Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Intelligent cloud-based digital imaging medical system solution

This research started with a simple fact: The global needs in medical care, and in medical imaging specifically, are increasing. This is mainly due to a population that is getting older and hence more likely to be exposed to diseases; but this same population would wish to keep a high quality of life. Therefore, to cope with these challenges, many systems, innovations and programs have been created and developed. Among them is the Picture Archiving and Communication System or PACS. Although this filmless system has shown to have a great deal of advantages when onsite - such as the capability to access medical data at different locations - these benefits seem to be outbalanced by the high initial costs, potential risk of data loss and the complexity of data sharing. Therefore, the aim of this research is to suggest a potential betterment of the onsite medical system, by introducing cloud and Computer Aided Diagnosis aspects to it. Lausanne Hospital has been used as a benchmark in order to evaluate the proposed solution, in terms of cost efficiency, diagnosis accuracy, users’ productivity, medical data sharing opportunities, data accessibility, procedure when upgrading systems, reporting process, workflow performed for handling technical issues, and teleradiology benefits. Investigating the potential impact of merging Cloud, PACS and CAD as one intelligent cloud-based digital imaging medical system solution has resulted with the following discovery: the proposed medical technology appears to be more profitable for its potential users than the current option. In point of fact, the proposed solution minimises initial costs, as a result of offsite hosting. Moreover, the suggested system eases offsite medical data viewing and sharing, which strengthens opportunities for 6 teleradiology and collaboration between medical experts. This system also allows its potential users to centre their focus on their core area of expertise, as the system provider becomes the sole manager responsible for the software. Regarding the integration of CAD, the analysis has shown that utilising this software presumably adds greater value to the cloud-based medical system, as CAD engenders higher efficiency and productivity during diagnosis and reporting processes

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

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

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results which are similar to the diagnosis made by the doctors and is acceptable by clinical standards