Dual Energy Computed Tomography (DECT) is an emerging technique that offers new possibilities to determine composition of tissues in clinical applications. Accurate knowledge of tissue composition is important for instance for brachytherapy (BT) treatment planning. However, the accuracy of CT numbers measured with contemporary clinical CT scanners is relatively low since CT numbers are affected by image artifacts. The aim of this work was to estimate the accuracy of CT numbers measured with the Siemens SOMATOM Definition Flash DECT scanner and the accuracy of the resulting volume or mass fractions calculated via the three material decomposition method. CT numbers of water, gelatin and a 3rd component (salt, hydroxyapatite or protein powder) mixtures were measured using Siemens SOMATOM Definition Flash DECT scanner. The accuracy of CT numbers was determined by (i) a comparison with theoretical (true) values and (ii) using different measurement conditions (configurations) and assessing the resulting variations in CT numbers. The accuracy of mass fractions determined via the three material decomposition method was estimated by a comparison with mass fractions measured with calibrated scales. The latter method was assumed to provide highly accurate results. It was found that (i) axial scanning biased CT numbers for some detector rows. (ii) large volume of air surrounding the measured region shifted CT numbers compared to a configuration where the region was surrounded by water. (iii) highly attenuating object shifted CT numbers of surrounding voxels. (iv) some image kernels caused overshooting and undershooting of CT numbers close to edges. The three material decomposition method produced mass fractions differing from true values by 8% and 15% for the salt and hydroxyapatite mixtures respectively. In this case, the analyzed CT numbers were averaged over a volumetric region. For individual voxels, the volume fractions were affected by statistical noise. The method failed when statistical noise was high or CT numbers of the decomposition triplet were similar. Contemporary clinical DECT scanners produced image artifacts that strongly affected the accuracy of the three material decomposition method; the Siemens’ image reconstruction algorithm is not well suited for quantitative CT. The three material decomposition method worked relatively well for averages of CT numbers taken from volumetric regions as these averages lowered statistical noise in the analyzed data.

Brain atrophy measurement is increasingly important in studies of neurodegenerative diseases such as Alzheimer’s disease. From this perspective, a regional segmentation framework for magnetic resonance images has recently been developed by the team that I joined for my master thesis. It combines an atlas fusion and a tissue classification. A graph-cuts optimization step is then applied to obtain the final segmentation from the combination probability maps. To begin with neighboring constraints were integrated into the optimization step so as to prevent some labels to be adjacent in accordance with anatomical criteria. They were successfully tested on a restricted list of patient images which previously presented segmentation errors. Secondly, a multigrid tissue classification was implemented in order to compensate for the effects of intensity inhomogeneities. However, the visual observations on a few cases showed little improvement compared to the increased computation time. Consequently another possibility was investigated to modify the classification. An atlas-based classification was implemented and tested both on a small-scale and a large-scale. The efficiency of the proposed method was visually assessed on a few patients, especially regarding the separation between grey and white matter. The process was then applied on a database containing several hundreds patients and the results demonstrated an improved group separation based on grey matter volume, whose reduction is particularly significant with patients suffering from Alzheimer’s Disease. To conclude, several links of the segmentation framework have been upgraded, which promises good results for future regional atrophy studies.

This work explores if combining information from multiple Magnetic Resonance Imaging (MRI) modalities provides improved interpretation of brain biological architecture as each MR modality can reveal different characteristics of underlying anatomical structures. Structural MRI provides a means for high-resolution quantitative study of brain morphometry. Diffusion-weighted MR imaging (DWI) allows for low-resolution modeling of diffusivity properties of water molecules. Structural and diffusion-weighted MRI modalities are commonly used for monitoring the biological architecture of the brain in normal development or neurodegenerative disease processes. Structural MRI provides an overall map of brain tissue organization that is useful for identifying distinct anatomical boundaries that define gross organization of the brain. DWI models provide a reflection of the micro-structure of white matter (WM), thereby providing insightful information for measuring localized tissue properties or for generating maps of brain connectivity. Multispectral information from different structural MR modalities can lead to better delineation of anatomical boundaries, but careful considerations should be taken to deal with increased partial volume effects (PVE) when input modalities are provided in different spatial resolutions. Interpretation of diffusion-weighted MRI is strongly limited by its relatively low spatial resolution. PVE's are an inherent consequence of the limited spatial resolution in low-resolution images like DWI. This work develops novel methods to enhance tissue classification by addressing challenges of partial volume effects encountered from multi-modal data that are provided in different spatial resolutions. Additionally, this project addresses PVE in low-resolution DWI scans by introducing a novel super-resolution reconstruction approach that uses prior information from multi-modal structural MR images provided in higher spatial resolution. The major contributions of this work include: 1) Enhancing multi-modal tissue classification by addressing increased PVE when multispectral information come from different spatial resolutions. A novel method was introduced to find pure spatial samples that are not affected by partial volume composition. Once detecting pure samples, we can safely integrate multi-modal information in training/initialization of the classifier for an enhanced segmentation quality. Our method operates in physical spatial domain and is not limited by the constraints of voxel lattice spaces of different input modalities. 2) Enhancing the spatial resolution of DWI scans by introducing a novel method for super-resolution reconstruction of diffusion-weighted imaging data using high biological-resolution information provided by structural MRI data such that the voxel values at tissue boundaries of the reconstructed DWI image will be in agreement with the actual anatomical definitions of morphological data. We used 2D phantom data and 3D simulated multi-modal MR scans for quantitative evaluation of introduced tissue classification approach. The phantom study result demonstrates that the segmentation error rate is reduced when training samples were selected only from the pure samples. Quantitative results using Dice index from 3D simulated MR scans proves that the multi-modal segmentation quality with low-resolution second modality can approach the accuracy of high-resolution multi-modal segmentation when pure samples are incorporated in the training of classifier. We used high-resolution DWI from Human Connectome Project (HCP) as a gold standard for super-resolution reconstruction evaluation to measure the effectiveness of our method to recover high-resolution extrapolations from low-resolution DWI data using three evaluation approaches consisting of brain tractography, rotationally invariant scalars and tensor properties. Our validation demonstrates a significant improvement in the performance of developed approach in providing accurate assessment of brain connectivity and recovering the high-resolution rotationally invariant scalars (RIS) and tensor property measurements when our approach was compared with two common methods in the literature. The novel methods of this work provide important improvements in tools that assist with improving interpretation of brain biological architecture. We demonstrate an increased sensitivity for volumetric and diffusion measures commonly used in clinical trials to advance our understanding of both normal development and disease induced degeneration. The improved sensitivity may lead to a substantial decrease in the necessary sample size required to demonstrate statistical significance and thereby may reduce the cost of future studies or may allow more clinical and observational trials to be performed in parallel.

Quantifying tissue volumes in pediatric brains from magnetic resonance (MR) images can provide insight into etiology and onset of neurological disease. Unbiased volumetric analysis can be applied to large population studies when automated image processing is possible. Standard segmentation strategies using adult atlases fail to account for varying tissue contrasts and types associated with the rapid growth and maturational changes seen in early neurodevelopment. The goal of this project was to develop an automated pipeline and two age-specific atlases capable of providing accurate tissue classification despite these challenges. The automated pipeline consisted of a stepwise initial atlas-to-subject registration, expectation maximization (EM) atlas based segmentation, and a post-processing level set segmentation for improved white/gray matter separation. This level set segmentation is a 3D and multiphase adaptation of a 2D method intended for use on images with the types of intensity Inhomogeneities found in MR images. The initial tissue maps required to determine spatial priors for the one-year-old atlas were created by manually cleaning the results of an adult atlas and the automated pipeline. Additional tissue maps were incrementally added until the spatial priors were sufficiently representative. The neonate atlas was similarly created, starting with the one-year-old atlas.

Registration of a group of images generally only gives a pointwise, dense correspondence defined over the whole image plane or volume, without having any specific description of any common structure that exists in every image. Furthermore, identifying tissue classes and structures that are significant across the group is often required for analysis, as well as the correspondence. The overall aim is instead to perform registration, segmentation, and modelling simultaneously, so that the registration can assist the segmentation, and vice versa. However, structural information does play a role in conventional registration, in that if the registration is successful, it would be expected structures to be aligned to some extent. Hence, we perform initial experiments to investigate whether there is explicit structural information present in the shape of the registration objective function about the optimum. We perturbed one image locally with a diffeomorphism, and found interesting structure in the shape of the quality of fit function. Then, we proceed to add explicit structural information into registration framework, using various types of structural information derived from the original intensity images. For the case of MR brain images, we augment each intensity image with its own set of tissue fraction images, plus intensity gradient images, which form an image ensemble for each example. Then, we perform groupwise registration by using these ensembles of images. We apply the method to four different real-world datasets, for which ground-truth annotation is available. It is shown that the method can give a greater than 25% improvement on the three difficult datasets, when compared to using intensity-based registration alone. On the easier dataset, it improves upon intensity-based registration, and achieves results comparable with the previous method.

It is of clinical interest to decompose a three material mixture into its constituted substances using dual-energy CT. In radiation therapy, for example material decomposition can be used to determine tissue properties for the calculation of dose in treatment planning. Due to use of polychromatic spectrum in CT, beam hardening artifacts prevent to achieve fully satisfactory results. Here an iterative reconstruction algorithm proposed by A. Malusek, M. Magnusson, M.Sandborg, and G. Alm Carlsson in 2008 is implemented to achieve this goal. The iterative algorithm can be implemented with both single- and dual-energy CT. The material decomposition process is based on mass conservation and volume conservation assumptions. The implementation and evaluation of iterative reconstruction algorithm is done by using simulation studies of analyzing mixtures of water, protein and adipose tissue. The results demonstrated that beam hardening artifacts are effectively removed and accurate estimation of mass fractions of each base material can be achieved with the proposed method. We also compared our novel iterative reconstruction algorithm to the commonly used water pre-correction method. Experimental results show that our novel iterative algorithm is more accurate.

The PhD thesis deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It is focused primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR) and microscopic images of tissues. It is easy to describe edges of the sought objects using of segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown in this thesis. Research in the area of image segmentation is focused on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. The results of the thesis are methods proposed for automatic image segmentation and classification.

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Made available in DSpace on 2016-08-17T14:53:17Z (GMT). No. of bitstreams: 1 Ulysses Santos Sousa.pdf: 1410915 bytes, checksum: 88235f7f4a3bc07a4da1b27c23dc71ca (MD5) Previous issue date: 2011-05-13 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Cancer is one of the biggest health problems worldwide, and the breast cancer is the one that causes more deaths among women. Also it is the second most frequent type in the world. The chances of survival for a patient with breast cancer increases the sooner this disease is discovered. Several Computer Aided Detection/Diagnosis Systems has been used to assist health professionals. This work presents a methodology to discriminate and classify mammographic tissues regions in mass and non-mass. For this purpose the Shannon-Wiener‟s Diversity Index, which is applied to measure the biodiversity in ecosystem, is used to describe pattern of breast image region with four approaches: global, in circles, in rings and directional. After, a Support Vector Machine is used to classify the regions in mass and non-mass. The methodology presents promising results for classification of mammographic tissues regions in mass and non-mass, achieving 99.85% maximum accuracy. / O câncer é um dos maiores problemas de saúde mundial, sendo o câncer de mama o que mais causa óbito entre as mulheres e o segundo tipo mais freqüente no mundo. As chances de uma paciente sobreviver ao câncer de mama aumentam à medida que a doença é descoberta mais cedo. Diversos Sistemas de Detecção e Diagnóstico auxiliados por computador (Computer Aided Detection/Diagnosis) têm sido utilizados para auxiliar profissionais de saúde. Este trabalho apresenta uma metodologia de discriminação e classificação de regiões de tecidos de mamografias em massa e não massa. Para este propósito utiliza-se o Índice de Diversidade de Shannon-Wiener, comumente aplicado para medir a biodiversidade em um ecossistema, para descrever padrões de regiões de imagens de mama com quatro abordagens: global, em círculos, em anéis e direcional. Em seguida, utiliza-se o classificador Support Vector Machine para classificar estas regiões em massa e não massa. A metodologia apresenta resultados promissores para a classificação de regiões de tecidos de mamografia em massa e não massa, obtendo uma acurácia máxima de 99,85%.