PHT-bot: Deep-Learning based system for automatic risk stratification of COPD patients based upon signs of Pulmonary Hypertension

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide. Identifying those at highest risk of deterioration would allow more effective distribution of preventative and surveillance resources. Secondary pulmonary hypertension is a manifestation of advanced COPD, which can be reliably diagnosed by the main Pulmonary Artery (PA) to Ascending Aorta (Ao) ratio. In effect, a PA diameter to Ao diameter ratio of greater than 1 has been demonstrated to be a reliable marker of increased pulmonary arterial pressure. Although clinically valuable and readily visualized, the manual assessment of the PA and the Ao diameters is time consuming and under-reported. The present study describes a non invasive method to measure the diameters of both the Ao and the PA from contrast-enhanced chest Computed Tomography (CT). The solution applies deep learning techniques in order to select the correct axial slice to measure, and to segment both arteries. The system achieves test Pearson correlation coefficient scores of 93% for the Ao and 92% for the PA. To the best of our knowledge, it is the first such fully automated solution

3D MODELLING AND RAPID PROTOTYPING FOR CARDIOVASCULAR SURGICAL PLANNING – TWO CASE STUDIES

In the last years, cardiovascular diagnosis, surgical planning and intervention have taken advantages from 3D modelling and rapid prototyping techniques. The starting data for the whole process is represented by medical imagery, in particular, but not exclusively, computed tomography (CT) or multi-slice CT (MCT) and magnetic resonance imaging (MRI). On the medical imagery, regions of interest, i.e. heart chambers, valves, aorta, coronary vessels, etc., are segmented and converted into 3D models, which can be finally converted in physical replicas through 3D printing procedure. In this work, an overview on modern approaches for automatic and semiautomatic segmentation of medical imagery for 3D surface model generation is provided. The issue of accuracy check of surface models is also addressed, together with the critical aspects of converting digital models into physical replicas through 3D printing techniques. A patient-specific 3D modelling and printing procedure (Figure 1), for surgical planning in case of complex heart diseases was developed. The procedure was applied to two case studies, for which MCT scans of the chest are available. In the article, a detailed description on the implemented patient-specific modelling procedure is provided, along with a general discussion on the potentiality and future developments of personalized 3D modelling and printing for surgical planning and surgeons practice

Image quality and dosimetry of a dual source computed tomography scanner with special emphasis on radiation dose of lung in a chest examination

The purpose of the current study was to evaluate the Dual Source Computed Tomography scanner in terms of Image quality and dosimetry with special emphasis of radiation dose of lung in a Chest examination.Zielsetzung der Studie war die Evaluation eines Dual-Source-Computertomographen hinsichtlich Bildqualität und Dosimetrie mit speziellem Fokus auf der Lungendosis in Thoraxuntersuchungen

Optimization of CT scanning protocol of Type B aortic dissection follow-up through 3D printed model

This research aims to develop and evaluate a human tissue-like material 3D printed model used as a phantom in determining optimized scanning parameters to reduce the radiation dose for Type B aortic dissection patients after thoracic endovascular aortic repair. The results show that radiation risk for follow-up Type B aortic dissection patients can be potentially reduced. Further, the value of using 3D printed model in studying CT scanning protocols was further validated

Serial decline in lung volume parameters on computed tomography (CT) predicts outcome in idiopathic pulmonary fibrosis (IPF)

OBJECTIVES: In patients with IPF, this study aimed (i) to examine the relationship between serial change in CT parameters of lung volume and lung function, (ii) to identify the prognostic value of serial change in CT parameters of lung volume, and (iii) to define a threshold for serial change in CT markers of lung volume that optimally captures disease progression. METHODS: Serial CTs were analysed for progressive volume loss or fibrosis progression in 81 IPF patients (66 males, median age = 67 years) with concurrent forced vital capacity (FVC) (median follow-up 12 months, range 6-23 months). Serial CT measurements of volume loss comprised oblique fissure posterior retraction distance (OFPRD), aortosternal distance (ASD), lung height corrected for body habitus (LH), and automated CT-derived total lung volumes (ALV) (measured using commercially available software). Fibrosis progression was scored visually. Serial changes in CT markers and FVC were compared using regression analysis, and evaluated against mortality using Cox proportional hazards. RESULTS: There were 58 deaths (72%, median survival = 17 months). Annual % change in ALV was most significantly related to annual % change in FVC (R2 = 0.26, p < 0.0001). On multivariate analysis, annual % change in ASD predicted mortality (HR = 0.97, p < 0.001), whereas change in FVC did not. A 25% decline in annual % change in ASD best predicted mortality, superior to 10% decline in FVC and fibrosis progression. CONCLUSION: In IPF, serial decline in CT markers of lung volume and, specifically, annualised 25% reduction in aortosternal distance provides evidence of disease progression, not always identified by FVC trends or changes in fibrosis extent. KEY POINTS: • Serial decline in automated and surrogate markers of lung volume on CT corresponds to changes in FVC. • Annualised reductions in the distance between ascending aorta and posterior border of the sternum on CT predict mortality beyond annualised percentage change in FVC

ESC core curriculum for the general cardiologist (2013)

[No abstract available

Mechanistic and pathological study of the genesis, growth, and rupture of abdominal aortic aneurysms

Postprint (published version

A computational framework for generating patient-specific vascular models and assessing uncertainty from medical images

Patient-specific computational modeling is a popular, non-invasive method to answer medical questions. Medical images are used to extract geometric domains necessary to create these models, providing a predictive tool for clinicians. However, in vivo imaging is subject to uncertainty, impacting vessel dimensions essential to the mathematical modeling process. While there are numerous programs available to provide information about vessel length, radii, and position, there is currently no exact way to determine and calibrate these features. This raises the question, if we are building patient-specific models based on uncertain measurements, how accurate are the geometries we extract and how can we best represent a patient's vasculature? In this study, we develop a novel framework to determine vessel dimensions using change points. We explore the impact of uncertainty in the network extraction process on hemodynamics by varying vessel dimensions and segmenting the same images multiple times. Our analyses reveal that image segmentation, network size, and minor changes in radius and length have significant impacts on pressure and flow dynamics in rapidly branching structures and tapering vessels. Accordingly, we conclude that it is critical to understand how uncertainty in network geometry propagates to fluid dynamics, especially in clinical applications.Comment: 21 pages, 9 figure