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

Automated Knowledge Discovery from Functional Magnetic Resonance Images using Spatial Coherence

Functional Magnetic Resonance Imaging (fMRI) has the potential to unlock many of the mysteries of the brain. Although this imaging modality is popular for brain-mapping activities, clinical applications of this technique are relatively rare. For clinical applications, classification models are more useful than the current practice of reporting loci of neural activation associated with particular disorders. Also, since the methods used to account for anatomical variations between subjects are generally imprecise, the conventional voxel-by-voxel analysis limits the types of discoveries that are possible. This work presents a classification-based framework for knowledge discovery from fMRI data. Instead of voxel-centric knowledge discovery, this framework is segment-centric, where functional segments are clumps of voxels that represent a functional unit in the brain. With simulated activation images, it is shown that this segment-based approach can be more successful for knowledge discovery than conventional voxel-based approaches. The spatial coherence principle refers to the homogeneity of behavior of spatially contiguous voxels. Auto-threshold Contrast Enhancing Iterative Clustering (ACEIC) - a new algorithm based on the spatial coherence principle is presented here for functional segmentation. With benchmark data, it is shown that the ACEIC method can achieve higher segmentation accuracy than Probabilistic Independent Component Analysis - a popular method used for fMRI data analysis. The spatial coherence principle can also be exploited for voxel-centric image-classification problems. Spatially Coherent Voxels (SCV) is a new feature selection method that uses the spatial coherence principle to eliminate features that are unlikely to be useful for classification. For a Substance Use Disorder dataset, it is demonstrated that feature selection with SCV can achieve higher classification accuracies than conventional feature selection methods

Spatial fuzzy c-mean sobel algorithm with grey wolf optimizer for MRI brain image segmentation

Segmentation is the process of dividing the original image into multiple sub regions called segments in such a way that there is no intersection between any two regions. In medical images, the segmentation is hard to obtain due to the intensity similarity among various regions and the presence of noise in medical images. One of the most popular segmentation algorithms is Spatial Fuzzy C-means (SFCM). Although this algorithm has a good performance in medical images, it suffers from two issues. The first problem is lack of a proper strategy for point initialization step, which must be performed either randomly or manually by human. The second problem of SFCM is having inaccurate segmented edges. The goal of this research is to propose a robust medical image segmentation algorithm that overcomes these weaknesses of SFCM for segmenting magnetic resonance imaging (MRI) brain images with less human intervention. First, in order to find the optimum initial points, a histogram based algorithm in conjunction with Grey Wolf Optimizer (H-GWO) is proposed. The proposed H-GWO algorithm finds the approximate initial point values by the proposed histogram based method and then by taking advantage of GWO, which is a soft computing method, the optimum initial values are found. Second, in order to enhance SFCM segmentation process and achieve higher accurate segmented edges, an edge detection algorithm called Sobel was utilized. Therefore, the proposed hybrid SFCM-Sobel algorithm first finds the edges of the original image by Sobel edge detector algorithm and finally extends the edges of SFCM segmented images to the edges that are detected by Sobel. In order to have a robust segmentation algorithm with less human intervention, the H-GWO and SFCM-Sobel segmentation algorithms are integrated to have a semi-automatic robust segmentation algorithm. The results of the proposed H-GWO algorithms show that optimum initial points are achieved and the segmented images of the SFCM-Sobel algorithm have more accurate edges as compared to recent algorithms. Overall, quantitative analysis indicates that better segmentation accuracy is obtained. Therefore, this algorithm can be utilized to capture more accurate segmented in images in the era of medical imaging

Innovative techniques to devise 3D-printed anatomical brain phantoms for morpho-functional medical imaging

Introduction. The Ph.D. thesis addresses the development of innovative techniques to create 3D-printed anatomical brain phantoms, which can be used for quantitative technical assessments on morpho-functional imaging devices, providing simulation accuracy not obtainable with currently available phantoms. 3D printing (3DP) technology is paving the way for advanced anatomical modelling in biomedical applications. Despite the potential already expressed by 3DP in this field, it is still little used for the realization of anthropomorphic phantoms of human organs with complex internal structures. Making an anthropomorphic phantom is very different from making a simple anatomical model and 3DP is still far from being plug-and-print. Hence, the need to develop ad-hoc techniques providing innovative solutions for the realization of anatomical phantoms with unique characteristics, and greater ease-of-use. Aim. The thesis explores the entire workflow (brain MRI images segmentation, 3D modelling and materialization) developed to prototype a new complex anthropomorphic brain phantom, which can simulate three brain compartments simultaneously: grey matter (GM), white matter (WM) and striatum (caudate nucleus and putamen, known to show a high uptake in nuclear medicine studies). The three separate chambers of the phantom will be filled with tissue-appropriate solutions characterized by different concentrations of radioisotope for PET/SPECT, para-/ferro-magnetic metals for MRI, and iodine for CT imaging. Methods. First, to design a 3D model of the brain phantom, it is necessary to segment MRI images and to extract an error-less STL (Standard Tessellation Language) description. Then, it is possible to materialize the prototype and test its functionality. - Image segmentation. Segmentation is one of the most critical steps in modelling. To this end, after demonstrating the proof-of-concept, a multi-parametric segmentation approach based on brain relaxometry was proposed. It includes a pre-processing step to estimate relaxation parameter maps (R1 = longitudinal relaxation rate, R2 = transverse relaxation rate, PD = proton density) from the signal intensities provided by MRI sequences of routine clinical protocols (3D-GrE T1-weighted, FLAIR and fast-T2-weighted sequences with ≤ 3 mm slice thickness). In the past, maps of R1, R2, and PD were obtained from Conventional Spin Echo (CSE) sequences, which are no longer suitable for clinical practice due to long acquisition times. Rehabilitating the multi-parametric segmentation based on relaxometry, the estimation of pseudo-relaxation maps allowed developing an innovative method for the simultaneous automatic segmentation of most of the brain structures (GM, WM, cerebrospinal fluid, thalamus, caudate nucleus, putamen, pallidus, nigra, red nucleus and dentate). This method allows the segmentation of higher resolution brain images for future brain phantom enhancements. - STL extraction. After segmentation, the 3D model of phantom is described in STL format, which represents the shapes through the approximation in manifold mesh (i.e., collection of triangles, which is continuous, without holes and with a positive – not zero – volume). For this purpose, we developed an automatic procedure to extract a single voxelized surface, tracing the anatomical interface between the phantom's compartments directly on the segmented images. Two tubes were designed for each compartment (one for filling and the other to facilitate the escape of air). The procedure automatically checks the continuity of the surface, ensuring that the 3D model could be exported in STL format, without errors, using a common image-to-STL conversion software. Threaded junctions were added to the phantom (for the hermetic closure) using a mesh processing software. The phantom's 3D model resulted correct and ready for 3DP. Prototyping. Finally, the most suitable 3DP technology is identified for the materialization. We investigated the material extrusion technology, named Fused Deposition Modeling (FDM), and the material jetting technology, named PolyJet. FDM resulted the best candidate for our purposes. It allowed materializing the phantom's hollow compartments in a single print, without having to print them in several parts to be reassembled later. FDM soluble internal support structures were completely removable after the materialization, unlike PolyJet supports. A critical aspect, which required a considerable effort to optimize the printing parameters, was the submillimetre thickness of the phantom walls, necessary to avoid distorting the imaging simulation. However, 3D printer manufacturers recommend maintaining a uniform wall thickness of at least 1 mm. The optimization of printing path made it possible to obtain strong, but not completely waterproof walls, approximately 0.5 mm thick. A sophisticated technique, based on the use of a polyvinyl-acetate solution, was developed to waterproof the internal and external phantom walls (necessary requirement for filling). A filling system was also designed to minimize the residual air bubbles, which could result in unwanted hypo-intensity (dark) areas in phantom-based imaging simulation. Discussions and conclusions. The phantom prototype was scanned trough CT and PET/CT to evaluate the realism of the brain simulation. None of the state-of-the-art brain phantoms allow such anatomical rendering of three brain compartments. Some represent only GM and WM, others only the striatum. Moreover, they typically have a poor anatomical yield, showing a reduced depth of the sulci and a not very faithful reproduction of the cerebral convolutions. The ability to simulate the three brain compartments simultaneously with greater accuracy, as well as the possibility of carrying out multimodality studies (PET/CT, PET/MRI), which represent the frontier of diagnostic imaging, give this device cutting-edge prospective characteristics. The effort to further customize 3DP technology for these applications is expected to increase significantly in the coming years

MR-based pseudo-CT generation using water-fat decomposition and Gaussian mixture regression

Tese de mestrado integrado em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2017O uso de tomografia computorizada (CT) é considerado como a prática clínica adequada para aplicações clínicas onde a simulação da atenuação de radiação pelos tecidos corporais é necessária, tais como a correcção de atenuação dos fotões em Tomografia de Emissão de Positrões (PET) e no cálculo da dosagem a ser administrada durante o planeamento de radioterapia (RTP). Imagens de ressonância magnética (MRI) têm vindo a substituir o uso de TC em algumas aplicações, sobretudo devido ao seu superior contraste entre tecidos moles e ao facto de não usar radiação ionizante. Desta forma, técnicas como PET-MRI e o planeamento de radioterapia apenas com recurso a imagens de ressonância magnética são alvo de uma crescente atenção. No entanto, estas técnicas estão limitadas pelo facto de imagens de ressonância magnética não fornecerem informação acerca da atenuação e absorção de radiação pelos tecidos. Normalmente, de forma a solucionar este problema, uma imagem de tomografia computorizada é adquirida de forma a realizar a correcção da atenuação dos fotões, assim como a dose a ser entregue em radioterapia. No entanto, esta prática introduz erros aquando do alinhamento entre as imagens de MRI e CT, que serão propagados durante todo o procedimento. Por outro lado, o uso de radiação ionizante e os custos adicionais e tempo de aquisição associado à obtenção de múltiplas modalidades de imagem limitam a aplicação clínica destas práticas. Assim, o seguimento natural prende-se com a completa substituição do uso de CT por MRI. Desta forma, o desenvolvimento de um método para a obtenção de uma imagem equivalente a CT usando MRI é necessário, sendo a imagem resultante designada de pseudo-CT. Vários métodos foram desenvolvidos de forma a construir pseudo-CT, usando métodos baseados na anatomia do paciente ou em métodos de regressão entre CT e MRI. No entanto, no primeiro caso, erros significativos são frequentes devido ao difícil alinhamento entre as imagens em casos em que a geometria do paciente é muito diferente da presente no atlas. No segundo caso, a ausência de sinal no osso cortical em MRI, torna-o indistinguível do ar. Sequências que usam um tempo de eco muito curto são normalmente utilizadas para distinguir osso cortical de ar. No entanto, para áreas com maior dimensão, como a área pélvica, dificuldades relacionadas com o equipamento e com o ruído limitam a sua aplicação nestas áreas. Por outro lado, estes métodos utilizam frequentemente diferentes imagens de MRI de forma a obter diferentes contrastes, aumentando assim o tempo de aquisição das imagens. Nesta dissertação, é proposto um método para a obtenção de um pseudo-CT baseado na combinação de um algoritmo de decomposição de água e gordura e um modelo de regressão de mistura gaussiana para a região pélvica através da aquisição de sequências de MRI convencionais. Desta forma, a aquisição de diferentes contrastes é obtida por pós-processamento das imagens originais. Desta forma, uma imagem ponderada em T1 foi adquirida com 3 tempos de eco. Um algoritmo de decomposição do sinal de ressonância magnética em sinal proveniente de água e gordura foi utilizado, permitindo a obtenção de duas imagens, cada uma representando apenas o sinal da água e gordura, respectivamente. Usando estas duas imagens, uma imagem da fracção de gordura em cada voxel foi também calculada. Por outro lado, usando o primeiro e o terceiro eco foi possível calcular o decaimento de sinal devido a efeitos relacionados com o decaimento T2*. O método para gerar o pseudo-CT baseia- se num modelo de regressão duplo entre as variáveis relacionadas com MRI e CT. Assim, o primeiro modelo aplica-se aos tecidos moles, enquanto que o segundo modelo se aplica aos tecidos ósseos. A segmentação entre estes tecidos foi realizada através da delineação manual dos tecidos ósseos. No caso do modelo de regressão para os tecidos moles, o modelo consiste numa regressão polinomial entre as imagens da fracção de gordura e os valores de CT. A ordem do polinómio usada foi obtida pela minimização do erro absoluto médio. No caso do modelo de regressão para os tecidos ósseos, um modelo de regressão de mistura gaussiana foi aplicado usando as imagens de gordura, água, de fracção de gordura e de R2*. Estas variáveis foram selecionadas, uma vez que estudos prévios correlacionam esta com a densidade mineral óssea, que por sua vez está relacionada com as intensidades em CT. A influência de incluir no modelo de regressão informação acerca da vizinhança foi estudada através da inclusão de imagens do desvio padrão nos 27 voxéis na vizinhança das variáveis previamente incluídas no modelo. O número de componentes a usar no modelo de regressão de mistura gaussiana foi obtido através da minimização do critério de Akaike. O pseudo-CT final foi obtido pela sobreposição das imagens obtidas através do duplo modelo de regressão, seguido da aplicação de um filtro gaussiano com desvio padrão de 0.5 de forma a mitigar os erros na segmentação dos tecidos ósseos. Este método foi validado usando imagens da zona pélvica de 6 pacientes usando um procedimento leave-one-out-cross-validation (LOOCV). Durante este procedimento, o modelo foi estimado através das variáveis de 5 pacientes (imagens de treino) e aplicado às variáveis relacionadas com MRI do paciente restante (imagem de validação), de forma a gerar o pseudo-CT. Este procedimento foi repetido para todas as seis combinações de imagens de treino e de validação e os pseudo-CT obtidos foram comparados com a imagem TC correspondente. No caso do modelo para os tecidos moles, verificou-se que a utilização de um polinómio de segundo grau permitia a obtenção de melhores resultados. Da mesma forma, verificou-se que a inclusão de informação acerca da vizinhança permitia uma melhor estimativa dos valores de pseudo-CT no caso dos tecidos ósseos. A segmentação dos tecidos ósseos foi considerada adequada uma vez que o valor médio do coeficiente de Dice entre estes tecidos e o osso em CT foi de 0.91 ±0.02. O valor médio do erro absoluto entre o pseudo-CT e a correspondente CT para todos os pacientes foi de 37.76±3.11 HU, enquanto que no caso dos tecidos ósseos o valor foi de 96.61±10.49 HU. Um erro médio de -2.68 ± 6.32 HU foi obtido, denotando a presença de bias no processo. Por outro lado, valores médios de peak-to-signal-noise-ratio (PSNR) e strucutre similarity índex (SSIM) de 23.92±1.62 dB e 0.91±0.01 foram obtidos, respectivamente. Os maiores erros foram encontrados no recto, uma vez que o ar não foi considerado neste método, nas interfaces entre diferentes tecidos, devido a erros no alinhamento das imagens, e nos tecidos ósseos. Desta forma, o método de obtenção de um pseudo-CT proposto nesta dissertação demonstrou ter potencial para permitir uma correcta estimativa da intensidade em CT. Os resultados obtidos demonstram uma melhoria significativa quando comparados com outros métodos encontrados na literatura que se baseiam num método relacionado com a intensidade, enquanto que se encontram na mesma ordem de magnitude de métodos baseados na anatomia do paciente. Para além disso, quando comparados com os primeiros, este método tem a vantagem de apenas uma sequência MRI ser utilizada, levando a uma redução no tempo de aquisição e nos custos associados. Por outro lado, a principal limitação deste método prende-se com a segmentação manual dos tecidos ósseos, o que dificulta a sua implementação clínica. Desta forma, o desenvolvimento de técnicas de segmentação automáticas dos tecidos ósseos torna-se necessária, sendo exemplos destas técnicas a criação de um shape model ou através da segmentação baseada num atlas. A combinação destes métodos com o método descrito nesta dissertação pode permitir a obtenção de uma alternativa às imagens de CT para o cálculo das doses em radioterapia e correcção de atenuação em PET-MRI.Purpose: Methods for deriving computed tomography (CT) equivalent information from MRI are needed for attenuation correction in PET-MRI applications, as well as for dose planning in MRI based radiation therapy workflows, due to the lack of correlation between the MR signal and the electron density of different tissues. This dissertation presents a method to generate a pseudo-CT from MR images acquired with a conventional MR pulse sequence. Methods: A T1-weighted Fast Field Echo sequence with 3 echo times was used. A 3-point water-fat decomposition algorithm was applied to the original MR images to obtain water and fat-only images as well as a quantitative fat fraction image. A R2* image was calculated using a mono-exponential fit between the first and third echo of the original MR images. The method for generating the pseudo-CT includes a dual-model regression between the MR features and a matched CT image. The first model was applied to soft tissues, while the second-model was applied to the bone anatomy that were previously segmented. The soft-tissue regression model consists of a second-order polynomial regression between the fat fraction values in soft tissue and the HU values in the CT image, while the bone regression model consists of a Gaussian mixture regression including the water, fat, fat fraction and R2* values in bone tissues. Neighbourhood information was also included in the bone regression model by calculating an image of the standard deviation of 27-neighbourhood of each voxel in each MR related feature. The final pseudo-CT was generated by combining the pseudo-CTs from both models followed by the application of a Gaussian filter for additional smoothing. This method was validated using datasets covering the pelvic area of six patients and applying a leave-one-out-cross-validation (LOOCV) procedure. During LOOCV, the model was estimated from the MR related features and the CT data of 5 patients (training set) and applied to the MR features of the remaining patient (validation set) to generate a pseudo-CT image. This procedure was repeated for the all six training and validation data combinations and the pseudo-CTs were compared to the corresponding CT image. Results: The average mean absolute error for the HU values in the body for all patients was 37.76±3.11 HU, while the average mean absolute error in the bone anatomy was 96.61±10.49 HU. No large differences in method accuracy were noted for the different patients, except for the air in the rectum which was classified as soft tissue. The largest errors were found in the rectum and in the interfaces between different tissue types. Conclusions: The pseudo-CT generation method here proposed has the potential to provide an accurate estimation of HU values. The results here reported are substantially better than other voxel-based methods proposed. However, they are in the same range as the results presented in anatomy-based methods. Further investigation in automatic MRI bone segmentation methods is necessary to allow the automatic application of this method into clinical practice. The combination of these automatic bone segmentation methods with the model here reported is expected to provide an alternative to CT images for dose planning in radiotherapy and attenuation correction in PET-MRI