Biomedical subjects

Pierre Fillard

Publications and source records attributed to Pierre Fillard.

5 recordsLinked to original sources

PubMed2006

Measuring brain variability by extrapolating sparse tensor fields measured on sulcal lines.

Modeling and understanding the variability of brain structures is a fundamental problem in neurosciences. Improved mathematical representations of structural brain variation are needed to help detect and understand genetic or disease related sources of abnormality, as well as to improve statistical power when integrating functional brain mapping data across subjects. In this paper, we develop a new mathematical model of normal brain variation based on a large set of cortical sulcal landmarks (72 per brain) delineated in each of 98 healthy human subjects scanned with 3D MRI (age: 51.8+/-6.2 years). We propose an original method to compute an average representation of the sulcal curves, which constitutes the mean anatomy. After affine alignment of the individual data across subjects, the second order moment distribution of the sulcal position is modeled as a sparse field of covariance tensors (symmetric, positive definite matrices). To extrapolate this information to the full brain, one has to overcome the limitations of the standard Euclidean matrix calculus. We propose an affine-invariant Riemannian framework to perform computations with tensors. In particular, we generalize radial basis function (RBF) interpolation and harmonic diffusion partial differential equations (PDEs) to tensor fields. As a result, we obtain a dense 3D variability map that agrees well with prior results on smaller subject samples. Moreover, "leave one (sulcus) out" tests show that our model is globally able to recover the missing information on brain variation when there is a consistent neighboring pattern of variability. Finally, we propose an innovative method to analyze the asymmetry of brain variability. As expected, the greatest asymmetries are found in regions that includes the primary language areas. Interestingly, any such asymmetries in anatomical variance, if it remains after anatomical normalization, could explain why there may be greater power to detect group activation in one hemisphere versus the other in fMRI studies.

Algorithms↗

PubMed2006

Log-Euclidean metrics for fast and simple calculus on diffusion tensors.

Diffusion tensor imaging (DT-MRI or DTI) is an emerging imaging modality whose importance has been growing considerably. However, the processing of this type of data (i.e., symmetric positive-definite matrices), called "tensors" here, has proved difficult in recent years. Usual Euclidean operations on matrices suffer from many defects on tensors, which have led to the use of many ad hoc methods. Recently, affine-invariant Riemannian metrics have been proposed as a rigorous and general framework in which these defects are corrected. These metrics have excellent theoretical properties and provide powerful processing tools, but also lead in practice to complex and slow algorithms. To remedy this limitation, a new family of Riemannian metrics called Log-Euclidean is proposed in this article. They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. This new approach is based on a novel vector space structure for tensors. In this framework, Riemannian computations can be converted into Euclidean ones once tensors have been transformed into their matrix logarithms. Theoretical aspects are presented and the Euclidean, affine-invariant, and Log-Euclidean frameworks are compared experimentally. The comparison is carried out on interpolation and regularization tasks on synthetic and clinical 3D DTI data.

Algorithms↗

PubMed2005

Fast and simple calculus on tensors in the log-Euclidean framework.

Computations on tensors have become common with the use of DT-MRI. But the classical Euclidean framework has many defects, and affine-invariant Riemannian metrics have been proposed to correct them. These metrics have excellent theoretical properties but lead to complex and slow algorithms. To remedy this limitation, we propose new metrics called Log-Euclidean. They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. Indeed, Log-Euclidean computations are Euclidean computations in the domain of matrix logarithms. Theoretical aspects are presented and experimental results for multilinear interpolation and regularization of tensor fields are shown on synthetic and real DTI data.

Algorithms↗

PubMed2005

Incorporating statistical measures of anatomical variability in atlas-to-subject registration for conformal brain radiotherapy.

Deforming a digital atlas towards a patient image allows the simultaneous segmentation of several structures. Such an intersubject registration is difficult as the deformations to recover are highly inhomogeneous. A priori information about the local amount of deformation to expect is precious, since it allows to optimally balance the quality of the matching versus the regularity of the deformation. However, intersubject variability makes it hard to heuristically estimate the degree of deformation. Indeed, the sizes and shapes of various structures differ greatly and their relative positions vary in a rather complex manner. In this article, we perform a statistical study of the deformations yielded by the registration of an image database with an anatomical atlas, and we propose methods to re-inject this information into the registration. We show that this provides more accurate segmentations of brain structures.

Algorithms↗

PubMedDate not supplied

MR diffusion tensor imaging and fiber tracking in spinal cord compression.

BACKGROUND AND PURPOSE: Spinal cord damage can result in major functional disability. Alteration of the spinal cord structural integrity can be assessed by using diffusion tensor imaging methods. Our objective is to evaluate the diagnostic accuracy of apparent diffusion coefficient (ADC), fractional anisotropy (FA), and fiber tracking in both acute and slowly progressive spinal cord compressions. METHODS: Fifteen patients with clinical symptoms of acute (n = 2) or slowly progressive (n = 13) spinal cord compression and 11 healthy volunteers were prospectively selected. We performed T2-weighted fast spin echo (FSE) and diffusion tensor imaging by using a 1.5-T MR scanner. ADC and FA maps were computed. Regions of interest were placed at the cervical, upper and lower thoracic cord levels for the healthy subjects and on the area with abnormal T2-weighted signal intensity in the patients with cord compression. In three patients, we used fiber tracking to locate the areas of cord compression precisely. Data were analyzed by using a mixed model. The sensitivity (SE) and specificity (sp) of imaging (T2, ADC, and FA maps) in the detection of spinal cord abnormality were statistically evaluated. RESULTS: For the healthy subjects, averaged ADC values ranged from 0.96 10(-3) mm(2)/s to 1.05 10(-3) mm(2)/s and averaged FA values ranged from 0.745 to 0.751. Ten patients had decreased FA (0.67 +/- 0.087), and one had increased FA values (0.831); only two patients had increased ADC values (1.03 +/- 0.177). There was a statistically significant difference in the FA values between volunteers and patients (P = .012). FA had a much higher sensitivity (SE = 73.3%) and specificity (sp = 100%) in spinal cord abnormalities detection compared with T2-weighted FSE imaging (se = 46.7%, sp = 100%) and ADC (SE = 13.4%, sp = 80%). CONCLUSIONS: FA has the highest sensitivity and specificity in the detection of acute spinal cord abnormalities. Spinal cord fiber tracking is a useful tool to focus measurements on the compressed spinal cord.

Acute Disease↗