Open-source virtual bronchoscopy for image guided navigation

This thesis describes the development of an open-source system for virtual bronchoscopy used in combination with electromagnetic instrument tracking. The end application is virtual navigation of the lung for biopsy of early stage cancer nodules. The open-source platform 3D Slicer was used for creating freely available algorithms for virtual bronchscopy. Firstly, the development of an open-source semi-automatic algorithm for prediction of solitary pulmonary nodule malignancy is presented. This approach may help the physician decide whether to proceed with biopsy of the nodule. The user-selected nodule is segmented in order to extract radiological characteristics (i.e., size, location, edge smoothness, calcification presence, cavity wall thickness) which are combined with patient information to calculate likelihood of malignancy. The overall accuracy of the algorithm is shown to be high compared to independent experts' assessment of malignancy. The algorithm is also compared with two different predictors, and our approach is shown to provide the best overall prediction accuracy. The development of an airway segmentation algorithm which extracts the airway tree from surrounding structures on chest Computed Tomography (CT) images is then described. This represents the first fundamental step toward the creation of a virtual bronchoscopy system. Clinical and ex-vivo images are used to evaluate performance of the algorithm. Different CT scan parameters are investigated and parameters for successful airway segmentation are optimized. Slice thickness is the most affecting parameter, while variation of reconstruction kernel and radiation dose is shown to be less critical. Airway segmentation is used to create a 3D rendered model of the airway tree for virtual navigation. Finally, the first open-source virtual bronchoscopy system was combined with electromagnetic tracking of the bronchoscope for the development of a GPS-like system for navigating within the lungs. Tools for pre-procedural planning and for helping with navigation are provided. Registration between the lungs of the patient and the virtually reconstructed airway tree is achieved using a landmark-based approach. In an attempt to reduce difficulties with registration errors, we also implemented a landmark-free registration method based on a balanced airway survey. In-vitro and in-vivo testing showed good accuracy for this registration approach. The centreline of the 3D airway model is extracted and used to compensate for possible registration errors. Tools are provided to select a target for biopsy on the patient CT image, and pathways from the trachea towards the selected targets are automatically created. The pathways guide the physician during navigation, while distance to target information is updated in real-time and presented to the user. During navigation, video from the bronchoscope is streamed and presented to the physician next to the 3D rendered image. The electromagnetic tracking is implemented with 5 DOF sensing that does not provide roll rotation information. An intensity-based image registration approach is implemented to rotate the virtual image according to the bronchoscope's rotations. The virtual bronchoscopy system is shown to be easy to use and accurate in replicating the clinical setting, as demonstrated in the pre-clinical environment of a breathing lung method. Animal studies were performed to evaluate the overall system performance

Zastosowanie algorytmów zamykania i wypełniania tuneli w komputerowej analizie materiałów na bazie tomograficznych obrazów 3D

Artykuł prezentuje szybkie, oryginalne algorytmy zamykania i wypełniania tuneli dla obiektów wolumetrycznych 3D oraz ich przykładowe zastosowania w badaniach materiałów z wykorzystaniem tomografii rentgenowskiej. Pierwsze zastosowanie dotyczy aproksymacji objętości wiązadeł mostowych w popękanej stali nierdzewnej. Następnie zaprezentowano badania nad strukturą poliuretanowych pian auksetycznych. Trzeci przykład dotyczy badań nad zgrzewaniem dwóch płyt będących stopami aluminium. Wszystkie zaprezentowane przypadki pokazują przydatność algorytmów bądź to na etapie wstępnego przetwarzania tomograficznego obrazu 3D lub na etapie ekstrakcji interesujących obiektów.The article presents fast, original algorithms of tunnel closing and tunnel filling for volumetric 3D objects and their exemplary applications in materiał Science with the use of X-ray tomography. The first application concerns volume approximation of bridge ligaments in stress corrosion cracking of stainless steel samples. Moreover the authors present research on Structure of polyurethane auxetic foam. The third example concerns research on friction stir spot welding of aluminum sheets. All presented cases show usefulness of these algorithms in 3D tomography image processing or extraction of interesting objects from a 3D image

Quantifying Airway Dilatation in the Lungs from Computed Tomography

Non CF bronchiectasis and idiopathic pulmonary fibrosis (IPF) are pulmonary diseases characterised by the abnormal and permanent dilatation of the airways. Computed tomography (CT) is used in clinical practice to diagnose and monitor patients with the disease. Currently, analysis of the scans is performed by manual inspection and there is no established computerised method to quantify the enlargement of airways. I developed a pipeline to quantify the cross-sectional area for a given airway track. Using an airway segmentation, my proposed algorithm measures the area at contiguous intervals along the airway arclength from the Carina to the most distal point visible on CT. I showed the use of the data generated from the pipeline in two applications. First, I proposed a novel tapering measure as the gradient of a linear regression between a logarithmic area against the arclength. The measurement was applied to airways affected by bronchiectasis. Second, I used Bayesian Changepoint Detection (BCD) with the area measurements to locate the progression of IPF along the airway track. The proposed pipeline was applied to a set of clinically acquired scans. I show a statistical difference (p = 3.4×10−4 ) in the tapering measurement between bronchiectatic (n = 53) and controlled (n = 39) airways. In addition, I report a statistical difference (p = 7.2×10−3 ) in the change in measurement between airways remaining healthy (n = 14) and airways that have become bronchiectatic (n = 5). I show the tapering measurement is reproducible independent to voxel size, CT reconstruction, and radiation dose. Using BCD, I show on simulated data (n = 14) my proposed method can detect the progression of IPF within 2.5mm. Finally, using results from BCD, I present a novel measure of IPF progression as the percentage volume change in the diseased region of the airways

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed

Development of a digital ascultometer for the diagnosis of heart murmurs

В процессе исследования был создан макет цифрового аускультометра проведена аускультация звуков сердца и легких на полученном устройстве. В результате изучения характеристик цифрового аускультометра, устройство показал эффективность в диагностике сердца и легких: четко распознает звуки разного диапазона спектров частотIn the process of research it was created a layout of digital audio. As a result of studying the characteristics of a digital audio device, the device showed effectiveness in the diagnosis of the heart and lungs: clearly recognizable sounds of different frequency ranges

Numerical studies of fluid-particle dynamics in human respiratory system

 This thesis investigates particle inhalation and its deposition in the human respiratory system for therapeutic and toxicology studies. Computational Fluid Dynamics (CFD) techniques including the Lagrangian approach to simulate gas-particle flows based on the domain airflow are used. The Lagrangian approach is used as it tracks each individual particle and determines its fate (e.g deposition location, or escape from computational domain). This has advantages over a Eulerian approach for respiratory inhalation flows as the volume fraction of the second phase can be neglected and a disperse phase for one-way coupling can be used. However, the very first step is to simulate and detail airflow structures. For the external airflow structures, the heat released from the human body has a significant effect on the airflow micro-environment around it in an indoor environment, which suggests that the transport and inhalation characteristics of aerosol particulates may also be affected since they are entrained by the air and their movement is dependent on the airflow field. Emphasis was put on the effect of human body heat on particle tracks. It was found that body heat causes a significant rising airflow on the downstream side of the body, which transports particles from a lower level into the breathing zone. The importance of body heat decreases with increasing indoor wind speed. Since the rising airflow exists only on the downstream side of an occupant, the occupant-wind orientation plays an important role in particle inhalation. The effect of body heat has to be taken into account when an occupant had his or her back to the wind, and the effect of body heat could be neglected when the occupant is facing the wind. A CFD model that integrates the three aspects of contaminant exposure by including the external room, human occupant with realistic facial features, and the internal nasal-trachea airway is presented. The results from the simulations visualize the flow patterns at different contaminant concentrations. As the particles are inhaled, they are transported through the respiratory airways, where some are deposited onto surrounding mucus walls while others may navigate through the complex geometry and even reach the lung airways, causing deleterious health effects. The studies in this thesis demonstrated that the transport and deposition of micron sized particles are dominated by its inertial property while submicron and nano sized particles are influenced by diffusion mechanisms. Studies based on an isolated model of the human nasal cavity or tracheobronchial airway tree rely on idealised inlet boundary condition imposed at the nostril or where, were a blunt, parabolic or uniform profile is applied. It is apparent that an integrated model made up of: i) room and ventilation, ii) aspiration efficiency, iii) and particle deposition efficiencies in the respiratory airway is needed. This leads to a more complete and holistic set of results, which can greatly contribute towards new knowledge in identifying preventative measures for health risk exposure assessment. With regards to the internal airflow structures and particle inhalation, ultrafine particle deposition sites in the human nasal cavity regions often omit the paranasal sinus regions. Because of the highly diffusive nature of nanoparticles, it is conjectured that deposition by diffusion may occur in the paranasal sinuses, which may affect the residual deposition fraction that leaves the nasal cavity. Thus a nasal-sinus model was created for analysis. In general there was little flow passing through the paranasal sinuses. However, flow patterns revealed that some streamlines reached the upper nasal cavity near the olfactory regions. These flow paths promote particle deposition in the sphenoid and ethmoid sinuses. Some differences were discovered in the deposition fractions and patterns for 5 and 10nm particles between the nasal-sinus and the nasal cavity models. This difference is amplified when the flow rate is decreased and at a flow rate of 4L/min the maximum difference was 17%. It is suggested that future evaluations of nanoparticle deposition should consider some deposition occurring in the paranasal sinuses especially if flow rates are of concern. Inhaled particles with pharmacological agents (e.g. histamine, methacholine) are introduced into the nasal cavity for targeted delivery. Effective nasal drug delivery is highly dependent on the delivery of the drug from the nasal spray device. Atomization of liquid spray occurs through the internal atomizer that can produce many forms of spray patterns and two of these, hollow-cone and full-cone sprays, are evaluated in this study to determine which spray pattern produced greater deposition in the middle regions of the nasal cavity. Past studies of spray particle deposition have ignored the device within the nasal cavity. Experimental measurements from a Particle Droplet Image Analyzer (PDIA) were taken in order to gain confidence to validate the initial particle conditions for the computational models.. Subsequent airflow patterns and its effects on particle deposition, with and without a spray device, are compared. Contours and streamlines of the flow field revealed that the presence of a spray device in the nasal vestibule produced higher levels of disturbed flow, which helped the dispersion of the sprayed particles. Particle deposition was found to be high in the anterior regions of the nasal cavity due to its inertia. Evaluation of the two spray types found that hollow spray cones produced more deposition in the middle regions of the nasal cavity

Computer-Aided Assessment of Tuberculosis with Radiological Imaging: From rule-based methods to Deep Learning

Mención Internacional en el título de doctorTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb.) that produces pulmonary damage due to its airborne nature. This fact facilitates the disease fast-spreading, which, according to the World Health Organization (WHO), in 2021 caused 1.2 million deaths and 9.9 million new cases. Traditionally, TB has been considered a binary disease (latent/active) due to the limited specificity of the traditional diagnostic tests. Such a simple model causes difficulties in the longitudinal assessment of pulmonary affectation needed for the development of novel drugs and to control the spread of the disease. Fortunately, X-Ray Computed Tomography (CT) images enable capturing specific manifestations of TB that are undetectable using regular diagnostic tests, which suffer from limited specificity. In conventional workflows, expert radiologists inspect the CT images. However, this procedure is unfeasible to process the thousands of volume images belonging to the different TB animal models and humans required for a suitable (pre-)clinical trial. To achieve suitable results, automatization of different image analysis processes is a must to quantify TB. It is also advisable to measure the uncertainty associated with this process and model causal relationships between the specific mechanisms that characterize each animal model and its level of damage. Thus, in this thesis, we introduce a set of novel methods based on the state of the art Artificial Intelligence (AI) and Computer Vision (CV). Initially, we present an algorithm to assess Pathological Lung Segmentation (PLS) employing an unsupervised rule-based model which was traditionally considered a needed step before biomarker extraction. This procedure allows robust segmentation in a Mtb. infection model (Dice Similarity Coefficient, DSC, 94%±4%, Hausdorff Distance, HD, 8.64mm±7.36mm) of damaged lungs with lesions attached to the parenchyma and affected by respiratory movement artefacts. Next, a Gaussian Mixture Model ruled by an Expectation-Maximization (EM) algorithm is employed to automatically quantify the burden of Mtb.using biomarkers extracted from the segmented CT images. This approach achieves a strong correlation (R2 ≈ 0.8) between our automatic method and manual extraction. Consequently, Chapter 3 introduces a model to automate the identification of TB lesions and the characterization of disease progression. To this aim, the method employs the Statistical Region Merging algorithm to detect lesions subsequently characterized by texture features that feed a Random Forest (RF) estimator. The proposed procedure enables a selection of a simple but powerful model able to classify abnormal tissue. The latest works base their methodology on Deep Learning (DL). Chapter 4 extends the classification of TB lesions. Namely, we introduce a computational model to infer TB manifestations present in each lung lobe of CT scans by employing the associated radiologist reports as ground truth. We do so instead of using the classical manually delimited segmentation masks. The model adjusts the three-dimensional architecture, V-Net, to a multitask classification context in which loss function is weighted by homoscedastic uncertainty. Besides, the method employs Self-Normalizing Neural Networks (SNNs) for regularization. Our results are promising with a Root Mean Square Error of 1.14 in the number of nodules and F1-scores above 0.85 for the most prevalent TB lesions (i.e., conglomerations, cavitations, consolidations, trees in bud) when considering the whole lung. In Chapter 5, we present a DL model capable of extracting disentangled information from images of different animal models, as well as information of the mechanisms that generate the CT volumes. The method provides the segmentation mask of axial slices from three animal models of different species employing a single trained architecture. It also infers the level of TB damage and generates counterfactual images. So, with this methodology, we offer an alternative to promote generalization and explainable AI models. To sum up, the thesis presents a collection of valuable tools to automate the quantification of pathological lungs and moreover extend the methodology to provide more explainable results which are vital for drug development purposes. Chapter 6 elaborates on these conclusions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidenta: María Jesús Ledesma Carbayo.- Secretario: David Expósito Singh.- Vocal: Clarisa Sánchez Gutiérre

Pulmonary Structure and Function in Chronic Obstructive Pulmonary Disease Evaluated using Hyperpolarized Noble Gas Magnetic Resonance Imaging

Chronic obstructive pulmonary disease (COPD) is the 4th leading cause of death worldwide and accounts for the highest rate of hospital admissions in Canada. The need for sensitive regional and surrogate measurements of lung structure and function in COPD continues to motivate the development of non-radiation based and sensitive imaging approaches, such as hyperpolarized helium-3 (3He) and xenon-129 (129Xe) magnetic resonance imaging (MRI). The static ventilation images acquired using these approaches allows us to directly visualize lung regions accessed by the hyperpolarized gas during a breath-hold, as well as quantify the regions without signal referred to as the percentage of the thoracic cavity occupied by ventilation defects (VDP). The lung micro-structure can also be probed using diffusion-weighted imaging which takes advantage of the rapid diffusion of 3He and 129Xe atoms to generate surrogate measurements of alveolar size, referred to as the apparent diffusion coefficient (ADC). Here we evaluated COPD lung structure and function using hyperpolarized gas MRI measurements longitudinally, following treatment and in early disease. In COPD ex-smokers, we demonstrated 3He VDP and ADC worsened significantly in only 2 years although there was no change in age-matched healthy volunteers, suggestive of disease progression. We also evaluated COPD ex-smokers pre- and post-bronchodilator and showed regional improvements in gas distribution following bronchodilator therapy regardless of spirometry-based responder classification; the ADC measured in these same COPD ex-smokers also revealed significant reductions in regional gas trapping post-bronchodilator. Although 3He MRI has been more widely used, the limited global quantities necessitates the transition to hyperpolarized 129Xe, and therefore we directly compared 3He and 129Xe MRI in the same COPD ex-smokers and showed significantly greater gas distribution abnormalities for 129Xe compared to 3He MRI that were spatially and significantly related to lung regions with elevated ADC. Finally, we demonstrated that ex-smokers with normal spirometry but abnormal diffusion capacity of the lung for carbon monoxide (DLCO) had significantly worse symptoms, exercise capacity and 3He ADC than ex-smokers with normal DLCO. These important findings indicate that hyperpolarized gas MRI can be used to improve our understanding of lung structural and functional changes in COPD

Proceedings Virtual Imaging Trials in Medicine 2024

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday