Hugues Benoit-Cattin

Intensity non-uniformity correction in MRI: Existing methods and their validation

Magnetic resonance imaging is a popular and powerful non-invasive imaging technique. Automated analysis has become mandatory to efficiently cope with the large amount of data generated using this modality. However, several artifacts, such as intensity non-uniformity, can degrade the quality of acquired data. Intensity non-uniformity consists in anatomically irrelevant intensity variation throughout data. It can be induced by the choice of the radio-frequency coil, the acquisition pulse sequence and by the nature and geometry of the sample itself. Numerous methods have been proposed to correct this artifact. In this paper, we propose an overview of existing methods. We first sort them according to their location in the acquisition/processing pipeline. Sorting is then refined based on the assumptions those methods rely on. Next, we present the validation protocols used to evaluate these different correction schemes both from a qualitative and a quantitative point of view. Finally, availability and usability of the presented methods is discussed.

Medical Images Simulation, Storage, and Processing on the European DataGrid Testbed

The European 1ST DataGrid project was a pioneer in identifying the medical imaging field as an application domain that can benefit from Grid technologies. This paper describes how and for which purposes medical imaging applications can be Grid-enabled. Applications that have been deployed on the DataGrid testbed and middleware are described. They relate to medical image manipulation, including image production, secured image storage, and image processing. Results show that Grid technologies are still in their youth to address all issues related to complex medical imaging applications. If the benefit of Grid enabling for some medical applications is clear, there remain opened research and technical issues to develop and integrate all necessary services.

A Virtual Imaging Platform for Multi-Modality Medical Image Simulation

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workίow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011.

Scalable discrepancy measures for segmentation evaluation

We propose a set of scalable discrepancy measures that may be applied for segmentation evaluation when a reference is known. The proposed measures take into account under and over detected points within an adjustable area. They give the intensity of the discrepancy and its relative position. Furthermore a scale parameter allows the accuracy of the measures to be adjusted.

Dynamic Partitioning of GATE Monte-Carlo Simulations on EGEE

The EGEE Grid offers the necessary infrastructure and resources for reducing the running time of particle tracking Monte-Carlo applications like GATE. However, efforts are required to achieve reliable and efficient execution and to provide execution frameworks to end-users. This paper presents results obtained with porting the GATE software on the EGEE Grid, our ultimate goal being to provide reliable, user-friendly and fast execution of GATE to radiation therapy researchers. To address these requirements, we propose a new parallelization scheme based on a dynamic partitioning and its implementation in two different frameworks using pilot jobs and workflows. Results show that pilot jobs bring strong improvement w.r.t. regular gLite submission, that the proposed dynamic partitioning algorithm further reduces execution time by a factor of two and that the genericity and user-friendliness offered by the workflow implementation do not introduce significant overhead.

Image segmentation functional model

Abstract We propose a new approach of the image segmentation methods. This approach is based on a functional model composed of five elementary blocks called in an iterative process. Different segmentation methods can be decomposed with such a scheme and lead to elementary building blocks with unified functionality and interfaces. We present the decompositions of three segmentation methods and the implementation results, which illustrate the potential of the proposed model. This generic model is a common framework, which makes segmentation techniques more readable and offers new perspectives for the development, the comparison and the implementation of segmentation methods.

Monte Carlo simulation on heterogeneous distributed systems: A computing framework with parallel merging and checkpointing strategies

This paper introduces an end-to-end framework for efficient computing and merging of Monte Carlo simulations on heterogeneous distributed systems. Simulations are parallelized using a dynamic load-balancing approach and multiple parallel mergers. Checkpointing is used to improve reliability and to enable incremental results merging from partial results. A model is proposed to analyze the behavior of the proposed framework and help tune its parameters. Experimental results obtained on a production grid infrastructure show that the model fits the real makespan with a relative error of maximum 10%, that using multiple parallel mergers reduces the makespan by 40% on average, that checkpointing enables the completion of very long simulations and that it can be used without penalizing the makespan.

New discrepancy measures for segmentation evaluation

In this paper, we propose new evaluation measures for scene segmentation results, which are based on computing the difference between a region extracted from a segmentation map and the corresponding one on an ideal segmentation. The proposed measures take into account separately both under and over detected pixels. It also associates in its computation the compactness of the region under investigation.

Magnetic resonance imaging (MRI) simulation on a grid computing architecture

In this paper, we present the implementation of a Magnetic Resonance Imaging (MRI) simulator on a GRID computing architecture. The simulation process is based on the resolution of Bloch equation [1] in a 3D space. The computation kernel of the simulator is distributed to the grid nodes using MPICH-G2 [2]. The results presented show that simulation of 3D MRI data is achieved with a reasonable cost which gives new perspectives to MRI simulations usage.

Magnetic resonance imaging method based on magnetic susceptibility effects to estimate bubble size in alveolar products: application to bread dough during proving.

Magnetic resonance imaging has proven its potential application in bread dough and gas cell monitoring studies, and dynamic processes such as dough proving and baking can be monitored. However, undesirable magnetic susceptibility effects often affect quantification studies, especially at high fields. A new low-field method is presented based on local assessment of porosity in spin-echo imaging, local characterization of signal loss in gradient-echo imaging and prediction of relaxation times by simulation to estimate bubble radii in bread dough during proving. Maps of radii showed different regions of dough constituting networks which evolved during proving. Mean radius and bubble distribution were assessed during proving.