5,109 research outputs found

    Incorporating accurate statistical modeling in PET: reconstruction for whole-body imaging

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    Tese de doutoramento em Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2007The thesis is devoted to image reconstruction in 3D whole-body PET imaging. OSEM ( Ordered Subsets Expectation maximization ) is a statistical algorithm that assumes Poisson data. However, corrections for physical effects (attenuation, scattered and random coincidences) and detector efficiency remove the Poisson characteristics of these data. The Fourier Rebinning (FORE), that combines 3D imaging with fast 2D reconstructions, requires corrected data. Thus, if it will be used or whenever data are corrected prior to OSEM, the need to restore the Poisson-like characteristics is present. Restoring Poisson-like data, i.e., making the variance equal to the mean, was achieved through the use of weighted OSEM algorithms. One of them is the NECOSEM, relying on the NEC weighting transformation. The distinctive feature of this algorithm is the NEC multiplicative factor, defined as the ratio between the mean and the variance. With real clinical data this is critical, since there is only one value collected for each bin the data value itself. For simulated data, if we keep track of the values for these two statistical moments, the exact values for the NEC weights can be calculated. We have compared the performance of five different weighted algorithms (FORE+AWOSEM, FORE+NECOSEM, ANWOSEM3D, SPOSEM3D and NECOSEM3D) on the basis of tumor detectablity. The comparison was done for simulated and clinical data. In the former case an analytical simulator was used. This is the ideal situation, since all the weighting factors can be exactly determined. For comparing the performance of the algorithms, we used the Non-Prewhitening Matched Filter (NPWMF) numerical observer. With some knowledge obtained from the simulation study we proceeded to the reconstruction of clinical data. In that case, it was necessary to devise a strategy for estimating the NEC weighting factors. The comparison between reconstructed images was done by a physician largely familiar with whole-body PET imaging

    Respiratory organ motion in interventional MRI : tracking, guiding and modeling

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    Respiratory organ motion is one of the major challenges in interventional MRI, particularly in interventions with therapeutic ultrasound in the abdominal region. High-intensity focused ultrasound found an application in interventional MRI for noninvasive treatments of different abnormalities. In order to guide surgical and treatment interventions, organ motion imaging and modeling is commonly required before a treatment start. Accurate tracking of organ motion during various interventional MRI procedures is prerequisite for a successful outcome and safe therapy. In this thesis, an attempt has been made to develop approaches using focused ultrasound which could be used in future clinically for the treatment of abdominal organs, such as the liver and the kidney. Two distinct methods have been presented with its ex vivo and in vivo treatment results. In the first method, an MR-based pencil-beam navigator has been used to track organ motion and provide the motion information for acoustic focal point steering, while in the second approach a hybrid imaging using both ultrasound and magnetic resonance imaging was combined for advanced guiding capabilities. Organ motion modeling and four-dimensional imaging of organ motion is increasingly required before the surgical interventions. However, due to the current safety limitations and hardware restrictions, the MR acquisition of a time-resolved sequence of volumetric images is not possible with high temporal and spatial resolution. A novel multislice acquisition scheme that is based on a two-dimensional navigator, instead of a commonly used pencil-beam navigator, was devised to acquire the data slices and the corresponding navigator simultaneously using a CAIPIRINHA parallel imaging method. The acquisition duration for four-dimensional dataset sampling is reduced compared to the existing approaches, while the image contrast and quality are improved as well. Tracking respiratory organ motion is required in interventional procedures and during MR imaging of moving organs. An MR-based navigator is commonly used, however, it is usually associated with image artifacts, such as signal voids. Spectrally selective navigators can come in handy in cases where the imaging organ is surrounding with an adipose tissue, because it can provide an indirect measure of organ motion. A novel spectrally selective navigator based on a crossed-pair navigator has been developed. Experiments show the advantages of the application of this novel navigator for the volumetric imaging of the liver in vivo, where this navigator was used to gate the gradient-recalled echo sequence

    Prostate lesion segmentation with convolutional neural networks

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    Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2020O cancro da próstata é o segundo tipo de cancro não cutâneo com maior incidência nos homens em todo o mundo, a seguir ao cancro do pulmão. Em Portugal, de acordo com a Associação Portuguesa de Urologia, esta doença representa, aproximadamente, 3,5% de todas as mortes nacionais, assim como 10% das mortes relacionadas com cancro. Para além destes dados, o Global Cancer Observatory, estima que a probabilidade de um homem ocidental ser diagnosticado ao longo da sua vida com cancro da próstata é de 8,1%. As causas diretas que levam ao aparecimento deste tipo de cancro ainda não estão totalmente clarificadas, no entanto, os hábitos alimentares, o estilo de vida e o ambiente em redor desempenham um fator preponderante no desencadeamento desta patologia. A deteção inicial deste cancro ocorre, normalmente, através de exames retais de rotina, ou através de alterações significativas do antigénio prostático específico detetáveis em análises ao sangue. De seguida, para confirmação e localização do possível tumor, podem ser adotados três procedimentos: ecografia transrectal, colheita de uma biópsia local ou análise de imagem prostática através de ressonância magnética. Por ser o procedimento menos invasivo, a ressonância magnética é a ferramenta mais utilizada para deteção e localização de lesões na próstata. No Hospital da Luz de Lisboa, a análise de imagens provenientes de ressonância magnética multi-paramétrica é o procedimento padrão para a localização de lesões prostáticas. Neste exame, geralmente, são adquiridas três sequências em T2, uma em cada um dos planos axial, coronal e sagital, duas sequências com difusão e uma sequência emT1. Cada exame demora, aproximadamente, 45minutos a ser analisado corretamente pelo radiologista. Após a análise, é atribuída uma classificação ao estado do paciente, de T1 a T4, sendo que até T2 o tumor ainda se encontra exclusivamente no interior da próstata e em T4 apresenta os maiores índices de disseminação em redor da próstata. Esta classificação é preponderante para o planeamento da cirurgia de remoção do tumor. Nesta avaliação, é normalmente identificada a lesão ”índex” da próstata, que corresponde à lesão com maior índice cancerígenae, por isso, a mais visível. No entanto, podem em certos casos existir lesões de menor dimensão ou de menor relevância, lesões ”não-índex”, que em determinadas circunstâncias levam à alteração da classificação do estado do paciente. Este tipo de lesões, por vezes, não é facilmente localizado e o procedimento cirúrgico resultante acaba por não ser o mais indicado e gerar, no futuro, reincidências. Até T2, a prostatectomia deve ser realizada com o intuito de remover apenas a lesão ou a próstata por completo, no entanto, em T3 e em T4, a abordagem deve ser um pouco mais severa, sendo necessário também remover camadas celulares fora da próstata como margem de segurança para evitar uma reincidência. A introdução de algoritmos de inteligência artificial no ramo da medicina, com o propósito de realizar tarefas como segmentação, classificação e deteção de artefactos em imagens digitais, tem sido cada vez mais preponderante na evolução tecnológica da saúde. No panorama geral da medicina, os métodos de avaliação automatizada permitem executar tarefas com maior rapidez, precisão e assertividade face à capacidade humana, sendo possível explorar numa imagem, por exemplo, texturas, formas, estruturas e até mesmo orientações nucleares de certos artefactos. Relativamente ao cancro da próstata, para além de algoritmos que visam auxiliar as avaliações promovidas pela anatomia patológica, o grande foco centra-se em melhorar os métodos de análise de imagem de ressonância, por forma a tornar os diagnósticos mais precisos. Assim sendo, a criação de algoritmos que permitam a segmentação das lesões prostáticas, assim como respetiva ponderação da classificação do estado do paciente, revela-se como a tarefa principal na evolução do diagnóstico do cancro da próstata. Desta forma, como objetivo de otimizar a deteção e localização das lesões prostáticas, esta dissertação apresenta um conjunto de algoritmos que visam a segmentação de lesões da próstata em imagens de ressonância magnética. O projeto foi desenvolvido no centro de formação e investigação LearningHealth, no Hospital da Luz de Lisboa, e apresenta duas etapas principais: a criação do modelo de segmentação da próstata e a elaboração do modelo de segmentação das lesões prostáticas. Na fase inicial desta dissertação, a criação de um modelo que segmentasse a zona da próstata, por forma a aumentar, posteriormente, a área de deteção das lesões, foi identificado como o primeiro passo. Com base em modelos de deep learning, mais especificamente através de convolutional neuralnetworks, foi desenvolvida uma arquitetura para o propósito anteriormente descrito. Esta arquitetura, baseada numa rede já previamente construída, a U-Net, apresenta características específicas que permitem a entrada de imagens de ressonância magnética da próstata, slice a slice, a gestão da informação que essas imagens apresentam e, por fim, a criação de máscaras binárias da zona da próstata consoante a slice de entrada. Com as máscaras da zona prostática, foi possível delinear um contorno e promover uma sub-seleção dessa zona na imagem original, criando volumes onde a área de deteção das lesões da próstata é isolada. Na segunda fase deste projeto, foi criado um modelo para segmentar diretamente as lesões da próstata. Para tal, foram utilizadas as imagens adquiridas após a primeira parte do projeto, assim como a rede identificada para localizar a próstata. Contudo, esta arquitetura sofreu alterações estruturais, por forma a otimizar o rendimento do modelo. Ao contrário da rede anterior, esta arquitetura permite a entrada de duas imagens na mesma instância, a original T2 e a respetiva original ADC. No final, o output é, igualmente, uma máscara binária, desta vez localizando as lesões da próstata em imagens de ressonância. Em ambos os modelos, foram utilizadas como imagens de input, casos de ressonância magnética adquiridos no Hospital da Luz de Lisboa. Para este processo final, foi necessário segmentar manualmente tanto a próstata, como as respetivas lesões, nas imagens do hospital. Para tal, utilizou-se um software hospitalar, o Multi-Parametric Analysis, que permite o registo das imagens originais e a elaboração das máscaras manualmente. Este processo de identificação e elaboração manual das máscaras da próstata e das lesões foi realizado por uma radiologista do Hospital da Luz de Lisboa, a Dra. Adalgisa Guerra. O modelo desenvolvido na primeira etapa, para a segmentação da próstata, apresentou um valor de Dice Similarity Coefficient, a principal métrica de avaliação em projetos de segmentação, de 0,88. Este valor é semelhante aos valores de referência destacados no state oftheart. Após a conclusão desta etapa, criaram-se cinco modelos para segmentar as lesões da próstata, sendo que o modelo que apresentou melhores resultados foi o que tinha como input as imagens ampliadas da próstata em T2 e ADC e as respetivas máscaras das lesões criadas em imagensT2. O resultado final deste modelo em termos de Dice Similarity Coefficient foi de 0,76, Hausdorff Distance de 20,2mm e Mean Square Distance de 2,1 mm. Este resultado realça o impacto que a informação combinada de duas sequências consegue ter no processo de segmentação de lesões da próstata. Concluindo, a medicina, em consonância com as restantes áreas da sociedade, está a evoluir e a inteligência artificial terá um papel preponderante nessa transição. Neste caso, esta dissertação pretende otimizar a metodologia utilizada num hospital local, conferindo aos profissionais de saúde cada vez mais e melhores condições para realizarem as suas tarefas

    3D shape instantiation for intra-operative navigation from a single 2D projection

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    Unlike traditional open surgery where surgeons can see the operation area clearly, in robot-assisted Minimally Invasive Surgery (MIS), a surgeon’s view of the region of interest is usually limited. Currently, 2D images from fluoroscopy, Magnetic Resonance Imaging (MRI), endoscopy or ultrasound are used for intra-operative guidance as real-time 3D volumetric acquisition is not always possible due to the acquisition speed or exposure constraints. 3D reconstruction, however, is key to navigation in complex in vivo geometries and can help resolve this issue. Novel 3D shape instantiation schemes are developed in this thesis, which can reconstruct the high-resolution 3D shape of a target from limited 2D views, especially a single 2D projection or slice. To achieve a complete and automatic 3D shape instantiation pipeline, segmentation schemes based on deep learning are also investigated. These include normalization schemes for training U-Nets and network architecture design of Atrous Convolutional Neural Networks (ACNNs). For U-Net normalization, four popular normalization methods are reviewed, then Instance-Layer Normalization (ILN) is proposed. It uses a sigmoid function to linearly weight the feature map after instance normalization and layer normalization, and cascades group normalization after the weighted feature map. Detailed validation results potentially demonstrate the practical advantages of the proposed ILN for effective and robust segmentation of different anatomies. For network architecture design in training Deep Convolutional Neural Networks (DCNNs), the newly proposed ACNN is compared to traditional U-Net where max-pooling and deconvolutional layers are essential. Only convolutional layers are used in the proposed ACNN with different atrous rates and it has been shown that the method is able to provide a fully-covered receptive field with a minimum number of atrous convolutional layers. ACNN enhances the robustness and generalizability of the analysis scheme by cascading multiple atrous blocks. Validation results have shown the proposed method achieves comparable results to the U-Net in terms of medical image segmentation, whilst reducing the trainable parameters, thus improving the convergence and real-time instantiation speed. For 3D shape instantiation of soft and deforming organs during MIS, Sparse Principle Component Analysis (SPCA) has been used to analyse a 3D Statistical Shape Model (SSM) and to determine the most informative scan plane. Synchronized 2D images are then scanned at the most informative scan plane and are expressed in a 2D SSM. Kernel Partial Least Square Regression (KPLSR) has been applied to learn the relationship between the 2D and 3D SSM. It has been shown that the KPLSR-learned model developed in this thesis is able to predict the intra-operative 3D target shape from a single 2D projection or slice, thus permitting real-time 3D navigation. Validation results have shown the intrinsic accuracy achieved and the potential clinical value of the technique. The proposed 3D shape instantiation scheme is further applied to intra-operative stent graft deployment for the robot-assisted treatment of aortic aneurysms. Mathematical modelling is first used to simulate the stent graft characteristics. This is then followed by the Robust Perspective-n-Point (RPnP) method to instantiate the 3D pose of fiducial markers of the graft. Here, Equally-weighted Focal U-Net is proposed with a cross-entropy and an additional focal loss function. Detailed validation has been performed on patient-specific stent grafts with an accuracy between 1-3mm. Finally, the relative merits and potential pitfalls of all the methods developed in this thesis are discussed, followed by potential future research directions and additional challenges that need to be tackled.Open Acces

    Doctor of Philosophy

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    dissertationMagnetic resonance guided high intensity focused ultrasound (MRgHIFU) is a promising minimal invasive thermal therapy for the treatment of breast cancer. This study develops techniques for determining the tissue parameters - tissue types and perfusion rate - that influence the local temperature during HIFU thermotherapy procedures. For optimal treatment planning for each individual patient, a 3D volumetric breast tissue segmentation scheme based on the hierarchical support vector machine (SVM) algorithm was developed to automatically segment breast tissues into fat, fibroglandular tissue, skin and lesions. Compared with fuzzy c-mean and conventional SVM algorithm, the presented technique offers tissue classification performance with the highest accuracy. The consistency of the segmentation results along both the sagittal and axial orientations indicates the stability of the proposed segmentation routine. Accurate knowledge of the internal anatomy of the breast can be utilized in the ultrasound beam simulation for the treatment planning of MRgHIFU therapy. Completely noninvasive MRI techniques were developed for visualizing blood vessels and determining perfusion rate to assist in the MRgHIFU therapy. Two-point Dixon fat-water separation was achieved using a 3D dual-echo SSFP sequence for breast vessel imaging. The performances of the fat-water separation with various readout gradient designs were evaluated on a water-oil phantom, ex vivo pork sample and in vivo breast imaging. Results suggested that using a dual-echo SSFP readout with bipolar readout gradient polarity, blood vasculature could be successfully visualized through the thin-slab maximum intensity projection SSFP water-only images. For determining the perfusion rate, we presented a novel imaging pulse sequence design consisting of a single arterial spin labeling (ASL) magnetization preparation followed by Look-Locker-like image readouts. This flow quantification technique was examined through simulation, in vitro and in vivo experiments. Experimental results from a hemodialyzer when fitted with a Bloch-equation-based model provide flow measurements that are consistent with ground truth velocities. With these tissue properties, it is possible to compensate for the dissipative effects of the flowing blood and ultimately improve the efficacy of the MRgHIFU therapies. Complete noninvasiveness of these techniques allows multiple measurements before, during and after the treatment, without the limitation of washout of the injected contrast agent

    Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images

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    Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing a reliable diagnostic value. Similarly, quantification of the myocardial mechanics throughout the cardiac cycle, analysis of the activation patterns in the heart via electrocardiography (ECG) signals, serve as good cardiac diagnosis indicators. Furthermore, high quality anatomical models of the heart can be used in planning and guidance of minimally invasive interventions under the assistance of image guidance. The most crucial step for the above mentioned applications is to segment the ventricles and myocardium from the acquired cardiac image data. Although the manual delineation of the heart structures is deemed as the gold-standard approach, it requires significant time and effort, and is highly susceptible to inter- and intra-observer variability. These limitations suggest a need for fast, robust, and accurate semi- or fully-automatic segmentation algorithms. However, the complex motion and anatomy of the heart, indistinct borders due to blood flow, the presence of trabeculations, intensity inhomogeneity, and various other imaging artifacts, makes the segmentation task challenging. In this work, we present and evaluate segmentation algorithms for multi-modal, multi-dimensional cardiac image datasets. Firstly, we segment the left ventricle (LV) blood-pool from a tri-plane 2D+time trans-esophageal (TEE) ultrasound acquisition using local phase based filtering and graph-cut technique, propagate the segmentation throughout the cardiac cycle using non-rigid registration-based motion extraction, and reconstruct the 3D LV geometry. Secondly, we segment the LV blood-pool and myocardium from an open-source 4D cardiac cine Magnetic Resonance Imaging (MRI) dataset by incorporating average atlas based shape constraint into the graph-cut framework and iterative segmentation refinement. The developed fast and robust framework is further extended to perform right ventricle (RV) blood-pool segmentation from a different open-source 4D cardiac cine MRI dataset. Next, we employ convolutional neural network based multi-task learning framework to segment the myocardium and regress its area, simultaneously, and show that segmentation based computation of the myocardial area is significantly better than that regressed directly from the network, while also being more interpretable. Finally, we impose a weak shape constraint via multi-task learning framework in a fully convolutional network and show improved segmentation performance for LV, RV and myocardium across healthy and pathological cases, as well as, in the challenging apical and basal slices in two open-source 4D cardiac cine MRI datasets. We demonstrate the accuracy and robustness of the proposed segmentation methods by comparing the obtained results against the provided gold-standard manual segmentations, as well as with other competing segmentation methods

    Crepuscular Rays for Tumor Accessibility Planning

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