Detection of Pulmonary Embolism: Workflow Architecture and Comparative Analysis of the CNN Models

Machine learning has proven to be a practical medical image processing technique for pattern discovery in low-quality labelled and unlabeled datasets. Deep vein thrombosis and pulmonary embolism are both examples of venous thromboembolism, which is a key factor in patient mortality and necessitates prompt diagnosis by experts. An immediate diagnosis and course of treatment are necessary for the life-threatening cardiovascular condition known as pulmonary embolism (PE). In the study of medical imaging, especially the identification of PE, machine learning (ML) algorithms have produced encouraging results. This study's objective is to assess how well machine learning (ML) algorithms perform in identifying PE in computed tomography (CT) scans. A range of ML approaches were used to the dataset, including deep learning algorithms such as convolutional neural networks. The effectiveness of PE detection systems can be greatly enhanced by the use of cutting-edge methodologies like deep learning, which lowers the possibility of incorrect diagnoses and enables the quick administration of therapy to individuals who require it. This work contributes to the growing body of evidence that supports the use of ML in medical imaging and diagnosis. Future research should examine how these algorithms might be included into clinical workflows, resolving any potential implementation challenges, and making sure their adoption is done so in a secure and efficient way. In this study, we provide a thorough evaluation of three different models: the streamlined architecture MobileNetV2 with an accuracy of 96%, compared to other models like the Xception model with an accuracy of 91%, and the Efficientnet B5 model with an accuracy of 97%, after observation and process following

Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation

Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a 3D image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and 94% . Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

Automation Process for Morphometric Analysis of Volumetric CT Data from Pulmonary Vasculature in Rats

With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention

Imaging in pulmonary hypertension: the role of MR and CT

Pulmonary hypertension (PH) is a debilitating disease with many causes that has a significant impact on quality of life and results in premature death. Until recently imaging has only played an adjunctive role to primary diagnostic modalities such as echocardiography and right heart catheterization in identifying these patients. The advent of newer imaging techniques and developments in hardware has opened up a new scope for imaging. CT offers excellent structural detail while MRI provides superb functional information without the risk of radiation. These modalities now offer a robust and in-depth diagnostic approach for the investigation of patients with suspected pulmonary hypertension. This document explores the role of MR and CT imaging methods in investigating patients with pulmonary vascular disease and different aspect of lung disease. In particular, subgroups of pulmonary hypertension associated with unique morphological changes have been closely scrutinized. In this work the value of MR angiography in patients suspected with chronic thromboembolic pulmonary hypertension or unexplained PH has been explored and in the same subgroup of patients, the role of 3D MR lung perfusion as a diagnostic tool has also been demonstrated. This research has also shown that the thoracic CT offers valuable prognostic information and imaging characteristics in patients with each of the major subcategories of pulmonary arterial hypertension. Furthermore, the diagnostic accuracy and prognostic significance of MR and CT indices for the detection of PH in patients with connective tissue disease associated with PH has been highlighted. Finally, the feasibility and diagnostic quality of MRI to identify structural parenchymal lung changes have also been analysed and this study demonstrates the potential clinical utility of imaging high risk patients with MRI in longitudinal studies thereby avoiding the hazards of radiation exposure

Novel approaches to the assessment of patients with chest systoms in the acute medical and outpatient settings: the use of multislice computed tomography

This thesis evaluated the clinical utility of cardiopulmonary computed tomography (CT) in patients presenting with chest pain and dyspnoea. Studies within this thesis confirmed the following. Firstly, there is a requirement for improved diagnostic pathways to minimise patients being discharged without a diagnosis, which currently occurs in 30-40% of patients admitted with chest pain and dyspnoea. Historically, CT has been utilised in 32% of admissions with chest pain and 10% of admissions with dyspnoea. Secondly, challenges exist to the wider adoption of cardiopulmonary CT. These include patient-related factors, institutional capabilities and guideline restrictions. In acute admissions, 11% of patients with dyspnoea and 7% of patients with chest pain and a low to moderate likelihood of CAD are suitable for CT. In the RACPC setting, including patients across the entire spectrum of CAD likelihood, 18% of patients are suitable for CT. NICE CG95 would recommend only 1% of acute chest pain admissions and 2% of RACPC attenders for CT. Thirdly, NICE CG95 would recommend 51% of acute chest pain admissions and 66% of RACPC attenders for discharge without cardiac investigation. In the RACPC population, significant CAD is identified in 10% of these patients and a major adverse cardiac event in 2%. Fourthly, in selected patients with suspected cardiac chest pain, cardiac CT has a diagnostic yield of 21% in acute admissions and 13% in RACPC attenders for significant CAD. In acute admissions with dyspnoea, cardiopulmonary CT has a diagnostic yield of 20% for CAD, 20% for pulmonary embolism, nil for aortic dissection and 89% for non-vascular chest pathology. Fifthly, inclusion of CT in diagnostic pathways for chest pain result in fewer patients discharged without a diagnosis, fewer invasive angiography procedures and reduced diagnostic costs. In patients with dyspnoea, CT provides value to clinicians making diagnoses and supports early discharge without detrimental outcomes.Open Acces

Improving the Diagnostic Pathway of Pulmonary Hypertension using Cardio-Pulmonary Magnetic Resonance Imaging

Whilst pulmonary hypertension is a relatively uncommon condition, it is associated with a poor quality of life and poor survival. It is therefore important that we correctly identify patients who suffer from pulmonary hypertension, assess the underlying cause (an essential step for treatment) and seek those who are at risk of death. Current guidelines centre on right heart catheterisation as the recommended tool to answer these important clinical questions. Since it was first described in the mid-1950s, there have been significant improvements in the survival of patients with pulmonary hypertension, mainly due to the introduction of vasodilator therapies and surgical procedures. There have been parallel improvements in imaging technologies, the most tangible of which is cardiac MRI, allowing time resolved assessment of cardiac structure and function. Despite these improvements in non-invasive methodologies, there remains heavy reliance upon invasively measured pressures and flow for the diagnosis, phenotyping and assessment of risk in patients with pulmonary hypertension. The aim of this PhD thesis is to evaluate, and hopefully increase, the role of cardio pulmonary vascular MRI in the non-invasive assessment of pulmonary hypertension. I show that cardiac MRI metrics, particularly when combined in a regression model, are able to predict mean pulmonary arterial pressure. Such models are able to identify with reasonable accuracy the presence of pulmonary hypertension in patients referred to a tertiary referral centre. The role of cardio-pulmonary MRI in the assessment of the underlying group of pulmonary hypertension, such as chronic thrombo-embolic pulmonary hypertension and PH-left heart disease, is then explored as identification of patients who may respond to PH specific therapy is an important step. Finally, the role of MRI in the assessment of prognosis, concentrating specifically on patients with PH left heart disease and PH in patients with chronic obstructive pulmonary disease is assessed