Eigenhearts for diagnosis of congestive heart failure (CHF)

Common cardiac diseases such as cardio-myopathy, coronary artery diseases, and valve diseases, result in abnormal myocardial movement, which could eventually lead to heart failure, also called congestive heart failure (CHF). CHF is a disease in which the heart's ability to pump blood efficiently is lost. Possible presence of this disease and location of the abnormal activity can be diagnosed from patient's scan images, by determining the wall motion abnormalities. In this paper, a new principal component analysis (PCA) technique, Eigenhearts, is presented to diagnose the abnormal contractility of heart wall. Experiments were carried out using a preliminary set of simulated scan data and the results are discussed

Neural hypernetwork approach for pulmonary embolism diagnosis

Background Hypernetworks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration of relational structure, logic and analytic dynamics. A pulmonary embolism is a blockage of the main artery of the lung or one of its branches, frequently fatal. Results Our study uses data on 28 diagnostic features of 1427 people considered to be at risk of pulmonary embolism enrolled in the Department of Internal and Subintensive Medicine of an Italian National Hospital “Ospedali Riuniti di Ancona”. Patients arrived in the department after a first screening executed by the emergency room. The resulting neural hypernetwork correctly recognized 94 % of those developing pulmonary embolism. This is better than previous results obtained with other methods (statistical selection of features, partial least squares regression, topological data analysis in a metric space). Conclusion In this work we successfully derived a new integrative approach for the analysis of partial and incomplete datasets that is based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The novelty of this method is that it does not use clinical parameters extracted by imaging analysis

Automatic detection of pulmonary embolism using computational intelligence.

Student Number : 0418382M - MSc(Eng) dissertation - School of Electrical Engineering and Information Technology - Faculty of Engineering and the Built EnvironmentPulmonary embolism (PE) is a potentially fatal, yet potentially treatable condition. The problem of diagnosing PE with any degree of confidence arises from the nonspecific nature of the symptoms. In difficult cases, multiple tests will need to be performed on a patient before an accurate diagnosis can be made. These tests include Ventilation-Perfusion (V/Q) scanning, Spiral CT, leg ultrasound and d- Dimer testing. The aim of this research is to test the performance of using neural networks, namely Bayesian Neural Networks, to make a diagnosis based on available information. The information contains of a set of 12 V/Q scans which have been processed, and from which features have been extracted to provide inputs to the neural network. This system will act as a second opinion, and is not intended to replace an experienced clinician. The V/Q scans are analysed using image processing techniques in order to segment the lung from the background image and to determine if any abnormalities are present in the lung. The system must be able to discriminate between a genuine case of PE and of other diseases showing similar symptoms such as tuberculosis and parenchymal lung disease. Relevant features to be used in classification were then extracted from the images. The goal of this system is to make use of Bayesian neural networks. Using Bayesian networks, confidence levels can be calculated for each prediction the network makes. This makes them more informative than traditional multi layer perceptron (MLP) networks. The V/Q scans themselves are greyscale images of [256x256] size. In order to reduce redundancy and increase computational speed, Principal Component Analysis (PCA) is used to obtain the most significant information in each of the scans. Usually the Gold Standard for such a system would be pulmonary angiography, but in this case the Bayesian MLP (BMLP) is trained based on diagnosis by an experienced nuclear medicine physician. The system will be used to look at new cases for which the accuracy of the system can be established. Results showed good training performance, while validation performance was reasonable. Intermediate cases proved to be the most difficult to diagnose correctly

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

A Heterogeneous Patient-Specific Biomechanical Model of the Lung for Tumor Motion Compensation and Effective Lung Radiation Therapy Planning

Radiation therapy is a main component of treatment for many lung cancer patients. However, the respiratory motion can cause inaccuracies in radiation delivery that can lead to treatment complications. In addition, the radiation-induced damage to healthy tissue limits the effectiveness of radiation treatment. Motion management methods have been developed to increase the accuracy of radiation delivery, and functional avoidance treatment planning has emerged to help reduce the chances of radiation-induced toxicity. In this work, we have developed biomechanical model-based techniques for tumor motion estimation, as well as lung functional imaging. The proposed biomechanical model accurately estimates lung and tumor motion/deformation by mimicking the physiology of respiration, while accounting for heterogeneous changes in the lung mechanics caused by COPD, a common lung cancer comorbidity. A biomechanics-based image registration algorithm is developed and is combined with an air segmentation algorithm to develop a 4DCT-based ventilation imaging technique, with potential applications in functional avoidance therapies

Imaging Biomarkers of Pulmonary Structure and Function

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airflow limitations resulting from airway obstruction and/or tissue destruction. The diagnosis and monitoring of these pulmonary diseases is primarily performed using spirometry, specifically the forced expiratory volume in one second (FEV1), which measures global airflow obstruction and provides no regional information of the different underlying disease pathologies. The limitations of spirometry and current therapies for lung disease patients have motivated the development of pulmonary imaging approaches, such as computed tomography (CT) and magnetic resonance imaging (MRI). Inhaled hyperpolarized noble gas MRI, specifically using helium-3 (3He) and xenon-129 (129Xe) gases, provides a way to quantify pulmonary ventilation by visualizing lung regions accessed by gas during a breath-hold, and alternatively, regions that are not accessed - coined “ventilation defects.” Despite the strong foundation and many advantages hyperpolarized 3He MRI has to offer research and patient care, clinical translation has been inhibited in part due to the cost and need for specialized equipment, including multinuclear-MR hardware and polarizers, and personnel. Accordingly, our objective was to develop and evaluate imaging biomarkers of pulmonary structure and function using MRI and CT without the use of exogenous contrast agents or specialized equipment. First, we developed and compared CT parametric response maps (PRM) with 3He MR ventilation images in measuring gas-trapping and emphysema in ex-smokers with and without COPD. We observed that in mild-moderate COPD, 3He MR ventilation abnormalities were related to PRM gas-trapping whereas in severe COPD, ventilation abnormalities correlated with both PRM gas-trapping and PRM emphysema. We then developed and compared pulmonary ventilation abnormalities derived from Fourier decomposition of free-breathing proton (1H) MRI (FDMRI) with 3He MRI in subjects with COPD and bronchiectasis. This work demonstrated that FDMRI and 3He MRI ventilation defects were strongly related in COPD, but not in bronchiectasis subjects. In COPD only, FDMRI ventilation defects were spatially related with 3He MRI ventilation defects and emphysema. Based on the FDMRI biomarkers developed in patients with COPD and bronchiectasis, we then evaluated ventilation heterogeneity in patients with severe asthma, both pre- and post-salbutamol as well as post-methacholine challenge, using FDMRI and 3He MRI. FDMRI free-breathing ventilation abnormalities were correlated with but under-estimated 3He MRI static ventilation defects. Finally, based on the previously developed free-breathing MRI approach, we developed a whole-lung free-breathing pulmonary 1H MRI technique to measure regional specific-ventilation and evaluated both asthmatics and healthy volunteers. These measurements not only provided similar information as specific-ventilation measured using plethysmography, but also information about regional ventilation defects that were correlated with 3He MRI ventilation abnormalities. These results demonstrated that whole-lung free-breathing 1H MRI biomarker of specific-ventilation may reflect ventilation heterogeneity and/or gas-trapping in asthma. These important findings indicate that imaging biomarkers of pulmonary structure and function using MRI and CT have the potential to regionally reveal the different pathologies in COPD and asthma without the use of exogenous contrast agents. The development and validation of these clinically meaningful imaging biomarkers are critically required to accelerate pulmonary imaging translation from the research workbench to being a part of the clinical workflow, with the overall goal to improve patient outcomes

Multi-Nuclear Magnetic Resonance Imaging of Obstructive Lung Disease

Obstructive lung diseases such as chronic-obstructive-lung-disease (COPD), bronchiectasis, and asthma are characterized by airflow obstruction. They affect over six million Canadians costing the economy $12 billion/year. Despite decades of research, therapies that modify obstructive-lung-disease progression and control are lacking because patient diagnosis, monitoring, and response to therapy are currently made using airflow measurements that may conceal the independent contributions of underlying pathologies. One goal of obstructive-lung-disease research is to develop ways to identify patients with specific underlying pathological phenotypes to improve patient care and outcomes. Thoracic computed-tomography (CT) and magnetic-resonance-imaging (MRI) provide ways to regionally identify the underlying pathologies associated with obstructive-lung-disease, and offer quantitative biomarkers of obstructive-lung-disease (e.g. lung-density, airway dimensions, ventilation abnormalities, and lung microstructure). As the first step to identify patients with specific underlying pathological phenotypes, it is important to understand the physiological and clinical consequences of these imaging derived measurements. Accordingly, our objective was to evaluate lung structure and function using multi-nuclear pulmonary MRI in aging and obstructive-lung-disease to provide a better understanding of MR-derived biomarkers. In older never-smokers, the majority of subjects had 3He MR ventilation abnormalities that were not responsive to bronchodilation. 3He ventilation abnormalities were related to airflow obstruction and airways resistance, but not occupational exposure or exercise limitation. We then developed and evaluated ultra-short-echo-time MRI in COPD subjects with and without bronchiectasis. This work demonstrated that ultra-short-echo-time MR-derived measurements were reproducible and significantly related to CT tissue-density measurements. In the COPD subjects with bronchiectasis, ultra-short-echo-time signal-intensity was related to airway measurements. In COPD subjects without bronchiectasis, ultra-short-echo-time signal-intensity was related to the severity of emphysema. Finally, based on the ultra-short-echo-time MR biomarkers developed in patients with COPD and bronchiectasis, patients that share some of the airway and inflammatory features common in asthmatics, we produced ultra-short-echo-time MR measurements in asthma. These measurements not only provided similar information as CT, but also information about regional ventilation deficits. These results demonstrated that ultra-short-echo-time MR biomarkers may reflect ventilation heterogeneity and/or gas-trapping in asthma. These important findings indicate that multi-nuclear pulmonary MRI has the potential to quantitatively evaluate the different pathologies of obstructive-lung-disease

Functional pulmonary MRI with ultra-fast steady-state free precession

To date, computed tomography and nuclear medicine techniques are still the reference standard for lung imaging, but radiation exposure is a major concern; especially in case of longitudinal examinations and in children. Therefore, radiation-free imaging is an urgent necessity. Pulmonary magnetic resonance imaging (MRI) is radiation-free, but poses challenges since the low proton density and the presence of strong mesoscopic susceptibility variations considerably reduce the detectable MR signal. As a result, the lung typically appears as a “black hole” with conventional MRI techniques. Recently, ultra-fast balanced steady-state free precession (ufSSFP) methods were proposed for ameliorated lung morphological imaging. In this thesis, ufSSFP is employed to develop and improve several pulmonary functional imaging methods, which can be used in clinical settings using standard MR scanners and equipment. At every breath, the lung expands and contracts, and at every heartbeat, the blood is pumped through the arteries to reach the lung parenchyma. This creates signal modulations associated with pulmonary blood perfusion and ventilation that are detectable by MRI. The second chapter of this thesis focuses on the optimization of time-resolved two-dimensional (2D) ufSSFP for perfusion-weighted and ventilation-weighted imaging of the lung. Subsequently, in the third chapter, three-dimensional (3D) multi-volumetric ufSSFP breath-hold imaging is used to develop a lung model and retrieve the measure α, a novel ventilation-weighted quantitative parameter. Oxygen-enhanced MRI exploits the paramagnetic properties of oxygen dissolved in the blood, acting as a weak T1-shortening contrast agent. When breathing pure oxygen, it reaches only ventilated alveoli of the parenchyma and dissolves only in functional and perfused regions. How ufSSFP imaging in combination with a lung model can be used to calculate robust 3D oxygen enhancement maps is described in the fourth chapter. In addition, in the fifth chapter, 2D inversion recovery ufSSFP imaging is employed to map the T1 and T2 relaxation times of the lung, the change of the relaxation times after hyperoxic conditions, as well as the physiological oxygen wash-in and wash-out time (related to the time needed to shorten T1 after oxygen breathing). The objective of the last chapter of this thesis is the application of 3D ufSSFP imaging before and after intravenous gadolinium-based contrast agent administration for the investigation of signal enhancement ratio (SER) mapping: a rapid technique to visualize perfusion-related diseases of the lung parenchyma. The techniques presented in this thesis using optimized ufSSFP pulse sequences demonstrated potential to reveal new insights on pulmonary function as well as quantification, and might become part of the future standard for the evaluation and follow-up of several lung pathologies