3-D Registration on Carotid Artery imaging data: MRI for different timesteps

A common problem which is faced by the researchers when dealing with arterial carotid imaging data is the registration of the geometrical structures between different imaging modalities or different timesteps. The use of the "Patient Position" DICOM field is not adequate to achieve accurate results due to the fact that the carotid artery is a relatively small structure and even imperceptible changes in patient position and/or direction make it difficult. While there is a wide range of simple/advanced registration techniques in the literature, there is a considerable number of studies which address the geometrical structure of the carotid artery without using any registration technique. On the other hand the existence of various registration techniques prohibits an objective comparison of the results using different registration techniques. In this paper we present a method for estimating the statistical significance that the choice of the registration technique has on the carotid geometry. One-Way Analysis of Variance(ANOVA) showed that the p-values were <0.0001 for the distances of the lumen from the centerline for both right and left carotids of the patient case that was studied.Comment: 4 pages, 4 figures, 1 table, preprint submitted to IEEE-EMBC 201

Comparison of Longley-Rice, ITU-R P.1546 and Hata-Davidson propagation models for DVB-T coverage prediction

This paper compares precision field-strength measurements taken by a Rohde & Schwarz FSH-3 portable spectrum analyzer with simulation results derived from the ITM coverage prediction model (Irregular Terrain Model), also known as NTIA-ITS Longley-Rice model, in conjunction with the 3-arc-second SRTM (Satellite Radar Topography Mission) geographical data, the propagation predictions of ITU-R Recommendation P.1546 and those of the empirical Hata-Davidson model using HAAT. ITU-R P.1546 and Hata-Davidson models exhibit higher errors at longer distances and therefore necessary corrections should be introduced in the models in order to increase propagation prediction accuracy. Especially, measurements results show that ITU-R P.1546, on average, underestimates the field strength at distances longer than 50 km. The Longley-Rice model using the terrain digital elevations is more accurate, as expected, and its results are closer to the measurement data

A Preliminary Study on In-Vivo3-D Imaging of Bioprosthetic Aortic Valve Deformation

This paper is based on the observation that all the available but also forthcoming valves for transcatheter aortic valve implantation (TAVI), do not examine how well the prosthetic valve will match the native anatomy (3d structure) after implantation. If the valve is not properly fitted in the native 3d anatomy, this may result in serious complications, which will affect short and long-term outcome. The latter are realized after valve implantation, they are difficult to correct and may significantly increase the cost of the procedure. Experienced operators take into account these factors when performing TAVI, as much as these can be appreciated by fluoroscopy and Computerized Tomography (CT) data; but they do not have available imaging algorithms that can guide them to the selection of the best valve for the specific anatomy or to quantify potential deformations of the valve during or after the procedure. Therefore, it is necessary to have a quantitative guidance that will assist interventional cardiologists on the selection of a certain valve, based on 3d structure, as well as on the evaluation of valve position and potential deformation. More specifically, a 3d reconstruction of an In-Vivo Bioprosthetic Aortic Valve (BAV) based on CT images is performed and the distances of the points on the wall of the Artificial Valve from the centerline are calculated

Fusion of optical coherence tomographic and angiographic data for more accurate evaluation of the endothelial shear stress patterns and neointimal distribution after bioresorbable scaffold implantation: comparison with intravascular ultrasound-derived reconstructions

Intravascular ultrasound (IVUS)-based reconstructions have been traditionally used to examine the effect of endothelial shear stress (ESS) on neointimal formation. The aim of this analysis is to compare the association between ESS and neointimal thickness (NT) in models obtained by the fusion of optical coherence tomography (OCT) and coronary angiography and in the reconstructions derived by the integration of IVUS and coronary angiography. We analyzed data from six patients implanted with an Absorb bioresorbable vascular scaffold that had biplane angiography, IVUS and OCT investigation at baseline and 6 or 12 months follow-up. The IVUS and OCT follow-up data were fused separately with the angiographic data to reconstruct the luminal morphology at baseline and follow-up. Blood flow simulation was performed on the baseline reconstructions and the ESS was related to NT. In the OCT-based reconstructions the ESS were lower compared to the IVUS-based models (1.29 +/- A 0.66 vs. 1.87 +/- A 0.66 Pa, P = 0.030). An inverse correlation was noted between the logarithmic transformed ESS and the measured NT in all the OCT-based models which was higher than the correlation reported in five of the six IVUS-derived models (-0.52 +/- A 0.19 Pa vs. -0.10 +/- A 0.04, P = 0.028). Fusion of OCT and coronary angiography appears superior to IVUS-based reconstructions; therefore it should be the method of choice for the study of the effect of the ESS on neointimal proliferation

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Abstract Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single–institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area. Key points • Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata. • Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data. • Developing a common data model for storing all relevant information is a challenge. • Trust of data providers in data sharing initiatives is essential. • An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area