Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques.

BACKGROUND AND PURPOSE: Brain volume loss is currently a MR imaging marker of neurodegeneration in MS. Available quantification algorithms perform either direct (segmentation-based techniques) or indirect (registration-based techniques) measurements. Because there is no reference standard technique, the assessment of their accuracy and reliability remains a difficult goal. Therefore, the purpose of this work was to assess the robustness of 7 different postprocessing algorithms applied to images acquired from different MR imaging systems. MATERIALS AND METHODS: Nine patients with MS were followed longitudinally over 1 year (3 time points) on two 1.5T MR imaging systems. Brain volume change measures were assessed using 7 segmentation algorithms: a segmentation-classification algorithm, FreeSurfer, BBSI, KN-BSI, SIENA, SIENAX, and JI algorithm. RESULTS: Intersite variability showed that segmentation-based techniques and SIENAX provided large and heterogeneous values of brain volume changes. A Bland-Altman analysis showed a mean difference of 1.8%, 0.07%, and 0.79% between the 2 sites, and a wide length agreement interval of 11.66%, 7.92%, and 11.94% for the segmentation-classification algorithm, FreeSurfer, and SIENAX, respectively. In contrast, registration-based algorithms showed better reproducibility, with a low mean difference of 0.45% for BBSI, KN-BSI and JI, and a mean length agreement interval of 1.55%. If SIENA obtained a lower mean difference of 0.12%, its agreement interval of 3.29% was wider. CONCLUSIONS: If brain atrophy estimation remains an open issue, future investigations of the accuracy and reliability of the brain volume quantification algorithms are needed to measure the slow and small brain volume changes occurring in MS.

[Modalities for the functionning of a Care Center for women at high risk for breast and ovarian cancers: The French experience of Tenon Hospital]. / Modalités de fonctionnement d'un centre de suivi des femmes à haut risque de cancer du sein et de l'ovaire : une expérience à l'hôpital Tenon.

High risk may be defined as either an absolute risk greater than 20 % or a relative risk greater than 4. Concerning breast and ovarian cancer, high risk patients include carriers of a constitutive deleterious mutation of BRCA1 or BRCA2 genes, patients with a significant family history of breast or ovarian cancer, and patients who have been diagnosed a benign breast lesion with a high risk of degeneration, i.e. atypical hyperplasia. Following up such patients relies on specific strategies. A center including a large panel of physicians involved in the various modalities for patients' management (geneticians, radiologists, gynecologists, plastic surgeons, pathologists, endocrinologists, psychologists, medical oncologists) has been created at Tenon Hospital with this purpose. The collaboration of these different specialists with the referent physician of the patient allows for the definition and the implementation of a patient-centered follow-up continuously updated to take into account the different periods of a woman's life, according to best practices recommendations and the evolving state-of-the art.

The SIMRI project: a versatile and interactive MRI simulator.

This paper gives an overview of SIMRI, a new 3D MRI simulator based on the Bloch equation. This simulator proposes an efficient management of the T2* effect, and in a unique simulator integrates most of the simulation features that are offered in different simulators. It takes into account the main static field value and enables realistic simulations of the chemical shift artifact, including off-resonance phenomena. It also simulates the artifacts linked to the static field inhomogeneity like those induced by susceptibility variation within an object. It is implemented in the C language and the MRI sequence programming is done using high level C functions with a simple programming interface. To manage large simulations, the magnetization kernel is implemented in a parallelized way that enables simulation on PC grid architecture. Furthermore, this simulator includes a 1D interactive interface for pedagogic purpose illustrating the magnetization vector motion as well as the MRI contrasts.