Application of Radiomics in Vesselness Analysis of CT Angiography Images of Stroke Patients.

Development of vascular collaterals in a lesion area is one of the key factors that determine not only the choice of treatment for ischemic stroke (IS) patients, but also outcome and therapy effectiveness. The main method for examining the vessels' ramification is CT angiography (CTA). CTA analysis may be improved by incorporating filters designed to extract more features about vessels and quantify their level of development. This work suggests the usage of radiomics methods in the analysis of vesselness measure calculated from CTA images. Vesselness measurement is based on the analysis of the Hessian matrix with a few modifications dictated by practical aspects of this issue. The developed algorithm was implemented as a filter that generates a new 3D image, every voxel of which has the probability of belonging to a vessel-like structure. Further analysis of the distribution of vesselness in the lesion area and in the intact contralateral area was conducted with the methods from the open library PyRadiomics. A set of radiomics features was calculated. Preliminary analysis on a sample of 30 IS patients showed the presence of significant differences between afflicted and intact hemispheres.

A Platform for Collection and Analysis of Image Data on Stroke.

Identifying imaging biomarkers (IBs) of stroke remains a priority in neurodiagnostics. There is a number of different methods for image analysis and learning rules applicable in this field, but all of them require large arrays of DICOM images and clinical data. In order to amass such dataset,we havedesigneda platform for systematic collection of clinical data and medical images in different modalities. The platform provides easy-to-use tools to create formalized radiology reports, contour and tag the regions of interest (ROIs) on the DICOM images, and extract radiomics data. Subsequent analysis of the obtained data will allow identifying the most relevant IBs that predict clinical outcome and possible complications. The results of the analysis will be used to develop predictive algorithms for stroke diagnostics.