Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Navigation system based in motion tracking sensor for percutaneous renal access

Tese de Doutoramento em Engenharia BiomédicaMinimally-invasive kidney interventions are daily performed to diagnose and treat several renal diseases. Percutaneous renal access (PRA) is an essential but challenging stage for most of these procedures, since its outcome is directly linked to the physician’s ability to precisely visualize and reach the anatomical target. Nowadays, PRA is always guided with medical imaging assistance, most frequently using X-ray based imaging (e.g. fluoroscopy). Thus, radiation on the surgical theater represents a major risk to the medical team, where its exclusion from PRA has a direct impact diminishing the dose exposure on both patients and physicians. To solve the referred problems this thesis aims to develop a new hardware/software framework to intuitively and safely guide the surgeon during PRA planning and puncturing. In terms of surgical planning, a set of methodologies were developed to increase the certainty of reaching a specific target inside the kidney. The most relevant abdominal structures for PRA were automatically clustered into different 3D volumes. For that, primitive volumes were merged as a local optimization problem using the minimum description length principle and image statistical properties. A multi-volume Ray Cast method was then used to highlight each segmented volume. Results show that it is possible to detect all abdominal structures surrounding the kidney, with the ability to correctly estimate a virtual trajectory. Concerning the percutaneous puncturing stage, either an electromagnetic or optical solution were developed and tested in multiple in vitro, in vivo and ex vivo trials. The optical tracking solution aids in establishing the desired puncture site and choosing the best virtual puncture trajectory. However, this system required a line of sight to different optical markers placed at the needle base, limiting the accuracy when tracking inside the human body. Results show that the needle tip can deflect from its initial straight line trajectory with an error higher than 3 mm. Moreover, a complex registration procedure and initial setup is needed. On the other hand, a real-time electromagnetic tracking was developed. Hereto, a catheter was inserted trans-urethrally towards the renal target. This catheter has a position and orientation electromagnetic sensor on its tip that function as a real-time target locator. Then, a needle integrating a similar sensor is used. From the data provided by both sensors, one computes a virtual puncture trajectory, which is displayed in a 3D visualization software. In vivo tests showed a median renal and ureteral puncture times of 19 and 51 seconds, respectively (range 14 to 45 and 45 to 67 seconds). Such results represent a puncture time improvement between 75% and 85% when comparing to state of the art methods. 3D sound and vibrotactile feedback were also developed to provide additional information about the needle orientation. By using these kind of feedback, it was verified that the surgeon tends to follow a virtual puncture trajectory with a reduced amount of deviations from the ideal trajectory, being able to anticipate any movement even without looking to a monitor. Best results show that 3D sound sources were correctly identified 79.2 ± 8.1% of times with an average angulation error of 10.4º degrees. Vibration sources were accurately identified 91.1 ± 3.6% of times with an average angulation error of 8.0º degrees. Additionally to the EMT framework, three circular ultrasound transducers were built with a needle working channel. One explored different manufacture fabrication setups in terms of the piezoelectric materials, transducer construction, single vs. multi array configurations, backing and matching material design. The A-scan signals retrieved from each transducer were filtered and processed to automatically detect reflected echoes and to alert the surgeon when undesirable anatomical structures are in between the puncture path. The transducers were mapped in a water tank and tested in a study involving 45 phantoms. Results showed that the beam cross-sectional area oscillates around the ceramics radius and it was possible to automatically detect echo signals in phantoms with length higher than 80 mm. Hereupon, it is expected that the introduction of the proposed system on the PRA procedure, will allow to guide the surgeon through the optimal path towards the precise kidney target, increasing surgeon’s confidence and reducing complications (e.g. organ perforation) during PRA. Moreover, the developed framework has the potential to make the PRA free of radiation for both patient and surgeon and to broad the use of PRA to less specialized surgeons.Intervenções renais minimamente invasivas são realizadas diariamente para o tratamento e diagnóstico de várias doenças renais. O acesso renal percutâneo (ARP) é uma etapa essencial e desafiante na maior parte destes procedimentos. O seu resultado encontra-se diretamente relacionado com a capacidade do cirurgião visualizar e atingir com precisão o alvo anatómico. Hoje em dia, o ARP é sempre guiado com recurso a sistemas imagiológicos, na maior parte das vezes baseados em raios-X (p.e. a fluoroscopia). A radiação destes sistemas nas salas cirúrgicas representa um grande risco para a equipa médica, aonde a sua remoção levará a um impacto direto na diminuição da dose exposta aos pacientes e cirurgiões. De modo a resolver os problemas existentes, esta tese tem como objetivo o desenvolvimento de uma framework de hardware/software que permita, de forma intuitiva e segura, guiar o cirurgião durante o planeamento e punção do ARP. Em termos de planeamento, foi desenvolvido um conjunto de metodologias de modo a aumentar a eficácia com que o alvo anatómico é alcançado. As estruturas abdominais mais relevantes para o procedimento de ARP, foram automaticamente agrupadas em volumes 3D, através de um problema de optimização global com base no princípio de “minimum description length” e propriedades estatísticas da imagem. Por fim, um procedimento de Ray Cast, com múltiplas funções de transferência, foi utilizado para enfatizar as estruturas segmentadas. Os resultados mostram que é possível detetar todas as estruturas abdominais envolventes ao rim, com a capacidade para estimar corretamente uma trajetória virtual. No que diz respeito à fase de punção percutânea, foram testadas duas soluções de deteção de movimento (ótica e eletromagnética) em múltiplos ensaios in vitro, in vivo e ex vivo. A solução baseada em sensores óticos ajudou no cálculo do melhor ponto de punção e na definição da melhor trajetória a seguir. Contudo, este sistema necessita de uma linha de visão com diferentes marcadores óticos acoplados à base da agulha, limitando a precisão com que a agulha é detetada no interior do corpo humano. Os resultados indicam que a agulha pode sofrer deflexões à medida que vai sendo inserida, com erros superiores a 3 mm. Por outro lado, foi desenvolvida e testada uma solução com base em sensores eletromagnéticos. Para tal, um cateter que integra um sensor de posição e orientação na sua ponta, foi colocado por via trans-uretral junto do alvo renal. De seguida, uma agulha, integrando um sensor semelhante, é utilizada para a punção percutânea. A partir da diferença espacial de ambos os sensores, é possível gerar uma trajetória de punção virtual. A mediana do tempo necessário para puncionar o rim e ureter, segundo esta trajetória, foi de 19 e 51 segundos, respetivamente (variações de 14 a 45 e 45 a 67 segundos). Estes resultados representam uma melhoria do tempo de punção entre 75% e 85%, quando comparados com o estado da arte dos métodos atuais. Além do feedback visual, som 3D e feedback vibratório foram explorados de modo a fornecer informações complementares da posição da agulha. Verificou-se que com este tipo de feedback, o cirurgião tende a seguir uma trajetória de punção com desvios mínimos, sendo igualmente capaz de antecipar qualquer movimento, mesmo sem olhar para o monitor. Fontes de som e vibração podem ser corretamente detetadas em 79,2 ± 8,1% e 91,1 ± 3,6%, com erros médios de angulação de 10.4º e 8.0 graus, respetivamente. Adicionalmente ao sistema de navegação, foram também produzidos três transdutores de ultrassom circulares com um canal de trabalho para a agulha. Para tal, foram exploradas diferentes configurações de fabricação em termos de materiais piezoelétricos, transdutores multi-array ou singulares e espessura/material de layers de suporte. Os sinais originados em cada transdutor foram filtrados e processados de modo a detetar de forma automática os ecos refletidos, e assim, alertar o cirurgião quando existem variações anatómicas ao longo do caminho de punção. Os transdutores foram mapeados num tanque de água e testados em 45 phantoms. Os resultados mostraram que o feixe de área em corte transversal oscila em torno do raio de cerâmica, e que os ecos refletidos são detetados em phantoms com comprimentos superiores a 80 mm. Desta forma, é expectável que a introdução deste novo sistema a nível do ARP permitirá conduzir o cirurgião ao longo do caminho de punção ideal, aumentado a confiança do cirurgião e reduzindo possíveis complicações (p.e. a perfuração dos órgãos). Além disso, de realçar que este sistema apresenta o potencial de tornar o ARP livre de radiação e alarga-lo a cirurgiões menos especializados.The present work was only possible thanks to the support by the Portuguese Science and Technology Foundation through the PhD grant with reference SFRH/BD/74276/2010 funded by FCT/MEC (PIDDAC) and by Fundo Europeu de Desenvolvimento Regional (FEDER), Programa COMPETE - Programa Operacional Factores de Competitividade (POFC) do QREN

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

The state-of-the-art in ultrasound-guided spine interventions.

During the last two decades, intra-operative ultrasound (iUS) imaging has been employed for various surgical procedures of the spine, including spinal fusion and needle injections. Accurate and efficient registration of pre-operative computed tomography or magnetic resonance images with iUS images are key elements in the success of iUS-based spine navigation. While widely investigated in research, iUS-based spine navigation has not yet been established in the clinic. This is due to several factors including the lack of a standard methodology for the assessment of accuracy, robustness, reliability, and usability of the registration method. To address these issues, we present a systematic review of the state-of-the-art techniques for iUS-guided registration in spinal image-guided surgery (IGS). The review follows a new taxonomy based on the four steps involved in the surgical workflow that include pre-processing, registration initialization, estimation of the required patient to image transformation, and a visualization process. We provide a detailed analysis of the measurements in terms of accuracy, robustness, reliability, and usability that need to be met during the evaluation of a spinal IGS framework. Although this review is focused on spinal navigation, we expect similar evaluation criteria to be relevant for other IGS applications

Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach

Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of many adults. It a ects almost 1:5 - 5% of the general population. Sub- Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of morbidity and mortality. Therefore, radiologists aim to detect it and diagnose it at an early stage, by analyzing the medical images, to prevent or reduce its damages. The analysis process is traditionally done manually. However, with the emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are adopted in the clinics to overcome the traditional process disadvantages, as the dependency of the radiologist's experience, the inter and intra observation variability, the increase in the probability of error which increases consequently with the growing number of medical images to be analyzed, and the artifacts added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA, etc.) which impedes the radiologist' s work. Due to the aforementioned reasons, many research works propose di erent segmentation approaches to automate the analysis process of detecting a CA using complementary segmentation techniques; but due to the challenging task of developing a robust reproducible reliable algorithm to detect CA regardless of its shape, size, and location from a variety of the acquisition methods, a diversity of proposed and developed approaches exist which still su er from some limitations. This thesis aims to contribute in this research area by adopting two promising techniques based on the multiresolution and statistical approaches in the Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform (CT), which empowers the segmentation by extracting features not apparent in the normal image scale. While the second technique is the Hidden Markov Random Field model with Expectation Maximization (HMRF-EM), which segments the image based on the relationship of the neighboring pixels in the contourlet domain. The developed algorithm reveals promising results on the four tested Three- Dimensional Rotational Angiography (3D RA) datasets, where an objective and a subjective evaluation are carried out. For the objective evaluation, six performance metrics are adopted which are: accuracy, Dice Similarity Index (DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city, and sensitivity. As for the subjective evaluation, one expert and four observers with some medical background are involved to assess the segmentation visually. Both evaluations compare the segmented volumes against the ground truth data

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis