Catálogo de publicaciones - libros

In Diffusion Tensor Imaging (DTI), differently oriented fiber bundles inside one voxel are incorrectly modeled by a single tensor. High Angular Resolution Diffusion Imaging (HARDI) aims at using more complex models, such as a two-tensor model, for estimating two fiber bundles.

We propose a new method for creating experimental phantom data of fiber crossings, by mixing the DWI-signals from high FA-regions with different orientation. The properties of these experimental phantoms approach the conditions of real data. These phantoms can thus serve as a ‘ground truth’ in validating crossing reconstruction algorithms. The angular resolution of a dual tensor model is determined using series of crossings, generated under different angles. An angular resolution of 0.6 was found in data scanned with a diffusion weighting parameter =1000 s/mm. This resolution did not change significantly in experiments with =3000 and 5000 s/mm, keeping the scanning time constant.

CT-images acquired by mobile C-arm devices can contain artefacts caused by positioning errors. We propose a data driven method based on iterative 3D-reconstruction and 2D/3D-registration to correct projection data inconsistencies. With a 2D/3D-registration algorithm, transformations are computed to align the acquired projection images to a previously reconstructed volume. In an iterative procedure, the reconstruction algorithm uses the results of the registration step. This algorithm also reduces small motion artefacts within 3D-reconstructions. Experiments with simulated projections from real patient data show the feasibility of the proposed method. In addition, experiments with real projection data acquired with an experimental robotised C-arm device have been performed with promising results.

We present large deformation diffeomorphic metric curve mapping (LDDMM-Curve) for registering cortical hemispheres. We showed global cortical hemisphere matching and evaluated the mapping accuracy in five subregions of the cortex in fourteen MRI scans.

We present a novel system for 3-D visualisation of the heart and coronary arteries. Binary tags (generated offline) are combined with value-gradient transfer functions (specified online) allowing for interactive visualisation, while relaxing the offline segmentation criteria. The arteries are roughly segmented using a Hessian-based line filter and the pericardial cavity using a Fast Marching active contour. A comparison of different contour initialisations reveals that simple geometric shapes (such as spheres or extruded polygons) produce suitable results.

Intravascular ultrasound (IVUS) is an invasive imaging modality capable of providing cross-sectional images of the interior of a blood vessel in real time and at normal video framerates (10-30 frames/s). Low contrast between the features of interest in the IVUS imagery remains a confounding factor in IVUS analysis; it would be beneficial therefore to have a method capable of detecting certain physical features imaged under IVUS in an automated manner. We present such a method and apply it to the detection of blood. While blood detection algorithms are not new in this field, we deviate from traditional approaches to IVUS signal characterization in our use of 1-class learning. This eliminates certain problems surrounding the need to provide “foreground” and “background” (or, more generally, -class) samples to a learner. Applied to the blood-detection problem on 40 MHz recordings made in swine, we are able to achieve ~95% sensitivity with ~90% specificity at a radial resolution of ~600 m.

This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.

We propose to carry out cooperatively both tissue and structure segmentations by distributing a set of and models in a unified MRF framework. Tissue segmentation is performed by partitionning the volume into subvolumes where local MRFs are estimated in cooperation with their neighbors to ensure consistency. Local estimation fits precisely to the local intensity distribution and thus handles nonuniformity of intensity without any bias field modelization. Structure segmentation is performed via local MRFs that integrate localization constraints provided by general fuzzy description of brain anatomy. Structure segmentation is not reduced to a postprocessing step but cooperates with tissue segmentation to gradually and conjointly improve models accuracy. The evaluation was performed using phantoms and real 3T brain scans. It shows good results and in particular robustness to nonuniformity and noise with a low computational cost.

With the advent of biomarkers such as C-PIB and the increase in use of PET, automated methods are required for processing and analyzing datasets from research studies and in clinical settings. A common preprocessing step is the calculation of standardized uptake value ratio (SUVR) for inter-subject normalization. This requires segmented grey matter (GM) for VOI refinement. However C-PIB uptake is proportional to amyloid build up leading to inhomogeneities in intensities, especially within GM. Inhomogeneities present a challenge for clustering and pattern classification based approaches to PET segmentation as proposed in current literature.

In this paper we modify a MR image segmentation technique based on expectation maximization for C-PIB PET segmentation. A priori probability maps of the tissue types are used to initialize and enforce anatomical constraints. We developed a Bézier spline based inhomogeneity correction techniques that is embedded in the segmentation algorithm and minimizes inhomogeneity resulting in better segmentations of C-PIB PET images. We compare our inhomogeneity with a global polynomial correction technique and validate our approach using co-registered MRI segmentations.

A mapping of unit vectors onto a 5D hypersphere is used to model and partition ODFs from HARDI data. This mapping has a number of useful and interesting properties and we make a link to interpretation of the second order spherical harmonic decompositions of HARDI data. The paper presents the working theory and experiments of using a von Mises-Fisher mixture model for directional samples. The MLE of the second moment of the HvMF pdf can also be related to fractional anisotropy. We perform error analysis of the estimation scheme in single and multi-fibre regions and then show how a penalised-likelihood model selection method can be employed to differentiate single and multiple fibre regions.

An optimal 3-D graph search approach designed for simultaneous multiple surface detection is extended to allow for varying smoothness and surface interaction constraints instead of the traditionally used constant constraints. We apply the method to the intraretinal layer segmentation of 24 3-D optical coherence tomography (OCT) images, learning the constraints from examples in a leave-one-subject-out fashion. Introducing the varying constraints decreased the mean unsigned border positioning errors (mean error of 7.3 ± 3.7 m using varying constraints compared to 8.3 ± 4.9 m using constant constraints and 8.2 ± 3.5 m for the inter-observer variability).