Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and di↵erent tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherrently sparse in some bases, due to their inherrant structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competetive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by `1 pooling, and correct patient identification is consistently achieved 100% over 1000 trials, when either the face data or fingerprint data are implemented as a classification basis. The algorithm gets 100% classification when faces and fingerprints are concatenated into multimodal datasets. This suggests that 100% patient identification will be achievable in the clinal setting. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Introdução: A síndrome da apneia obstrutiva do sono (SAOS) tem sido cada vez mais estudada devido à sua prevalência e relação com problemas graves de saúde, como o aumento do risco de patologias cardiovasculares. O padrão-ouro para diagnóstico é a polissonografia (PSG), mas este método não fornece dados anatômicos da faringe dos pacientes. Objetivos: Avaliar, por meio de imagens de TC multislice (TCMS), o aspecto da faringe em indivíduos de um grupo controle e do grupo SAOS em vigília e durante o sono, observando as modificações sofridas nas estruturas faríngeas circunvizinhas e na coluna aérea; avaliar o comportamento faríngeo durante o ciclo respiratório nos indivíduos em vigília. Métodos: A amostra compreendeu 11 pacientes com SAOS grave, índice apneia/hipopneia maior que trinta, confirmados por PSG, e 7 indivíduos sem SAOS, controles, confirmados por PSG por Stardust, todos do sexo masculino. Os indivíduos foram submetidos a avaliação por TCMS com 16 canais (modelo Brilliance 16,Philips Medical Systems) e polissonografia simultânea. Os exames foram realizados com os indivíduos acordados e dormindo, em sono espontâneo. Foram realizadas aquisições volumétricas em ambos os estados (acordado e dormindo) e, posteriormente, avaliação das reformatações nos planos axiais, sagitais e das reconstruções em 3D. Foram realizadas medidas lineares e volumétricas: diâmetro mínimo anteroposterior e laterolateral, área mínima na orofaringe, retropalatal (RP) e retrolingual (RL), diâmetro transverso da língua, espessura da parede faríngea na região retropalatal, distância entre os ramos mandibulares, diâmetro anteroposterior da língua, comprimento e espessura do palato mole, espessura do tecido gorduroso subcutâneo na região submentoniana, distância MP-H (borda superior do hioide ao plano mandibular); volume da coluna aérea, volume das paredes faríngeas laterais, volume da gordura do espaço parafaríngeo e o volume da lingua. Resultados: As medidas significativamente menores encontradas no grupo SAOS, na região retropalatal, foram o diâmetro laterolateral e a área. Também houve diferença estatisticamente significativa nas medidas de espessura da língua, do palato, comprimento do palato, distância MP-H e ângulo mandibular. Foi realizado estudo comparativo entre as medidas lineares e volumétricas da faringe do grupo SAOS em estado de vigília e sono. Houve redução dos diâmetros retropalatal anteroposterior e laterolateral, da área e do volume da via aérea entre os estados acordado e dormindo. No grupo controle foram realizadas medidas lineares e volumétricas, porém não houve diferença significativa entre os diferentes estados. A variação da medida da área na região retropalatal foi significativa no grupo SAOS durante o estudo dinâmico. Conclusão: Em nossa amostra, as vias aéreas superiores de indivíduos do grupo SAOS diferem daquelas dos indivíduos do grupo controle. O grupo SAOS e o grupo controle sofrem modificações diferentes nas suas VAS, quando entram em estado de sono, de forma que as modificações do grupo SAOS são caracterizadas por mudanças significativas de medidas lineares e volumétricas, sugerindo maior suscetibilidade ao colapso faríngeo. O estudo dinâmico demonstrou que, na região retropalatal, indivíduos com SAOS são mais passíveis de colapso faríngeo. / Introduction: The obstructive sleep apnea syndrome (OSAS) has been increasingly studied because of the prevalence and relationship to serious healthy problems such as increased risk of cardiovascular diseases. The gold standard for diagnosis is polysomnography (PSG), but this method does not provide anatomical data of the pharynx of patients. Objectives: To evaluate the changes in upper airway and soft tissue structures surrounding the upper airway during wakefulness and sleep in patients with OSAS and a control group without OSAS. Methods: 11 patients with severe OSAS (apnea index > 30 event/hour) and 7 subjects without apnea and polysomnogram (Stardust) negative for OSAS were examined on a 16-channel multislice computed tomography. The exam was carried out with the patient awake and during spontaneous asleep, without any kind of sedation. The patients were monitored with a polysomnogram during the exam to assure the sleep and awaken state. Airway and soft-tissue 2-D measurements were performed in retropalatal and retroglossal level as well as, 3-D volumetric measurements. The measures were minimum anteroposterior and laterolateral diameter, the minimum area in the oropharynx in retropalatal and retroglossal levels, transverse and anteroposterior diameter of the tongue, pharyngeal wall thickness in RP, the distance between the mandibular branches, thickness and the length of the soft palate, the thickness of subcutaneous fat tissue in the submental region, MP-H distance (top edge of the hyoid to mandibular plane); volume of air column, volume of lateral pharyngeal wall, the volume of the parapharyngeal space fat, and the tongue volume. Results: Patients with OSAS had a small cross sectional area of retropalatal pharynx and the upper airway are significantly smaller when the patients were asleep. The measurements which presented significant decrease during sleep were the laterolateral and anteroposterior retropalatal diameter and minimal cross-seccional area. There was an inverse relationship between dimensions of lateral pharyngeal walls and the airway area, which was more pronounced during sleep. There was also a statistically significant difference in measures of the tongue thickness, palate thickness and length, MP-H distance and mandibular angle. There was a reduction of laterolateral, anteroposterior diameter, cross seccional area and upper airway volume, between awake and asleep states. Linear and volumetric measurements were performed in the control group but there was no significant difference between the different states. The variation of the cross seccional area in the OSAS group was significant during the dynamic study. Conclusion: The upper airway of subjects with severe OSAS differs from controls subjects. The OSAS group and the control group undergo different modifications in their upper airway after the sleep onset, and the changes of obstructive sleep apnea syndrome are characterized by significant changes of linear and volumetric measurements, suggesting greater pharyngeal collapsibility. There were significant differences in the patterns of dynamic airway motion between patients with and those without OSAS.

A doença arterial coronária (DAC) avançada caracteriza-se pela presença de vasos calcificados e difusamente estenosados, o que reduz a acurácia da angiografia coronária não-invasiva por meio dos atuais aparelhos de tomografia computadorizada (CT) com 16 colunas de detectores (16-MDCTA). O principal objetivo deste estudo foi tentar demonstrar uma acurácia diagnóstica global de pelo menos 90% para a detecção de estenoses coronárias >= 50% em pacientes com DAC avançada e alta probabilidade de possuírem escores de cálcio coronário elevados, mediante a utilização de um sistema de CT com 32 colunas de detectores, todas capazes de adquirir simultaneamente cortes com 0,5 mm de espessura (32x0,5-MDCTA). Angiografias coronárias sincronizadas ao traçado de ECG foram obtidas por meio da 32x0,5-MDCTA (32 cortes de 0,5 mm, voxels isotrópicos de 0,35x0,35x0,35 mm³, rotação do gantry a 400 ms) em 30 pacientes consecutivos (25 do sexo masculino, com idade média igual a 59±13 anos e índice de massa corpórea médio de 26,2±4,9 Kg/m²) e portadores de DAC avançada. As principais artérias nativas, incluindo seus ramos de primeira ordem com diâmetro >= 1,5 mm bem como os enxertos coronários existentes, foram avaliados de forma independente quanto à presença de estenoses >= 50%. Os stents foram excluídos. As angiografias coronarianas convencionais (realizadas em média 18±12 dias antes das respectivas 32x0,5-MDCTAs) foram analisadas de maneira quantitativa (angiografia coronária quantitativa). A mediana do escore de cálcio de Agatston foi igual a 510 (variação entre 3 e 5066). A sensibilidade, a especificidade e os valores preditivos positivo e negativo para a detecção de estenoses >= 50% nas artérias coronárias nativas foram seguintes: 76% (29/38), 94% (190/202), 71% (29/41), e 96% (190/199), respectivamente. A acurácia diagnóstica global foi de 91% (219/240). Do total de vasos analisados, 20% (69/352) foram excluídos devido à existência de um dos seguintes artefatos: movimento, ruído e baixo realce do contraste radiológico isoladamente ou em conjunto (45/69 ou 65%), distorção da imagem secundária à presença de eletrodo de desfibrilador ou marcapasso (18/69 ou26%), e calcificação arterial excessiva (6/69 ou 9%). Conclui-se que a 32x0,5-MDCTA exclui com precisão as estenoses coronarianas >= 50% em pacientes com DAC avançada e escore de cálcio coronário elevado, com acurácia diagnóstica global de 91% / Advanced coronary artery disease (CAD) is characterized by calcified and diffusely stenotic vessels, hampering accuracy of noninvasive coronary angiography with current 16-detector computed tomography (CT) scanners. The main purpose of this study was to try to demonstrate an overall diagnostic accuracy of at least 90% for detection of coronary stenoses >= 50% by half-millimeter 32-detector CT angiography (32x0.5-MDCTA) in patients with advanced CAD and a high likelihood of having elevated coronary calcium scores. ECG-gated coronary 32x0.5-MDCTA (32x0.5 mm cross-sections, 0.35x0.35x0.35 mm³ isotropic voxels, 400 ms gantry rotation) was performed in 30 consecutive patients (25 male, 59±13 years-old, 26.2±4.9 Kg/m²) with advanced CAD. Major coronary arteries, including >=1.5-mm first order branches, and bypass grafts were independently evaluated for >= 50% stenoses. Stents were excluded. Conventional coronary angiography (performed on average 18±12 days before their corresponding 32x0.5-MDCTAs) was analyzed by quantitative coronary angiography. Median Agatston calcium score was 510 (3-5066 range). Sensitivity, specificity, positive and negative predictive values for detection of >= 50% stenoses in the native coronary arteries were: 76% (29/38), 94% (190/202), 71% (29/41), and 96% (190/199), respectively. Overall diagnostic accuracy was 91% (219/240). Twenty percent (69/352) of the vessels were excluded from the analysis due to one of the following artifacts: motion, noise, and low contrast enhancement isolated or in combination (45/69 or 65%), image distortion secondary to an ICD or pacemaker lead (18/69 or 26%), and severe arterial calcification (6/69 or 9%). We concluded that 32x0.5-MDCTA accurately excludes >= 50% coronary stenoses in patients with advanced CAD and high calcium scores, showing an overall diagnostic accuracy of 91%

Medical images have been used increasingly for diagnosis, treatment planning, monitoring disease processes, and other medical applications. A large variety of medical imaging modalities exists including CT, X-ray, MRI, Ultrasound, etc. Frequently a group of images need to be compared to one another and/or combined for research or cumulative purposes. In many medical studies, multiple images are acquired from subjects at different times or with different imaging modalities. Misalignment inevitably occurs, causing anatomical and/or functional feature shifts within the images. Computerized image registration (alignment) approaches can offer automatic and accurate image alignments without extensive user involvement and provide tools for visualizing combined images. This dissertation focuses on providing automatic image registration strategies. After a through review of existing image registration techniques, we identified two registration strategies that enhance the current field: (1) an automated rigid body and affine registration using voxel similarity measurements based on a sequential hybrid genetic algorithm, and (2) an automated deformable registration approach based upon a linear elastic finite element formulation. Both methods streamlined the registration process. They are completely automatic and require no user intervention. The proposed registration strategies were evaluated with numerous 2D and 3D MR images with a variety of tissue structures, orientations and dimensions. Multiple registration pathways were provided with guidelines for their applications. The sequential genetic algorithm mimics the pathway of an expert manually doing registration. Experiments demonstrated that the sequential genetic algorithm registration provides high alignment accuracy and is reliable for brain tissues. It avoids local minima/maxima traps of conventional optimization techniques, and does not require any preprocessing such as threshold, smoothing, segmentation, or definition of base points or edges. The elastic model was shown to be highly effective to accurately align areas of interest that are automatically extracted from the images, such as brains. Using a finite element method to get the displacement of each element node by applying a boundary mapping, this method provides an accurate image registration with excellent boundary alignment of each pair of slices and consequently align the entire volume automatically. This dissertation presented numerous volume alignments. Surface geometries were created directly from the aligned segmented images using the Multiple Material Marching Cubes algorithm. Using the proposed registration strategies, multiple subjects were aligned to a standard MRI reference, which is aligned to a segmented reference atlas. Consequently, multiple subjects are aligned to the segmented atlas and a full fMRI analysis is possible.

Pulmonary emphysema contributes to the chronic airflow limitation characteristic of chronic obstructive pulmonary disease (COPD), which is a leading cause of morbidity and mortality worldwide. Computed tomography (CT) has enabled in vivo assessment of pulmonary emphysema at the macroscopic level, and is commonly used to identify and assess the extent of the disease. During the past decade, the availability of CT imaging data has increased rapidly, while the image quality has continued to improve. High-resolution CT is extremely valuable both for patient diagnosis and for studying diseases at the population level. However, visual assessment of these large data sets is subjective, inefficient, and expensive. This has increased the demand for objective, automatic, and reproducible image analysis methods. For the assessment of pulmonary emphysema on CT, computational models usually aim either to give a measure of the extent of the disease, or to categorize the emphysema subtypes apparent in a scan. The standard methods for quantitating emphysema extent are widely used, but they remain sensitive to changes in imaging protocols and patient inspiration level. For computational subtyping of emphysema, the methods remain at a developmental stage, and one of the main challenges is the lack of reliable label data. Furthermore, the classic emphysema subtypes were defined on autopsy before the availability of CT and could be considered outdated. There is also no consensus on how to match the subtypes on autopsy to the varying emphysema patterns present on CT. This work presents two methodological improvements for analyzing emphysema on CT. For the assessment of emphysema extent, a novel probabilistic approach is introduced and evaluated on a longitudinal data set with varying imaging protocols. The presented model is shown to improve significantly compared to standard methods, particularly at the presence of differing noise levels. The approach is also applied on quantifying emphysema on a large data set of cardiac CT scans, and is shown to improve the prediction of emphysema extent on subsequent full-lung CT scans. The second major contribution of this work applies unsupervised learning to recognizing patterns of emphysema on CT. Instead of trying to reproduce the classic subtypes, the novel approach aims to capture the most dominant variations of lung structure pertaining to emphysema. While removing the reliance on visually assigned labels, the learned patterns are shown to represent different manifestations of emphysema with distinct appearances and regular spatial distributions. The clinical significance of the patterns is also demonstrated, along with high-level performance in the application of content-based image retrieval. The contributions of this work advance the analysis of emphysema on CT by applying novel machine learning approaches to increase the value of the available imaging data. Probabilistic methods improve from the crude standard methods that are currently used to quantitate emphysema, and the value of learning disease patterns directly from image data is demonstrated. The common framework relying on replicating visually assigned labels of outdated subtypes has not achieved widespread acceptance. The methodology presented in this work may have a substantial impact on how emphysema subtypes on CT are recognized and defined in the future.

Molecular imaging of the human body is beginning to revolutionize drug development, drug delivery targeting, prognostics and diagnostics, and patient screening for clinical trials. The primary clinical tool of molecular imaging is Positron Emission Tomography (PET), which uses radioactively tagged probes (radioligands) for the in vivo quantification of blood flow, metabolism, protein distribution, gene expression and drug target occupancy. While many radioligands are used in human research, only a few have been adopted for clinical use. A major obstacle to translating these tools from bench-to-bedside is that PET images acquired using complex radioligands can not be properly interpreted or quantified without arterial blood sampling during the scan. Arterial blood sampling is an invasive, risky, costly, time consuming and uncomfortable procedure that deters subjects' participation and requires highly specialized medical staff presence and laboratories to run blood analysis. Many approaches have been developed over the years to reduce the number of blood samples for certain classes of radioligands, yet the ultimate goal of zero blood samples has remained illusive. In this dissertation we break this proverbial blood barrier and present for the first time a non-invasive PET quantification framework. To accomplish this, we introduce novel image processing, modeling, and tomographic reconstruction tools. First, we developed dedicated pharmacokinetic modeling, machine learning and optimization framework based on the fusion of Electronic Health Records (EHR) data with dynamic PET brain imaging information. EHR data is used to infer individualized metabolism and clearance rates of the radioligand from the body. This is combined with simultaneous estimation on multiple distinct regions of the PET image. A substantial part of this effort involved curating, and then mining, an extensive database of PET, EHR and arterial blood sampling data. Second, we outline a new tomographic reconstruction and resolution modeling approach that takes into account the scanner point spread function in order to improve the resolution of existing PET data-sets. This technique allows visualization and quantification of structures smaller than previously possible. Recovery of signal from blood vessels and integration with the non-invasive framework is demonstrated. We also show general applicability of this technique for visualization and signal recovery from the raphe, a sub-resolution cluster of nuclei in the brain that were previously not detectible with standard techniques. Our framework can be generalizable to all classes of radioligands, independent of their kinetics and distribution within body. Work presented in this thesis will allow the PET scientific and clinical community to advance towards the ultimate goal of making PET cost-effective and to enable new clinical use cases.

Cardiovascular disease (CVD) consistently ranks among the leading causes of death in the United States. The most common subtype of CVD, ischemic heart disease, is a frequent precursor of myocardial infarction and heart failure, most commonly affecting the left ventricle (LV). Today, echocardiography is regarded as the gold standard in screening, diagnosis, and monitoring of LV dysfunction. But while global assessment of LV function tends to be quantitative, cardiologists with specific expertise still perform many regional evaluations subjectively. However, a more objective and quantitative measure of regional function – myocardial strain – has been developed and widely studied using 2D echocardiography. With recent developments in real-time 3D echocardiography (RT3DE), it has become possible to measure strain in its native 3D orientation as well. Our laboratory’s earlier work introduced the Optical Flow (OF) method of strain analysis, which was validated on simulated echocardiograms as well as through animal studies. The principal goal of this thesis is to translate this OF-based method of strain estimation from the research setting to the patient’s bedside. We have performed a series of studies to evaluate the feasibility, accuracy, and reproducibility of OF-based myocardial strain estimation in a routine clinical setting. The first investigation focused on the optimization of RT3DE acquisition and the OF processing pipeline for use in human subjects. Subsequently, we evaluated the capacity of this technique to distinguish abnormal strain patterns in patients with CVD and varying degrees of LV dysfunction. Our analysis revealed that segmental strain measures obtained by OF may have better sensitivity and specificity than the more commonly used global LV strains. Our third validation study examined the reproducibility of these strain measures in both healthy and diseased populations. We established that OF-based strain measures demonstrate repeatability comparable to that achieved by the latest commercial software commonly used in clinical research to estimate 2D or 3D strain. These studies were driven in large part by the absence of a ground truth or accepted gold standard of 3D strain measurements in the human LV. However, cardiac magnetic resonance imaging has had considerable success in measuring some forms of strain in the human LV. We therefore began to develop an image-processing pipeline to derive strain estimates from a new pulse sequence called 3D-DENSE. We further sought to improve the OF pipeline by automating the process of tracking the LV border. To this end, we developed a level-set based technique which tracks the LV endocardium. Our evaluation of its performance on RT3DE data confirmed that this method performs within the limits of inter-observer variability. Overall, our pilot studies of OF-based strain estimation demonstrate that the technique possesses several promising features for improving cardiologists’ ability to quantify and interpret the complex three-dimensional deformations of the human LV.

Cardiac diseases and conduction disorders are associated with stroke, heart failure and sudden cardiac death and are a major health concern worldwide. In the US alone, more than 14 million people suffer from heart rhythm disorders. Current mapping and characterization techniques in the clinic involve invasive procedures, which are time-consuming, costly, and may involve ionizing radiation. In this dissertation, we introduce Electromechanical Wave Imaging (EWI) as a non-invasive, ultrasound-based treatment planning tool for pre-procedure characterization and assessment of arrhythmia in the clinic. In particular, standard EWI processing methods for mapping the electromechanical wave (EW), i.e. the onset of the mechanical activity following the depolarization of the heart, are described and detailed. Next, validation of EWI is performed with 3D electromechanical mapping and the EW propagation is shown to follow the electrical activation in all four chambers of the heart. Demonstration of the value of EWI for the characterization of cardiac arrhythmia is accomplished in vivo in a large animal model. First, EWI is shown capable of localizing the earliest region of activation in the ventricles during pacing from a standard pacemaker lead, as well as during pacing from a novel biological pacemaker. Repeatability is also demonstrated between consecutive cardiac cycle during normal sinus rhythm and during pacing. Then, in the atria, we demonstrate that EWI is capable of accurately identifying focal sources while pacing from several locations in both the left and right atria. In addition to being capable of localizing the focal source, EWI is also shown capable of differentiating between endocardial and epicardial focal sources. Finally, it is shown that EWI can correctly identify regions of infarction and monitor formation of infarcts over several days, after ligation of the left anterior descending coronary artery of canine hearts. Novel processing techniques aimed at extracting quantitative parameters from EWI estimates are then developed and implemented. Details of the implementation of processing methods for estimating the velocity of the EW propagation are presented, and a study of the EW velocity values in a canine heart before and after infarct formation is conducted. Electromechanical cycle length mapping (ECLM), which is aimed at extracting local rates of electromechanical activation in the heart, is then introduced and its implementation detailed. ECLM is subsequently validated in a paced canine heart in vivo. Finally, initial clinical feasibility is demonstrated. First, in the study of treatment of chaotic arrhythmia such as in the case of atrial fibrillation patients undergoing direct current cardioversion, ECLM is shown to be able to confirm acute treatment success. Then, the clinical value of EWI in the electrophysiology lab as a treatment planning tool for the characterization of focal arrhythmia is shown in ventricular tachycardia and Wolff-Parkinson-White patients. EWI is currently only a step away from real-world clinical application. As a non-invasive, ultrasound-based imaging modality, EWI is capable of providing relevant insights into the origins of an arrhythmia and has the potential to position itself in the clinic as a uniquely valuable pre-procedure planning tool for the non-invasive characterization of focal arrhythmias.

The overall goal of this thesis was to determine whether optical tomographic imaging can be employed to predict treatment outcome in women with breast cancer (BC) who undergo neoadjuvant chemotherapy (NACT). NACT is widely applied as a standard treatment for patients with newly diagnosed operable invasive BC. Only about 13-30% of women have a response to this treatment. Furthermore, NACT is an expensive and toxic treatment that takes several months to completely administer. In order to know the response of the patient, physicians usually need to wait until the months of NACT has finished and the patient has undergo surgery in which they receive the pathology. If the long-term treatment response could be predicted early into the treatment regimen, the patient would be relieved of any unnecessary side effects and alternative treatments could be initiated. We have used a novel dynamic DOT system to study the effects of targeted NACT. Unlike X-ray imaging, which requires potentially harmful ionizing radiation, DOT can be applied without side effects, which is particularly important in the case of multiple imaging sessions to be performed over the course of treatment. We have tracked 40 subjects and imaged them at 6 different time points during their NACT. For this study, two different types of data were collected: static (single 3D image) and dynamic (3D movies). The combination of the data may be used to accurately determine the response of the patient. With non-invasive, non-ionizing DOT imaging we have been able to determine within two weeks if the patient will respond to treatment with an accuracy as high as 94.1%.

A delimitação do sarcoma de aplicação em felinos (SAF) é um desafio para os cirurgiões, portanto a tomografia computadorizada (TC) possui grande impacto no planejamento cirúrgico. Novas técnicas de imagem estão sendo desenvolvidas na tentativa de obter não apenas a análise estrutural mas também funcional dos diferentes órgãos e lesões, como no caso da Perfusão por tomografia computadorizada (PTC), permitindo a quantificação da angiogenese tumoral. Foi realizado o estudo por capítulos e teve como objetivo o entendimento e aplicação da técnica de PTC; caracterização dos SAF com PTC; caracterização de formações não sarcomatosas por PTC. Dois protocolos foram utilzados para o exame de PTC, o primeiro nos casos de SAF e formações não sarcomatosas, iniciando a injeção do meio de contraste após 2-3 segundos do início da aquisição das imagens, o segundo apenas no SAF, iniciando a aquisição das imagens após a injeção do meio de contraste intravenoso. As variáveis fornecidas pelo exame de PTC foram: Perfusão, Pico de intensidade do contraste, Tempo para o pico de contraste e Volume sanguíneo, analisadas por meio de mapas coloridos de perfusão e valores numéricos, tanto das áreas intratumorais de alta perfusão quanto de um tecido muscular sadio. O exame de PTC foi viável permitindo adquirir mapas coloridos de boa qualidade e aquisição dos valores perfusionais. No estudo do SAF foi possível caracterizar seus diferentes aspectos por meio da PTC, determinando as regiões de alta perfusão intratumoral, porém não sendo possível determinar um valor comum de perfusão dos SAF. No estudo das formações não sarcomatosas, a análise estrutural por meio de TC possibilitou informações mais detalhadas dos aspectos à tomografia computadorizada das massas. Evidente diferença no valor de perfusão da única lesão maligna em relação as lesões benignas foi detectada por meio do exame de PTC. Nos trabalhos de caracterização das formações em gatos foi possível definir que formações benignas e malignas apresentam valores de perfusão superiores comparados à musculatura normal. Conclui-se que a técnica de PTC possa ser incorporada na rotina médica veterinária, permitindo acesso a dados adicionais em relação a vascularização tumoral. / The delimitation of feline injection-site sarcoma (FISS) is a challenge for surgeons, so computed tomography (CT) has a great impact on surgical planning. New imaging techniques as Perfusion Computed Tomography (PCT) have been developed to attempt not only a structural but also a functional analysis allowing the tumor angiogenesis quantification. This study was divided in chapters and had the objective of understand the technique of PCT and its, the characterization of the FISS by PCT and the characterization of non-sarcoma masses by PTC. Two protocols were used for the PTC exam, the first in cases of FISS and non-sarcoma masses, starting the injection of the contrast medium after 2-3 seconds after the beginning of the acquisition of the images, the second only in the SAF, initiating the acquisition after the intravenous injection of contrast medium. The variables provided by the PTC examination were: Perfusion, Peak contrast intensity, Time to peak contrast and Blood volume, analyzed by perfusion colored maps and perfusion numerical values, both at high perfusion intratumoral areas and normal muscle tissue. The PCT examination was feasible, allowing acquisition of perfusion colored maps with good quality and acquisition of perfusion values. In the FISS study, was possible to characterize different aspects with PCT, determining intratumoral areas of high perfusion, but it was not possible to determine a common perfusion value. In the study of non-sarcoma formations, structural analysis by CT allowed more detailed information of the aspects of the masses and a clear difference in perfusion value of the single malignant lesion in comparison to benign lesions was detected by PCT examination. In both studies it was possible to detect that either benign or malignant masses presented higher perfusion values compared to the normal musculature. In conclusion the PCT technique showed to be feasible and might be important to be incorporated into the CT protocols of soft tissue tumors in order to obtain additional data regarding tumor vascularization, allowing access to additional data regarding tumor vascularization.