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

Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error

This work presents a new algorithm (nonuniform intensity correction; NIC) for correction of intensity inhomogeneities in T1-weighted magnetic resonance (MR) images. The bias field and a bias-free image are obtained through an iterative process that uses brain tissue segmentation. The algorithm was validated by means of realistic phantom images and a set of 24 real images. The first evaluation phase was based on a public domain phantom dataset, used previously to assess bias field correction algorithms. NIC performed similar to previously described methods in removing the bias field from phantom images, without introduction of degradation in the absence of intensity inhomogeneity. The real image dataset was used to compare the performance of this new algorithm to that of other widely used methods (N3, SPM'99, and SPM2). This dataset included both low and high bias field images from two different MR scanners of low (0.5 T) and medium (1.5 T) static fields. Using standard quality criteria for determining the goodness of the different methods, NIC achieved the best results, correcting the images of the real MR dataset, enabling its systematic use in images from both low and medium static field MR scanners. A limitation of our method is that it might fail if the bias field is so high that the initial histogram does not show bimodal distribution for white and gray matterPublicad

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Efficient automatic correction and segmentation based 3D visualization of magnetic resonance images

In the recent years, the demand for automated processing techniques for digital medical image volumes has increased substantially. Existing algorithms, however, still often require manual interaction, and newly developed automated techniques are often intended for a narrow segment of processing needs. The goal of this research was to develop algorithms suitable for fast and effective correction and advanced visualization of digital MR image volumes with minimal human operator interaction. This research has resulted in a number of techniques for automated processing of MR image volumes, including a novel MR inhomogeneity correction algorithm derivative surface fitting (dsf), automatic tissue detection algorithm (atd), and a new fast technique for interactive 3D visualization of segmented volumes called gravitational shading (gs). These newly developed algorithms provided a foundation for the automated MR processing pipeline incorporated into the UniViewer medical imaging software developed in our group and available to the public. This allowed the extensive testing and evaluation of the proposed techniques. Dsf was compared with two previously published methods on 17 digital image volumes. Dsf demonstrated faster correction speeds and uniform image quality improvement in this comparison. Dsf was the only algorithm that did not remove anatomic detail. Gs was compared with the previously published algorithm fsvr and produced rendering quality improvement while preserving real-time frame-rates. These results show that the automated pipeline design principles used in this dissertation provide necessary tools for development of a fast and effective system for the automated correction and visualization of digital MR image volumes

Active contours for intensity inhomogeneous image segmentation

La “inhomogeneidad” (falta d'homogeneïtat) d'intensitat és un problema ben conegut en la segmentació d'imatges, la qual cosa afecta la precisió dels mètodes de segmentació basats en la intensitat. En aquesta tesi, es proposen mètodes de contorn actiu basat en fronteres i regions per segmentar imatges inhomogènies. En primer lloc, s'ha proposat un mètode de contorn actiu basat en fronteres mitjançant Diferència de Gaussianes (DoG), que ajuda a segmentar l'estructura global de la imatge. En segon lloc, hem proposat un mètode de contorn actiu basat en regions per corregir i segmentar imatges inhomogènies. S'ha utilitzat un nucli de transformació de fase (phase stretch transform - PST) per calcular noves intensitats mitjanes i camps de polarització, que s'empren per definir una imatge ajustada de polarització. En tercer lloc, s'ha proposat un altre mètode de contorn actiu basat en regions utilitzant un funcional d'energia basat en imatges ajustades locals i globals. El camp de polarització s'aproxima amb una distribució Gaussiana i el biaix de les regions no homogènies es corregeix dividint la imatge original pel camp aproximat de polarització. Finalment, s'ha proposat un mètode híbrid de contorns actius multifàsic (quatre fases) per dividir una imatge de RM cerebral en tres regions diferents: matèria blanca (WM), matèria grisa (GM) i líquid cefaloraquidi (CSF). En aquest treball, també s'ha dissenyat un mètode de post-processat (correcció de píxels) per millorar la precisió de les regions WM, GM i CSF segmentades. S'han utilitzat resultats experimentals tant amb imatges sintètiques com amb imatges reals de RM del cervell per a una comparació quantitativa i qualitativa amb mètodes de contorns actius de l'estat de l'art per mostrar els avantatges de les tècniques de segmentació proposades.La “inhomogeneidad” (falta de homogeneidad) de intensidad es un problema bien conocido en la segmentación de imágenes, lo que afecta la precisión de los métodos de segmentación basados en la intensidad. En esta tesis, se proponen métodos de contorno activo basado en bordes y regiones para segmentar imágenes inhomogéneas. En primer lugar, se ha propuesto un método de contorno activo basado en fronteras mediante Diferencia de Gaussianas (DoG), que ayuda a segmentar la estructura global de la imagen. En segundo lugar, hemos propuesto un método de contorno activo basado en regiones para corregir y segmentar imágenes inhomogéneas. Se ha utilizado un núcleo de transformación de fase (phase stretch transform - PST) para calcular nuevas intensidades medias y campos de polarización, que se emplean para definir una imagen ajustada de polarización. En tercer lugar, se ha propuesto otro método de contorno activo basado en regiones utilizando un funcional de energía basado en imágenes ajustadas locales y globales. El campo de polarización se aproxima con una distribución Gaussiana y el sesgo de las regiones no homogéneas se corrige dividiendo la imagen original por el campo aproximado de polarización. Finalmente, se ha propuesto un método híbrido de contornos activos multifásico (cuatro fases) para dividir una imagen de RM cerebral en tres regiones distintas: materia blanca (WM), materia gris (GM) y líquido cefalorraquídeo (CSF). En este trabajo, también se ha diseñado un método de post-procesado (corrección de píxeles) para mejorar la precisión de las regiones WM, GM y CSF segmentadas. Se han utilizado resultados experimentales tanto con imágenes sintéticas como con imágenes reales de RM del cerebro para una comparación cuantitativa y cualitativa con métodos de contornos activos del estado del arte para mostrar las ventajas de las técnicas de segmentación propuestas.Intensity inhomogeneity is a well-known problem in image segmentation, which affects the accuracy of intensity-based segmentation methods. In this thesis, edge-based and region-based active contour methods are proposed to segment intensity inhomogeneous images. Firstly, we have proposed an edge-based active contour method based on the Difference of Gaussians (DoG), which helps to segment the global structure of the image. Secondly, we have proposed a region-based active contour method to both correct and segment intensity inhomogeneous images. A phase stretch transform (PST) kernel has been used to compute new intensity means and bias field, which are employed to define a bias fitted image. Thirdly, another region-based active contour method has been proposed using an energy functional based on local and global fitted images. Bias field is approximated with a Gaussian distribution and the bias of intensity inhomogeneous regions is corrected by dividing the original image by the approximated bias field. Finally, a hybrid region-based multiphase (four-phase) active contours method has been proposed to partition a brain MR image into three distinct regions: white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). In this work, a post-processing (pixel correction) method has also been devised to improve the accuracy of the segmented WM, GM and CSF regions. Experimental results with both synthetic and real brain MR images have been used for a quantitative and qualitative comparison with state-of-the-art active contour methods to show the advantages of the proposed segmentation techniques

Inhomogeneity Correction in High Field Magnetic Resonance Images

Projecte realitzat en col.laboració amb el centre Swiss Federal Institute of Technology (EPFL)Magnetic Resonance Imaging, MRI, is one of the most powerful and harmless ways to study human inner tissues. It gives the chance of having an accurate insight into the physiological condition of the human body, and specially, the brain. Following this aim, in the last decade MRI has moved to ever higher magnetic field strength that allow us to get advantage of a better signal-to-noise ratio. This improvement of the SNR, which increases almost linearly with the field strength, has several advantages: higher spatial resolution and/or faster imaging, greater spectral dispersion, as well as an enhanced sensitivity to magnetic susceptibility. However, at high magnetic resonance imaging, the interactions between the RF pulse and the high permittivity samples, which causes the so called Intensity Inhomogeneity or B1 inhomogeneity, can no longer be negligible. This inhomogeneity causes undesired efects that afects quantitatively image analysis and avoid the application classical intensity-based segmentation and other medical functions. In this Master thesis, a new method for Intensity Inhomogeneity correction at high ¯eld is presented. At high ¯eld is not possible to achieve the estimation and the correction directly from the corrupted data. Thus, this method attempt the correction by acquiring extra information during the image process, the RF map. The method estimates the inhomogeneity by the comparison of both acquisitions. The results are compared to other methods, the PABIC and the Low-Pass Filter which try to correct the inhomogeneity directly from the corrupted data