Jean-Philippe Thiran

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Intra-operative MR imaging is an emerging tool for image guided (neuro)surgery. Due to the small size of the magnets and the short acquisition time, the images produced by such devices are often subject to distortions. In this work, we show the particular case of images provided by an ODIN device (Odin Medical Technologies, Newton, MA 02458, USA). Such images suffer from geometric distortions and an important bias field in the luminance. In order to simultaneously correct these deformations, we propose to r

Motivation: Estimating longitudinal changes in imaging biomarkers is challenging due to the multiple sources of variation during acquisition that can influence the analysis of MRI data. Goal(s): To provide a robust estimate of longitudinal changes based on the comparison of cross-sectional imaging biomarkers from different time points. Approach: We introduce RIMLA, a Reproducibility-Informed Method for Longitudinal Assessment that quantifies longitudinal imaging biomarker changes while accounting for the ro

In this paper, we address the problem of retrieving soil moisture over a grassland alpine area from Synthetic Aperture Radar (SAR) data using a statistical algorithm trained by simulations of a physical model. A time series of C-band VV-polarized Wide Swat ...

In this paper, we present the segmentation of the head and neck lymph node regions using a new active contour based atlas registration model. We propose to segment the lymph node regions without directly including them in the atlas registration process; in ...

In this paper we define a scale-space for cortical mean curvature maps on the sphere, that offers a hierarchical representation of the brain cortical structures, useful in multi-scale registration and analysis algorithms. A spherical feature map is obtaine ...

Background Since the emergence of diffusion tensor imaging, a lot of work has been done to better understand the properties of diffusion MRI tractography. However, the validation of the reconstructed fiber connections remains problematic in many respects. ...

The relationship between electrophysiological and functional magnetic resonance imaging (fMRI) signals remains poorly understood. To date, studies have required invasive methods and have been limited to single functional regions and thus cannot account for ...

The high complexity of cortical convolutions in humans remains a mystery for both engineers to measure and compare it, and for biologists to understand it. We developed a method for the quantification of the cortical area allowed by the process of folding. Our method uses accurate three-dimensional cortical reconstruction and compute local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize subregional gyrification abnorma

Impaired myelin plays a central role in a wide range of degenerative brain diseases. A method for non-invasive and in vivo assessment of myelin content within clinically acceptable acquisition times is thus desirable. In this work, a 3D multi-echo gradient and spin-echo (GRASE) sequence was accelerated with CAIPIRINHA to achieve high-resolution and whole-brain myelin imaging in less than ten minutes. Myelin water fraction (MWF) maps were derived from multi-echo GRASE data in a cohort of healthy subjects and

In this paper we propose to use clustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/ tracking systems that overestimate the number of targets. Clustering techniques are applied to ...

In this paper we propose an approach to count the number of pedestrians, given a trajectory data set provided by a tracking system. The tracking process itself is treated as a black box providing us the input data. The idea is to apply a hierarchical clustering algorithm, using different data representations and distance measures, as a post-processing step. The final goal is to reduce the difference between the number of tracked pedestrians and the real number of individuals present in the scene.

Deep learning shows great promise in enhancing ultrasound acquisitions using sparse arrays. We previously introduced a deep learning-based in-painting method to estimate missing raw radio frequency signals in single plane wave acquisitions with sparse arrays in reception. This study evaluates its robustness with fully sparse arrays, where both transmission and reception are undersampled. We compare three training strategies: (i) training from scratch on reception-only sparse array data, (ii) training from s

Subthalamic nucleus (STN) Deep brain stimulation (DBS) has demonstrated its efficacy in the treatment of Parkinson´s disease. Accurate targeting of the STN is a crucial step for the success of DBS surgery. MRI has shown to be reliable and allows direct targeting. Unfortunately, STN is not always clearly identifiable on standard T2-weighted images. In these situations, manual targeting procedures represent only an estimation of the real STN position. Recently, registration algorithms have been developed to a

In functional stereotactic neurosurgery, one of the cornerstones upon which the success and the operating time depends is an accurate targeting. The subthalamic nucleus (STN) is the usual target involved when applying Deep Brain Stimulation (DBS) for the Parkinson s disease (PD). Unfortunately, STN is usually not clearly visible in common medical imaging modalities, which justifies the use of atlas-based segmentation techniques to infer the STN location. We propose a scheme that allows both, to perform a co

We use DT-MRI and statistical fibre tracking in order to quantify the left-right asymmetry of connectivity between the posterior part of the superior temporal gyrus (Wernicke) and the homolateral pars opercularis of the frontal inferior gyrus (Broca) in 12 healthy subjects. Our results show that there is a left-right brain asymmetry in terms of connectivity. A right handed population has denser association pathways left which seems to be fairly constant over that population (small variance). The left handed

We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Another issue addressed is how to select the number of eigenfaces to achieve a good classification rate. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies and discuss their effectiveness in the case of face-class modeling.

Facial expression recognition is a hard and ambiguous problem in computer vision. It is hard due to the wide variety of faces and the wide variety of ways of representing the same expression. And it is ambiguous because, even though common approaches treat it as a classification problem, actually when looking at the same scene, different people can feel a different expression. In this presentation, we will show preliminary results obtained in an ongoing project of Discrete Choice Modeling of human perceptio

In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior classification over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant t ...

One of the major limitations of diffusion MRI tractography is that the tractograms, i.e. set of fiber tracts, recovered by existing algorithms are not truly quantitative. Hence, the structural connectivity between different brain regions, a.k.a. connectomi ...

Purpose MP2RAGE T-1-weighted imaging has been shown to be beneficial for various applications, mainly because of its good grey-white matter contrast, its B-1-robustness and ability to derive T(1)maps. Even using parallel imaging, the method requires long a ...

Jean-Philippe Thiran

Chattez avec Graph Search

Simultaneous Registration and Bias Correction of Brain Intra-operative MR Images

RIMLA: Reproducibility-Informed Method for Longitudinal Assessment

Soil Moisture Estimation by SAR in Alpine Fields Using Gaussian Process Regressor Trained by Model Simulations

Segmentation of head and neck lymph node regions for radiotherapy planning, using active contour based atlas registration

A Scale-Space of Cortical Feature Maps

Estimating the Confidence Level of White Matter Connections Obtained with MRI Tractography

Methods for Determining Frequency- and Region-Dependent Relationships Between Estimated LFPs and BOLD Responses in Humans

A Surface-based approach to Quantify Local Cortical Gyrification

Accelerating Multi-Echo GRASE with CAIPIRINHA for Fast and High-Resolution Myelin Water Imaging

Counting pedestrians in video sequences using trajectory clustering

Multi-layer hierarchical clustering of pedestrian trajectories for automatic counting of people in video sequences

Addressing Domain Shifts Across Sparse Array Strategies for Deep Learning Inpainting in Ultrafast Ultrasound Imaging

A Cross Validation Study of STN targeting on MRI: from Stereotactic Procedures and Automatic Registration Algorithms to direct identification

Automatic Subthalamic Nucleus Targeting for Deep Brain Stimulation. A Validation Study

DTI tractography of theWernicke and Broca connectivity in right and left hander

Face Class Modeling in Eigenfaces Space

Modelling Human Perception of Facial Expressions by Discrete Choice Models

Joint Object Segmentation and Behavior Classification in Image Sequences

On evaluating the accuracy and biological plausibility of diffusion MRI tractograms

Accelerated MP2RAGE imaging using Cartesian phyllotaxis readout and compressed sensing reconstruction