Biomedical subjects
Publications and source records attributed to P Fillard.
Functional MRI is a technique of imaging which is developing fast as it allows non-aggressive evaluation of brain functions. Diffusion, perfusion and activation are each used to study brain responsiveness to a given task. As a pretherapeutic routine investigation, in brain tumours, it can be helpful as an additional tool to morphological MRI in evaluating the prognosis of patients.
OBJECTIVES: To illustrate the value of diffusion tensor imaging and tractography in the diagnosis and follow-up of central pontine myelinolysis. CASE REPORT: We report a case of central pontine myelinolysis in a 29 year old woman, also anorexic, studied using MR Diffusion Tensor Imaging (DTI) and Fibre Tracking (FT) focused on the pons, and compared with the studies of 5 normal volunteers. Tractography showed a swollen aspect of the right corticospinal fiber tract correlating with mild left lower extremity deficit at clinical evaluation. The pontine fibers were posteriorly displaced but intact. The sensory tracts were also intact. Apparent Diffusion Coefficient values were increased and Fractional Anisotropy was decreased in the lesions. Follow up imaging showed persistent abnormal ADC and FA values in the pons although the left cortico-spinal tract returned to normal, consistent with the clinical outcome. CONCLUSION: Diffusion Tensor Imaging MR and Fiber tractography are a new method to analyse white matter tracts. It can be used to prospectively evaluate the location of white matter tract lesions at the acute phase of central pontine myelinolysis and follow up.
Spinal cord astrocytomas are rare neoplasms that can result in alteration of the spinal cord structural integrity, which can be assessed by using diffusion tensor imaging methods. Our objective was to visualize the deformation of the posterior spinal cord lemniscal and corticospinal tracts in 5 patients with low-grade astrocytomas compared with 10 healthy volunteers by using 3D fiber-tracking reconstructions.
PURPOSE: Our aim was to study the fractional anisotropy (FA) variations and the fiber tracking (FT) patterns observed in patients with myelitis. MATERIAL AND METHODS: Fifteen patients with symptomatic myelitis and 11 healthy subjects were prospectively selected. We performed T2-weighted and diffusion tensor imaging on a 1.5T MR scanner. FA and apparent diffusion coefficient maps were computed in both healthy subjects and patients. In each patient, we performed FT to study pathologic aspects on this imaging method. FA data were analyzed by using z-scores. RESULTS: For the healthy subjects, averaged FA values ranged from 0.745 to 0.751. All abnormal areas seen on T2-weighted imaging had a significantly decreased FA value. In 9 patients (60%), FA maps showed decreased FA areas, whereas T2-weighted imaging findings were normal. These areas matched the neurologic deficit in 33%. Eighty percent of patients had multiple decreased FA areas. Five patients (33%) had increased FA values in normal T2-weighted areas. CONCLUSION: We observed specific FA and FT pattern variations in patients with myelitis.
We report here two cases of diffuse axonal injury (DAI) studied by MR diffusion tensor imaging (DTI) and fibre tracking (FT) focused on the corpus callosum. In one case, DTI and FT pattern matched the diagnosis of broken white matter tracts. In the other case there was a discrepancy between DTI and FT data that showed unaltered white matter tracts with the presence of intra-cellular oedema. These data suggested that DTI and FT are able to differentiate between traumatic cytotoxic oedema and broken fibres in the case of DAI.
In inter-subject registration, one often lacks a good model of the transformation variability to choose the optimal regularization. Some works attempt to model the variability in a statistical way, but the re-introduction in a registration algorithm is not easy. In this paper, we interpret the elastic energy as the distance of the Green-St Venant strain tensor to the identity, which reflects the deviation of the local deformation from a rigid transformation. By changing the Euclidean metric for a more suitable Riemannian one, we define a consistent statistical framework to quantify the amount of deformation. In particular, the mean and the covariance matrix of the strain tensor can be consistently and efficiently computed from a population of non-linear transformations. These statistics are then used as parameters in a Mahalanobis distance to measure the statistical deviation from the observed variability, giving a new regularization criterion that we called the statistical Riemannian elasticity. This new criterion is able to handle anisotropic deformations and is inverse-consistent. Preliminary results show that it can be quite easily implemented in a non-rigid registration algorithms.