Model-driven segmentation of X-ray left ventricular angiograms

X-ray left ventricular (LV) angiography is an important imaging modality to assess cardiac function. Using a contrast fluid a 2D projection of the heart is obtained. In current clinical practice cardiac function is analyzed by drawing two contours manually: one in the end diastolic (ED) phase and one in the end systolic (ES) phase. From the contours the LV volumes in these phases are calculated and the patient__s ejection fraction is assessed. Drawing these contours manually is a cumbersome and time-consuming task for a medical doctor. Furthermore, manual drawing introduces inter- and intra-observer variabilities. The focus of the research presented in this thesis was to automate the process of contour drawing in X-ray LV angiography. The developed method is based on Active Appearance Models. These statistical models, in which the cardiac shape and the cardiac appearance are modeled, have proven to be able to mimic the drawing behavior of an expert cardiologist. The clinical parameters, as determined by the automated method, showed a similar degree of accuracy as when determined by an expert. Furthermore, the required time for patient analysis was reduced considerably and the inter- and intra-observer variabilities were structurally decreased.UBL - phd migration 201

Semiautomated and automated algorithms for analysis of the carotid artery wall on computed tomography and sonography: a correlation study.

Objectives—The purpose of this study was to compare automated and semiautomated algorithms for analysis of carotid artery wall thickness and intima-media thickness on multidetector row computed tomographic (CT) angiography and sonography, respectively, and to study the correlation between them. Methods—Twenty consecutive patients underwent multidetector row CT angiographic and sonographic analysis of carotid arteries (mean age, 66 years; age range, 59–79 years). The intima-media thickness of the 40 carotid arteries was measured with novel and dedicated automated software analysis and by 4 observers who manually calculated the intima-media thickness. The carotid artery wall thickness was automatically estimated by using a specific algorithm and was also semiautomatically quantified. The correlation between groups was calculated by using the Pearson ρ statistic, and scatterplots were calculated. We evaluated intermethod agreement using Bland-Altman analysis. Results—By comparing automated carotid artery wall thickness, automated intima-media thickness, semiautomated carotid artery wall thickness, and semiautomated intima-media thickness analyses, a statistically significant association was found, with the highest values obtained for the association between semiautomated and thickness analyses(Pearson ρ = 0.9; 95% confidence interval, 0.82–0.95; P = 0.0001). The lowest values were obtained for the association between semiautomated intima-media thickness and automated carotid artery wall thickness analyses (Pearson ρ = 0.44; 95% confidence interval, 0.15–0.66; P = 0.0047). In the Bland-Altman analysis, the better results were obtained by comparing the semiautomated and automated algorithms for the study of intima-media thickness, with an interval of –16.1% to +43.6%. Conclusions—The results of this preliminary study showed that carotid artery wall thickness and intima-media thickness can be studied with automated software, although the CT analysis needs to be further improved

Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods

Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case

Computer analysis of left ventricular cineangiograms

The heart is in continuous and rhythmic motion. For centuries this unique property has fascinated clinicians and scientists, although their attempts to get a better insight in the mechanisms which govern heart wall motion were seriously hampered by the almost complete absence of effective measurement tools. A dramatic change took place in the course of this century thanks to the technological revolution which changed cardiology more than any other of the biomedical sciences. The introduction of roentgen rays into medical practice enabled physicians for the first time to observe the beating heart in the intact human being. In cases of myocardial infarction, assessed by the new electrocardiographic technique, the fluoroscopy screen revealed abnormal motion of the epicardial contour. Numerous tools and methods were designed and developed to preclude the subjectivity and inaccuracy inherent with naked eye observation of heart wall motion. However, as only the epicardial contour could be determined, the information thus obtained remained limited. In 1929 Forssman introduced a catheter into his right heart. This act set the pace for the development of angiocardiography which after a hesitating start became one of the fastest growing areas in the medical field. As a consequence of the rapidly increasing number of cardiac catheterizations paralleled by an explosive growth of the amount of data obtained during each procedure, automated data processing became indispensible. In 1967 cardiologists of Stanford University in Palo Alto, California, described their flrst experiences with the use of a computing device in the cardiac catheterization procedure. This example was soon followed by cardiologists and technicians of the Thoraxcenter who developed a computer system, tailored to their catheterization laboratory, which became operative in 1973 and comprised facilities to process pressure signals on line and under control of the operator. To pursue the automation of data processing in the catheterization laboratory two more systems had to be developed: a system to detect automatically coronary artery contours and an analysis system for left ventriculograms. For this automated detection of the left ventricular endocardial contour from cineangiograms, a hard-wired system was designed and constructed. It was called the "Contouromat'. The aim of this thesis is to describe work to evaluate and validate this tool and its applicability in clinical practice and research. The first question which arises is whether an automated angie processing system has any benefit. Indeed in some clinics left ventriculo

Angiocardiología por rayos X

La radiología permite obtener una películaradiográfica de la imagen de una parte delcuerpo humano, por su exposición a los rayosX. Cuando la radiación X atraviesa el objetobajo estudio, sufre una atenuación que depende dela densidad y el espesor del objeto. Los rayos atenuadosllegan a un a un receptor que puede ser la película fotográfica,produciendo así una imagen cuyo contraste facilitaráel diagnóstico médico. La angiografía es un procedimientoradiológico usado para observar el flujo de sangre,en cualquier órgano del cuerpo. Bajo este procedimientodestacan la angiografía cardiaca para observar las arteriascoronarias, la angiografía vascular para estudiar la irrigacióndel cerebro, y la ventriculografía, para observar la cavidadventricular. En el presente artículo, se presentan unconjunto de técnicas desarrolladas para el procesamientode imágenes adquiridas durante procedimientos de angiografíacardiaca

Algorithms and mathematical methods for extraction of functional information from magnetic resonance images of the heart

Imperial Users onl

Improvements in Cardiac Spect/CT for the Purpose of Tracking Transplanted Cells

Regenerative therapy via stem cell transplantation has received increased attention to help treat the myocardial injury associated with heart disease. Currently, the hybridisation of SPECT with X-ray CT is expanding the utility of SPECT. This thesis compared two SPECT/CT systems for attenuation correction using slow or fast-CT attenuation maps (mu-maps). We then developed a method to localize transplanted cells in relation to compromised blood flow in the myocardium following a myocardial infarction using SPECT/CT. Finally, a method to correct for image truncation was studied for a new SPECT/CT design that incorporated small field-of-view (FOV) detectors. Computer simulations compared gated-SPECT reconstructions using slow-CT and fast-CT mu-maps with gated-CT mu-maps. Using fast-CT mu-maps improved the Root Mean Squared (RMS) error from 4.2% to 4.0%. Three canine experiments were performed comparing SPECT/CT reconstruction using the Infinia/Hawkeye-4 (slow-CT) and Symbia T6 (fast-CT). Canines were euthanized prior to imaging, and then ventilated. The results showed improvements in both RMS errors and correlation coefficients for all canines. A first-pass contrast CT imaging technique can identify regions of myocardial infarction and can be fused with SPECT. Ten canines underwent surgical ligation of the left-anterior-descending artery. Cells were labeled with 111In-tropolone and transplanted into the myocardium. SPECT/CT was performed on day of transplantation, 4, and 10 days post-transplantation. For each imaging session first-pass perfusion CT was performed and successfully delineated the infarct zone. Delayed-enhanced MRI was performed and correlated well with first-pass CT. Contrast-to-noise ratios were calculated for 111In-SPECT and suggested that cells can be followed for 11 effective half-lives. We evaluated a modified SPECT/CT acquisition and reconstruction method for truncated SPECT. Cardiac SPECT/CT scans were acquired in 14 patients. The original projections were truncated to simulate a small FOV acquisition. Data was reconstructed in three ways: non-truncated and standard reconstruction (NTOSEM), which was our gold-standard; truncated and standard reconstruction (TOSEM); and truncated and a modified reconstruction (TMOSEM). Compared with NTOSEM, small FOV imaging incurred an average cardiac count ratio error greater than 100% using TOSEM and 8.9% using TMOSEM. When we plotted NTOSEM against TOSEM and TMOSEM the correlation coefficient was 0.734 and 0.996 respectively

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Progress Report No. 16

Progress report of the Biomedical Computer Laboratory, covering period 1 July 1979 to 30 June 1980

Progress Report No. 17

Progress report of the Biomedical Computer Laboratory, covering period 1 July 1980 to 30 June 1981