Pilvo aortos vietos nustatymas krūtinės ląstos tomografinėje nuotraukoje

Kompiuterinė tomografija, kuri naudojama pilvo aortos aneurizmos diagnostikai ir stebėjimui, leidžia vartotojams stebėti aneurizmos būseną paciento kūno skerspjūvio nuotraukų sekoje. Dažnai kompiuterine tomografija grįstiems diagnostikos įrankiams aortos vieta turi būti nurodoma vartotojo. Darbe aprašomas pilvo aortos vidinių taškų identifikavimo būdas be pradinės aortos vietos išankstinio žymėjimo. Darbe sprendžiami uždaviniai: medicinos vaizdų analizės metodų apžvalga bei parinkimas, pirminis vaizdų apdorojimas pašalinant triukšmą bei išskiriant vaizde esančių objektų kontūrus, aortos aptikimo metodo pasiūlymas. Taip pat pasiūlomas aortos trombo aptikimo metodas, kuriam vartotojas turi pateikti sekos vaizdą, kuriame trombas yra matomas, bet jam nereikia nurodyti tikslios ar apytikslės trombo vietos

A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified

Multidimensional image analysis of cardiac function in MRI

Cardiac morphology is a key indicator of cardiac health. Important metrics that are currently in clinical use are left-ventricle cardiac ejection fraction, cardiac muscle (myocardium) mass, myocardium thickness and myocardium thickening over the cardiac cycle. Advances in imaging technologies have led to an increase in temporal and spatial resolution. Such an increase in data presents a laborious task for medical practitioners to analyse. In this thesis, measurement of the cardiac left-ventricle function is achieved by developing novel methods for the automatic segmentation of the left-ventricle blood-pool and the left ventricle myocardium boundaries. A preliminary challenge faced in this task is the removal of noise from Magnetic Resonance Imaging (MRI) data, which is addressed by using advanced data filtering procedures. Two mechanisms for left-ventricle segmentation are employed. Firstly segmentation of the left ventricle blood-pool for the measurement of ejection fraction is undertaken in the signal intensity domain. Utilising the high discrimination between blood and tissue, a novel methodology based on a statistical partitioning method offers success in localising and segmenting the blood pool of the left ventricle. From this initialisation, the estimation of the outer wall (epi-cardium) of the left ventricle can be achieved using gradient information and prior knowledge. Secondly, a more involved method for extracting the myocardium of the leftventricle is developed, that can better perform segmentation in higher dimensions. Spatial information is incorporated in the segmentation by employing a gradient-based boundary evolution. A level-set scheme is implemented and a novel formulation for the extraction of the cardiac muscle is introduced. Two surfaces, representing the inner and the outer boundaries of the left-ventricle, are simultaneously evolved using a coupling function and supervised with a probabilistic model of expertly assisted manual segmentations

Pattern recognition methods applied to medical imaging: lung nodule detection in computed tomography images

Lung cancer is one of the main public health issues in developed countries. The overall 5-year survival rate is only 10−16%, although the mortality rate among men in the United States has started to decrease by about 1.5% per year since 1991 and a similar trend for the male population has been observed in most European countries. By contrast, in the case of the female population, the survival rate is still decreasing, despite a decline in the mortality of young women has been ob- served over the last decade. Approximately 70% of lung cancers are diagnosed at too advanced stages for the treatments to be effective. The five-year survival rate for early-stage lung cancers (stage I), which can reach 70%, is sensibly higher than for cancers diagnosed at more advanced stages. Lung cancer most commonly manifests itself as non-calcified pulmonary nodules. The CT has been shown as the most sensitive imaging modality for the detection of small pulmonary nodules, particularly since the introduction of the multi-detector-row and helical CT technologies. Screening programs based on Low Dose Computed Tomography (LDCT) may be regarded as a promising technique for detecting small, early-stage lung cancers. The efficacy of screening programs based on CT in reducing the mortality rate for lung cancer has not been fully demonstrated yet, and different and opposing opinions are being pointed out on this topic by many experts. However, the recent results obtained by the National Lung Screening Trial (NLST), involving 53454 high risk patients, show a 20% reduction of mortality when the screening program was carried out with the helical CT, rather than with a conventional chest X-ray. LDCT settings are currently recommended by the screening trial protocols. However, it is not trivial in this case to identify small pulmonary nodules,due to the noisier appearance of the images in low-dose CT with respect to the standard-dose CT. Moreover, thin slices are generally used in screening programs, thus originating datasets of about 300 − 400 slices per study. De- pending on the screening trial protocol they joined, radiologists can be asked to identify even very small lung nodules, which is a very difficult and time- consuming task. Lung nodules are rather spherical objects, characterized by very low CT values and/or low contrast. Nodules may have CT values in the same range of those of blood vessels, airway walls, pleura and may be strongly connected to them. It has been demonstrated, that a large percent- age of nodules (20 − 35%) is actually missed in screening diagnoses. To support radiologists in the identification of early-stage pathological objects, about one decade ago, researchers started to develop CAD methods to be applied to CT examinations. Within this framework, two CAD sub-systems are proposed: CAD for internal nodules (CADI), devoted to the identification of small nodules embedded in the lung parenchyma, i.e. Internal Nodules (INs) and CADJP, devoted the identification of nodules originating on the pleura surface, i.e. Juxta-Pleural Nodules (JPNs) respectively. As the training and validation sets may drastically influence the performance of a CAD system, the presented approaches have been trained, developed and tested on different datasets of CT scans (Lung Image Database Consortium (LIDC), ITALUNG − CT) and finally blindly validated on the ANODE09 dataset. The two CAD sub-systems are implemented in the ITK framework, an open source C++ framework for segmentation and registration of medical im- ages, and the rendering of the obtained results are achieved using VTK, a freely available software system for 3D computer graphics, image processing and visualization. The Support Vector Machines (SVMs) are implemented in SVMLight. The two proposed approaches have been developed to detect solid nodules, since the number of Ground Glass Opacity (GGO) contained in the available datasets has been considered too low. This thesis is structured as follows: in the first chapter the basic concepts about CT and lung anatomy are explained. The second chapter deals with CAD systems and their evaluation methods. In the third chapter the datasets used for this work are described. In chapter 4 the lung segmentation algorithm is explained in details, and in chapter 5 and 6 the algorithms to detect internal and juxta-pleural candidates are discussed. In chapter 7 the reduction of false positives findings is explained. In chapter 8 results of the train and validation sessions are shown. Finally in the last chapter the conclusions are drawn

Shape/image registration for medical imaging : novel algorithms and applications.

This dissertation looks at two different categories of the registration approaches: Shape registration, and Image registration. It also considers the applications of these approaches into the medical imaging field. Shape registration is an important problem in computer vision, computer graphics and medical imaging. It has been handled in different manners in many applications like shapebased segmentation, shape recognition, and tracking. Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. Many image processing applications like remote sensing, fusion of medical images, and computer-aided surgery need image registration. This study deals with two different applications in the field of medical image analysis. The first one is related to shape-based segmentation of the human vertebral bodies (VBs). The vertebra consists of the VB, spinous, and other anatomical regions. Spinous pedicles, and ribs should not be included in the bone mineral density (BMD) measurements. The VB segmentation is not an easy task since the ribs have similar gray level information. This dissertation investigates two different segmentation approaches. Both of them are obeying the variational shape-based segmentation frameworks. The first approach deals with two dimensional (2D) case. This segmentation approach starts with obtaining the initial segmentation using the intensity/spatial interaction models. Then, shape model is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of an energy functional which integrating the shape model with the intensity information. The second one is a 3D simultaneous segmentation and registration approach. The information of the intensity is handled by embedding a Willmore flow into the level set segmentation framework. Then the shape variations are estimated using a new distance probabilistic model. The experimental results show that the segmentation accuracy of the framework are much higher than other alternatives. Applications on BMD measurements of vertebral body are given to illustrate the accuracy of the proposed segmentation approach. The second application is related to the field of computer-aided surgery, specifically on ankle fusion surgery. The long-term goal of this work is to apply this technique to ankle fusion surgery to determine the proper size and orientation of the screws that are used for fusing the bones together. In addition, we try to localize the best bone region to fix these screws. To achieve these goals, the 2D-3D registration is introduced. The role of 2D-3D registration is to enhance the quality of the surgical procedure in terms of time and accuracy, and would greatly reduce the need for repeated surgeries; thus, saving the patients time, expense, and trauma

Multimodal breast imaging: Registration, visualization, and image synthesis

The benefit of registration and fusion of functional images with anatomical images is well appreciated in the advent of combined positron emission tomography and x-ray computed tomography scanners (PET/CT). This is especially true in breast cancer imaging, where modalities such as high-resolution and dynamic contrast-enhanced magnetic resonance imaging (MRI) and F-18-FDG positron emission tomography (PET) have steadily gained acceptance in addition to x-ray mammography, the primary detection tool. The increased interest in combined PET/MRI images has facilitated the demand for appropriate registration and fusion algorithms. A new approach to MRI-to-PET non-rigid breast image registration was developed and evaluated based on the location of a small number of fiducial skin markers (FSMs) visible in both modalities. The observed FSM displacement vectors between MRI and PET, distributed piecewise linearly over the breast volume, produce a deformed Finite-Element mesh that reasonably approximates non-rigid deformation of the breast tissue between the MRI and PET scans. The method does not require a biomechanical breast tissue model, and is robust and fast. The method was evaluated both qualitatively and quantitatively on patients and a deformable breast phantom. The procedure yields quality images with average target registration error (TRE) below 4 mm. The importance of appropriately jointly displaying (i.e. fusing) the registered images has often been neglected and underestimated. A combined MRI/PET image has the benefits of directly showing the spatial relationships between the two modalities, increasing the sensitivity, specificity, and accuracy of diagnosis. Additional information on morphology and on dynamic behavior of the suspicious lesion can be provided, allowing more accurate lesion localization including mapping of hyper- and hypo-metabolic regions as well as better lesion-boundary definition, improving accuracy when grading the breast cancer and assessing the need for biopsy. Eight promising fusion-for-visualization techniques were evaluated by radiologists from University Hospital, in Syracuse, NY. Preliminary results indicate that the radiologists were better able to perform a series of tasks when reading the fused PET/MRI data sets using color tables generated by a newly developed genetic algorithm, as compared to other commonly used schemes. The lack of a known ground truth hinders the development and evaluation of new algorithms for tasks such as registration and classification. A preliminary mesh-based breast phantom containing 12 distinct tissue classes along with tissue properties necessary for the simulation of dynamic positron emission tomography scans was created. The phantom contains multiple components which can be separately manipulated, utilizing geometric transformations, to represent populations or a single individual being imaged in multiple positions. This phantom will support future multimodal breast imaging work

Novel 3D Ultrasound Elastography Techniques for In Vivo Breast Tumor Imaging and Nonlinear Characterization

Breast cancer comprises about 29% of all types of cancer in women worldwide. This type of cancer caused what is equivalent to 14% of all female deaths due to cancer. Nowadays, tissue biopsy is routinely performed, although about 80% of the performed biopsies yield a benign result. Biopsy is considered the most costly part of breast cancer examination and invasive in nature. To reduce unnecessary biopsy procedures and achieve early diagnosis, ultrasound elastography was proposed.;In this research, tissue displacement fields were estimated using ultrasound waves, and used to infer the elastic properties of tissues. Ultrasound radiofrequency data acquired at consecutive increments of tissue compression were used to compute local tissue strains using a cross correlation method. In vitro and in vivo experiments were conducted on different tissue types to demonstrate the ability to construct 2D and 3D elastography that helps distinguish stiff from soft tissues. Based on the constructed strain volumes, a novel nonlinear classification method for human breast tumors is introduced. Multi-compression elastography imaging is elucidated in this study to differentiate malignant from benign tumors, based on their nonlinear mechanical behavior under compression. A pilot study on ten patients was performed in vivo, and classification results were compared with biopsy diagnosis - the gold standard. Various nonlinear parameters based on different models, were evaluated and compared with two commonly used parameters; relative stiffness and relative tumor size. Moreover, different types of strain components were constructed in 3D for strain imaging, including normal axial, first principal, maximum shear and Von Mises strains. Interactive segmentation algorithms were also evaluated and applied on the constructed volumes, to delineate the stiff tissue by showing its isolated 3D shape.;Elastography 3D imaging results were in good agreement with the biopsy outcomes, where the new classification method showed a degree of discrepancy between benign and malignant tumors better than the commonly used parameters. The results show that the nonlinear parameters were found to be statistically significant with p-value \u3c0.05. Moreover, one parameter; power-law exponent, was highly statistically significant having p-value \u3c 0.001. Additionally, volumetric strain images reconstructed using the maximum shear strains provided an enhanced tumor\u27s boundary from the surrounding soft tissues. This edge enhancement improved the overall segmentation performance, and diminished the boundary leakage effect. 3D segmentation provided an additional reliable means to determine the tumor\u27s size by estimating its volume.;In summary, the proposed elastographic techniques can help predetermine the tumor\u27s type, shape and size that are considered key features helping the physician to decide the sort and extent of the treatment. The methods can also be extended to diagnose other types of tumors, such as prostate and cervical tumors. This research is aimed toward the development of a novel \u27virtual biopsy\u27 method that may reduce the number of unnecessary painful biopsies, and diminish the increasingly risk of cancer