methods: We conducted a secondary analysis from prospectively collected databases (FRAME, 2017-2019; ETIS, 2015-2022). We selected acute MRI data from anterior LVO stroke patients with witnessed onset, which were divided into training and independent validation datasets. In the training dataset, using Gaussian mixture analysis, we classified the patients into 3 growth groups based on their rate of infarct growth (diffusion volume/time-to-imaging). Subsequently, we extrapolated pseudo-longitudinal models of infarct growth for each group and generated sequential frequency maps to highlight the spatial distribution of infarct growth. We used these charts to attribute a growth group to the independent patients from the validation dataset. We compared their 3-month modified Rankin scale (mRS) with the predicted values based on a multivariable regression model from the training dataset that used growth group as an independent variable.

Results: We included 804 patients (median age 73.0 years [interquartile range 61.2-82.0 years]; 409 men). The training dataset revealed nonsupervised clustering into 11% (74/703) slow, 62% (437/703) intermediate, and 27% (192/703) fast progressors. Infarct volume evolutions were best fitted with a linear (r = 0.809; p < 0.001), cubic (r = 0.471; p < 0.001), and power (r = 0.63; p < 0.001) function for the slow, intermediate, and fast progressors, respectively. Notably, the deep nuclei and insular cortex were rapidly affected in the intermediate and fast groups with further cortical involvement in the fast group. The variable growth group significantly predicted the 3-month mRS (multivariate odds ratio 0.51; 95% CI 0.37-0.72, p < 0.0001) in the training dataset, yielding a mean area under the receiver operating characteristic curve of 0.78 (95% CI 0.66-0.88) in the independent validation dataset.

Discussion: We revealed spatiotemporal archetype dynamic evolutions following LVO stroke according to 3 growth phenotypes called slow, intermediate, and fast progressors, providing insight into anticipating clinical outcome. We expect this could help in designing neuroprotective trials aiming at modulating infarct growth before EVT.