Instructor in Radiology at Harvard Medical School.
Boston Children's Hospital
300 Longwood Avenue
Department of Radiology, Wolbach 215
Boston MA 02115
I am currently an Instructor at the Computational Radiology Laboratory at Children's Hospital Boston, Harvard Medical School.
My research goal is to develop novel medical imaging technologies to improve our understanding of the brain and particularly of brain changes in disease and injuries. My research is driven by the motivation to overcome the current limitations of diffusion-weighted magnetic resonance imaging (DW-MRI) to enable fast high spatial resolution characterization of brain tissues in routine clinical practice. To do so, I focus on jointly considering the image acquisition process and processing, with the goal of combining both image acquisition advances and novel analysis methods.
I completed an engineering degree in Applied Mathematics and Computer Science in Toulouse University (France), a MS degree in Image Analysis, Vision and Robotics from the Grenoble University (France) and received my Ph.D. degree in Applied Mathematics and Computer Science from Grenoble University in December 2008. During my Ph.D., I focused on anatomical MRI analysis and, more precisely, on brain image segmentation. An important contribution developed in my thesis was the formulation of the notion of ‘collaborative computing’ in a unified and theoretically sound Bayesian framework. This work was awarded by the ``Young Investigator Award’’ at MICCAI’08 in New York and by the ``Best PhD in Applied Mathematics’’ price of INPG Grenoble University in 2009.
I joined to the Computational Radiology Laboratory (CRL), directed by Dr. Simon K. Warfield in January 2009. I chose to shift my main research focus from anatomic MRI analysis to diffusion-weighted MRI. My research at the CRL has been focusing on novel models to characterize the brain white matter in DW-MRI and on innovative approaches to accelerate the acquisition of DW images and to increase the imaged spatial resolution. My research was awarded by a Trainee Abstract Award at HBM2011, two Magna Cum Laude Merit Award at ISMRM2012 and the Elsevier/MEDIA Best Paper Award 2012.
DOMAIN OF INTEREST
- Diffusion MRI, Anatomical MRI
- Novel MRI acquisitions, Q-space sampling techniques, MRI Physics
- Tissue microstructure imaging, biophysical modeling
- Bayesian Analysis, Markov Random Fields & Image Segmentation.
- Medical & Neuroscience Applications.
Powerful Medical Image Viewer.
! Version 0.1.2 released on 02/15/2014 !
LOcal and Cooperative Unified Segmentation (LOCUS)In most approaches, tissue and subcortical structure segmentations of MR brain scans are handled globally over the entire brain volume through two relatively independent sequential steps. We investigated a fully Bayesian joint model that integrates within a multi-agent framework local tissue and structure segmentations and local... [more]
CUbe and SPhere - Multi-Fiber Model diffusion imaging (CUSP-MFM)Parametric and non-parametric models have been proposed to represent the diffusion signal, in order to enable the assessment of white matter connectivity. While it has been widely accepted that a single diffusion tensor provides an excellent parametric representation for an individual white matter fiber bundle, the most appropriate... [more]
Super-Resolution in Diffusion-Weighted Imaging.Diffusion-weighted imaging (DWI) enables non-invasive investigation and characterization of the white-matter but is strongly limited by the relatively poor resolution achievable. Increasing the resolution in DWI holds out the potential to reveal and investigate novel finer fiber structures not visible with conventional diffusion MRI, and will ... [more]
Early Predictors and Clinical Correlates of Autism in Tuberous Sclerosis ComplexTuberous Sclerosis Complex (TSC) is a neurocutaneous autosomal dominant disorder involving mutations of the TSC1 or TSC2 genes. It is characterized by the presence of benign tumors throughout the body, including the brain where they are known as cortical tubers. TSC symptoms may take time to develop and vary considerably from ... [more]