Biomedical subjects

Florent Segonne

Publications and source records attributed to Florent Segonne.

3 recordsLinked to original sources

PubMed2006

Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location.

Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four methods: Brain Extraction Tool (BET; Smith [2002]: Hum Brain Mapp 17:143-155); 3dIntracranial (Ward [1999] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage 22:1060-1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41-54; Shattuck et al. [2001] Neuroimage 13:856-876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T1-weighted image sets; and four diagnostic groups (depressed, Alzheimer's, young and elderly control). To provide a criterion for outcome assessment, two experts manually stripped six sagittal sections for each dataset in locations where brain and nonbrain tissue are difficult to distinguish. Methods were compared on Jaccard similarity coefficients, Hausdorff distances, and an Expectation-Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more robust across diagnostic groups compared with 3dIntracranial and BET. With respect to specificity, BSE tended to perform best across all groups, whereas HWA was more sensitive than other methods. The results of this study may direct users towards a method appropriate to their T1-weighted datasets and improve the efficiency of processing for large, multisite neuroimaging studies.

Adult↗

PubMed2004

Metallic electrodes and leads in simultaneous EEG-MRI: specific absorption rate (SAR) simulation studies.

The purpose of this study was to investigate the changes in specific absorption rate (SAR) in human-head tissues while using nonmagnetic metallic electroencephalography (EEG) electrodes and leads during magnetic resonance imaging (MRI). A realistic, high resolution (1 mm(3)) head model from individual MRI data was adopted to describe accurately thin tissues, such as bone marrow and skin. The RF power dissipated in the human head was evaluated using the FDTD algorithm. Both surface and bird cage coils were used. The following numbers of EEG electrodes/leads were considered: 16, 31, 62, and 124. Simulations were performed at 128 and 300 MHz. The difference in SAR between the electrodes/leads and no-electrodes conditions was greater with the bird cage coil than with the surface coil. The peak 1 g averaged SAR values were highest at 124 electrodes, increasing to as much as two orders of magnitude (x172.3) at 300 MHz compared to the original value. At 300 MHz, there was a fourfold (x3.6) increase of SAR averaged over the bone marrow, and a sevenfold (x7.4) increase in the skin. At 128 MHz, there was a fivefold (x5.6) increase of whole head SAR. Head models were obtained from two different subjects, with an inter-subject whole head SAR variability of 3%. .

Adult↗

PubMed2004

Variational, geometric, and statistical methods for modeling brain anatomy and function.

We survey the recent activities of the Odyssée Laboratory in the area of the application of mathematics to the design of models for studying brain anatomy and function. We start with the problem of reconstructing sources in MEG and EEG, and discuss the variational approach we have developed for solving these inverse problems. This motivates the need for geometric models of the head. We present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical magnetic resonance (MR) images. Anatomical connectivity can be extracted from diffusion tensor magnetic resonance images but, in the current state of the technology, it must be preceded by a robust estimation and regularization stage. We discuss our work based on variational principles and show how the results can be used to track fibers in the white matter (WM) as geodesics in some Riemannian space. We then go to the statistical modeling of functional magnetic resonance imaging (fMRI) signals from the viewpoint of their decomposition in a pseudo-deterministic and stochastic part that we then use to perform clustering of voxels in a way that is inspired by the theory of support vector machines and in a way that is grounded in information theory. Multimodal image matching is discussed next in the framework of image statistics and partial differential equations (PDEs) with an eye on registering fMRI to the anatomy. The paper ends with a discussion of a new theory of random shapes that may prove useful in building anatomical and functional atlases.

Algorithms↗