Catálogo de publicaciones - libros

Osteoarthritis is characterized by the degeneration of the articular cartilage in joints. We have developed a fully automatic method for segmenting the articular cartilage in knee MR scans based on supervised learning. A binary approximate kNN classifier first roughly separates cartilage from background voxels, then a three-class classifier assigns one of three classes to each voxel that is classified as cartilage by the binary classifier. The resulting sensitivity and specificity are 90.0% and 99.8% respectively for the medial cartilage compartments. We show that an accurate automatic cartilage segmentation is achievable using a low-field MR scanner.

We present a fully automatic 3D segmentation method for the left ventricle (LV) in human myocardial perfusion SPECT data. This model-based approach consists of 3 phases: 1. finding the LV in the dataset, 2. extracting its approximate shape and 3. segmenting its exact contour.

Finding of the LV is done by flexible pattern matching, whereas segmentation is achieved by registering an anatomical model to the functional data. This model is a new kind of stable 3D mass spring model using direction-weighted 3D contour sensors.

Our approach is much faster than manual segmention, which is standard in this application up to now. By testing it on 41 LV SPECT datasets of mostly pathological data, we could show, that it is very robust and its results are comparable with those made by human experts.

Vulnerable plaques are dangerous atherosclerotic lesions that bear a high risk of complications that can lead to heart attacks and strokes. These plaques are known to be chronically inflamed. The vasa vasorum (VV) are microvessels that nourish vessel walls. Proliferation of VV is part of the “response to injury” phenomenon in the process of plaque formation. Recent evidence has shown strong correlations between neovessel formation and macrophage infiltration in atherosclerotic plaque, suggesting VV density as a surrogate marker of plaque inflammation and vulnerability. We have developed a novel method for imaging and analyzing the density and perfusion of VV in human coronary atherosclerotic plaques using intravascular ultrasound (IVUS). Images are taken during the injection of a microbubble contrast agent and the spatiotemporal changes of the IVUS signal are monitored using enhancement-detection techniques. We present analyses of human coronary cases that, for the first time, demonstrate the feasibility of IVUS imaging of VV.

Analyses of fMRI brain data are often based on statistical tests applied to each voxel or use summary statistics within a region of interest (such as mean or peak activation). These approaches do not explicitly take into account spatial patterns in the activation signal. In this paper, we develop a response surface model with parameters that directly describe the spatial shapes of activation patterns. We present a stochastic search algorithm for parameter estimation. We apply our method to data from a multi-site fMRI study, and show how the estimated parameters can be used to analyze different sources of variability in image generation, both qualitatively and quantitatively, based on spatial activation patterns.

Understanding of the dynamic behaviour of the levator ani is important to the assessment of pelvic floor dysfunction. Whilst shape modelling allows the depiction of 3D morphological variation of the levator ani between different patient groups, it is insufficient to determine the underlying behaviour of how the muscle deforms during contraction and strain. The purpose of this study is to perform a subject specific finite element analysis of the levator ani with open access magnetic resonance imaging. The method is based on a Mooney-Rivlin hyperelastic model and permits dynamic study of subjects under natural physiological loadings. The value of the proposed modelling framework is demonstrated with dynamic 3D data from nulliparous, female subjects.

A detailed visualization of the dental occlusion in 3D image-based planning environments for oral and maxillofacial planning is important. With CT imaging however, this occlusion is often deteriorated by streak artifacts caused by amalgam fillings. Moreover, more detailed surface information at the level of the dental cuspids is often desired.

In this paper, a double scan technique is introduced to image the dental occlusion by means of a newly designed 3D splint. The patient wears this splint between the upper and lower teeth during CT-scan. In a second step, the splint is positioned between the plaster casts of the upper and lower jaw, and this setup is scanned. Based on markers in the 3D splint, both data sets are fused and a combined visualization is possible. The accuracy, robustness and applicability in clinical routine is shown.

This technology enables meticulous 3D cephalometric analysis, detailed maxillofacial planning and opens possibilities towards intraoperative support.

Focal cortical dysplasia (FCD), a malformation of cortical development, is an important cause of medically intractable epilepsy. FCD lesions are difficult to distinguish from non-lesional cortex and their delineation on MRI is a challenging task. This paper presents a method to segment FCD lesions on T1-weighted MRI, based on a 3D deformable model, implemented using the level set framework. The deformable model is driven by three MRI features: cortical thickness, relative intensity and gradient. These features correspond to the visual characteristics of FCD and allow to differentiate lesions from normal tissues. The proposed method was tested on 18 patients with FCD and its performance was quantitatively evaluated by comparison with the manual tracings of two trained raters. The validation showed that the similarity between the level set segmentation and the manual labels is similar to the agreement between the two human raters. This new approach may become a useful tool for the presurgical evaluation of patients with intractable epilepsy.

A publicly available database of high-quality, multi-modal MR brain images of carefully screened healthy subjects, equally divided by sex, and with an equal number of subjects per age decade, would be of high value to investigators interested in the statistical study of disease. This report describes initial use of an accumulating healthy database currently comprising 50 subjects aged 20-72. We examine changes by age and sex to the volumes of gray matter, white matter and cerebrospinal fluid for subjects within the database. We conclude that traditional views of healthy aging should be revised. Significant atrophy does not appear in healthy subjects 60 or 70 years old. Gray matter loss is not restricted to senility, but begins in early adulthood and is progressive. The percentage of white matter increases with age. A carefully-designed healthy database should be useful in the statistical analysis of many age- and non-age- related diseases.

The ability to predict a clinical variable from automated analysis of single, cross-sectional T1-weighted (T1w) MR scans stands to improve the management of patients with neurological diseases. We present a methodology for predicting yearly Mini-Mental Score Examination (MMSE) changes in Mild Cognitive Impairment (MCI) patients. We begin by generating a non-pathological, multidimensional reference space from a group of 152 healthy volunteers by Principal Component Analyses of (i) T1w MR intensity of linearly registered Volumes of Interest (VOI); and (ii) trace of the deformation fields of nonlinearly registered VOIs. We use multiple regression to build linear models from eigenvectors where the projection eigencoordinates of patient data in the reference space are highly correlated with the clinical variable of interest. In our cohort of 47 MCI patients, composed of 16 decliners, 26 stable and 5 improvers (based on MMSE at 1 yr follow-up), there was a significant difference ( = 0.0003) for baseline MMSE scores between decliners and improvers, but no other differences based on age or sex. First, we classified our three groups using leave-one-out, forward stepwise linear discriminant analyses of the projection eigencoordinates with 100% accuracy. Next, we compared various linear models by computing F-statistics on the residuals of predicted actual values. The best model was based on 10 eigenvectors + baseline MMSE, with predicted yearly changes highly correlated ( = 0.6955) with actual data. Prospective study of an independent cohort of patients is the next logical step towards establishing this promising technique for clinical use.

Motivated by the need for methods to aid the deformable registration of brain tumor images, we present a three-dimensional (3D) mechanical model for simulating large non-linear deformations induced by tumors to the surrounding encephalic tissues. The model is initialized with 3D radiological images and is implemented using the finite element (FE) method. To simulate the widely varying behavior of brain tumors, the model is controlled by a number of parameters that are related to variables such as the bulk tumor location, size, mass-effect, and peri-tumor edema extent. Model predictions are compared to real brain tumor-induced deformations observed in serial-time MRI scans of a human subject and 3 canines with surgically transplanted gliomas. Results indicate that the model can reproduce the real deformations with an accuracy that is similar to that of manual placement of landmark points to which the model is compared.