Volumetric segmentation of multiple basal ganglia structures

We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities based on training data, we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy

Estimation of the rigid-body motion from three-dimensional images using a generalized center-of-mass points approach

We present an analytical method for the estimation of rigid-body motion in sets of three-dimensional (3-D) SPECT and PET slices. This method utilizes mathematically defined generalized center-of-mass points in images, requiring no segmentation. It can be applied to compensation of the rigid-body motion in both SPECT and PET, once a series of 3-D tomographic images are available. We generalized the formula for the center-of-mass to obtain a family of points comoving with the object\u27s rigid-body motion. From the family of possible points we chose the best three points which resulted in the minimum root-mean-square difference between images as the generalized center-of-mass points for use in estimating motion. The estimated motion was used to sum the sets of tomographic images, or incorporated in the iterative reconstruction to correct for motion during reconstruction of the combined projection data. For comparison, the principle-axes method was also applied to estimate the rigid-body motion from the same tomographic images. To evaluate our method for different noise levels, we performed simulations with the MCAT phantom. We observed that though noise degraded the motion-detection accuracy, our method helped in reducing the motion artifact both visually and quantitatively. We also acquired four sets of the emission and transmission data of the Data Spectrum Anthropomorphic Phantom positioned at four different locations and/or orientations. From these we generated a composite acquisition simulating periodic phantom movements during acquisition. The simulated motion was calculated from the generalized center-of-mass points calculated from the tomographic images reconstructed from individual acquisitions. We determined that motion-compensation greatly reduced the motion artifact. Finally, in a simulation with the gated MCAT phantom, an exaggerated rigid-body motion was applied to the end-systolic frame. The motion was estimated from the end-diastolic and end-systolic images, and used to sum them into a summed image without obvious artifact. Compared to the principle-axes method, in two of the three comparisons with anthropomorphic phantom data our method estimated the motion in closer agreement to the Polaris system than the principal-axes method, while the principle-axes method gave a more accurate estimation of motion in most cases for the MCAT simulations. As an image-driven approach, our method assumes angularly com plete data sets for each state of motion. We expert this method to be applied in correction of respiratory motion in respiratory gated SPECT, and respiratory or other rigid-body motion in PET. © 2006 IEEE

Construction of boundary element models in bioelectromagnetism

Multisensor electro- and magnetoencephalographic (EEG and MEG) as well as electro- and magnetocardiographic (ECG and MCG) recordings have been proved useful in noninvasively extracting information on bioelectric excitation. The anatomy of the patient needs to be taken into account, when excitation sites are localized by solving the inverse problem. In this work, a methodology has been developed to construct patient specific boundary element models for bioelectromagnetic inverse problems from magnetic resonance (MR) data volumes as well as from two orthogonal X-ray projections. The process consists of three main steps: reconstruction of 3-D geometry, triangulation of reconstructed geometry, and registration of the model with a bioelectromagnetic measurement system. The 3-D geometry is reconstructed from MR data by matching a 3-D deformable boundary element template to images. The deformation is accomplished as an energy minimization process consisting of image and model based terms. The robustness of the matching is improved by multi-resolution and global-to-local approaches as well as using oriented distance maps. A boundary element template is also used when 3-D geometry is reconstructed from X-ray projections. The deformation is first accomplished in 2-D for the contours of simulated, built from the template, and real X-ray projections. The produced 2-D vector field is back-projected and interpolated on the 3-D template surface. A marching cube triangulation is computed for the reconstructed 3-D geometry. Thereafter, a non-iterative mesh-simplification method is applied. The method is based on the Voronoi-Delaunay duality on a 3-D surface with discrete distance measures. Finally, the triangulated surfaces are registered with a bioelectromagnetic measurement utilizing markers. More than fifty boundary element models have been successfully constructed from MR images using the methods developed in this work. A simulation demonstrated the feasibility of X-ray reconstruction; some practical problems of X-ray imaging need to be solved to begin tests with real data.reviewe

Feature-Based Models for Three-Dimensional Data Fitting.

There are numerous techniques available for fitting a surface to any supplied data set. The feature-based modeling technique takes advantage of the known, geometric shape of the data by deforming a model having this generic shape to approximate the data. The model is constructed as a rational B-spline surface with characteristic features superimposed on its definition. The first step in the fitting process is to align the model with a data set using the center of mass, principal axes and/or landmarks. Using this initial orientation, the position, rotation and scale parameters are optimized using a Newton-type optimization of a least squares cost function. Once aligned, features embedded within the model, corresponding to pertinent characteristics of the shape, are used to improve the fit of the model to the data. Finally, the control vertex weights and positions of the rational B-spline model are optimized to approximate the data to within a specified tolerance. Since the characteristic features are defined within the model a creation, important measures are easily extracted from a data set, once fit. The feature-based modeling approach is demonstrated in two-dimensions by the fitting of five facial, silhouette profiles and in three-dimensions by the fitting of eleven human foot scans. The algorithm is tested for sensitivity to data distribution and structure and the extracted measures are tested for repeatability and accuracy. Limitations within the current implementation, future work and potential applications are also provided

Development and characterization of methodology and technology for the alignment of fMRI time series

This dissertation has developed, implemented and tested a novel computer based system (AUTOALIGN) that incorporates an algorithm for the alignment of functional Magnetic Resonance Image (fMRI) time series. The algorithm assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lie on the directions correspondent to two of the eigenvectors of inertia of the volume, at the intersections with the head boundary. The eigenvectors are found weighting the inertia components with the voxel\u27s intensity values assumed as mass. The three reference points are found in the same position, relative to the origin of the head coordinate system, in both test and reference brain images. Intensity correction is performed at sub-voxel accuracy by tri-linear interpolation. A test fMR brain volume in which controlled simulations of rigid-body transformations have been introduced has preliminarily assessed system performance. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations have been retrieved automatically and the values of the motion parameters compared to those obtained by the Statistical Parametric Mapping (SPM99), and the Automatic Image Registration (AIR 3.08). Results indicated that AUTOALIGN offers subvoxel accuracy in correcting both misalignment and intensity among time points in fMR images time series, and also that its performance is comparable to that of SPM99 and AIR3.08

Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections

Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE

Metodi automatici di analisi e caratterizzazione di esami radiologici del massiccio facciale.

L'obiettivo di questo lavoro è lo sviluppo di algoritmi e procedure di analisi di referti radiografici digitali di tipo CBCT delle strutture della mandibola e dell’apparato dentario. In particolare, mediante un’opportuna campagna di sperimentazione, in collaborazione con i reparti di radiologia ed odontoiatria del Policlinico di Palermo, è stata realizzata un procedura in grado di: • eliminare i problemi di sovrapponibilità dei referti tridimensionali effettuati in tempi successivi; • identificare lo spazio parodontale su indagini CBCT per la valutazione dei possibili difetti nello stesso e prevedere l’insorgenza di parodontiti. • individuare gli elementi di maggiore interesse medico caratterizzanti l'anatomia dell'arcata mandibolare quali linee inferiori, linea media ed elementi dentari. La prima parte del lavoro ha affrontato le problematiche riguardanti la sovrapposizione di esami effettuati in tempi differenti dello stesso soggetto e quindi la necessità di risolvere le problematiche relative agli scostamenti tra le due immagini, dovuti all’impossibilità di riposizionare il paziente nella posizione esatta e alle differenze di intensità luminosa dovute alle caratteristiche degli strumenti di acquisizione e alle differenze di posizionamento del paziente. Successivamente sono state sviluppate procedure di analisi delle immagini CBCT per l'identificazione degli elementi dentari e l'analisi del tessuto parodontale, cioè l’insieme delle strutture che circondano il dente e lo mantengono saldamente attaccato all’osso, per identificare dei difetti ossei di interesse chirurgico. Infine l'attività si è concentrata sul riconoscimento semi automatico degli elementi caratterizzanti l'anatomia dell'arcata mandibolare, consentendo un analisi personalizzata dei dati che al contempo faciliti le attività di diagnosi, prognosi e terapia in ambito odontoiatrico. Nel Capitolo 1 viene descritta la struttura della tesi. Nel Capitolo 2 vengono affrontate le tematiche relative alla sovrapposizione di esami effettuati in tempi successivi e vengono descritte le due procedure sviluppate ai fini delle sovrapposizione. La prima procedura è costituita da due fasi principali; nella prima si effettua una sovrapposizione approssimata ruotando le due immagine 3D in modo da sovrapporre gli assi d'inerzia delle strutture ossee agli assi di riferimento e nella seconda si applica la tecnica Digital Image Correlation (DIC) per eliminare i piccoli scostamenti residui tra le due immagini. Anche la seconda procedura è costituita da una fase di sovrapposizione approssimata (differente dalla prima) e da una seconda fase in cui si applica la tecnica DIC. La fase di sovrapposizione approssimata è effettuata determinando le linee inferiori dell'osso mandibolare di entrambe le immagini e determinando opportuni parametri di traslazione e rotazione per posizionare in modo simmetrico rispetto agli assi di riferimento entrambe le immagini. Nel Capitolo 3 viene descritta la tecnica di elaborazione implementata per identificare lo spazio parodontale su indagini CBCT per la valutazione dei possibili difetti nello stesso e prevedere l’insorgenza di parodontiti. Nel Capitolo 4 sono descritte le procedure sviluppate per l'individuazione semi automatica degli elementi di maggiore interesse in campo odontoiatrico ai fini della caratterizzazione dell'anatomia dell'arcata mandibolare del paziente. Gli elementi determinati dalle procedure sono: • insieme mandibola ed elementi dentari; • elementi dentari; • linea media della sezione della mandibola di maggiore dimensione. Infine, nelle Conclusioni, vengono discussi i risultati ottenuti e i possibili sviluppi futuri