A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively

Investigating the effect of flow compensation and quantitative susceptibility mapping method on the accuracy of venous susceptibility measurement

Quantitative susceptibility mapping (QSM) is a promising non-invasive method for obtaining information relating to oxygen metabolism. However, the optimal acquisition sequence and QSM reconstruction method for reliable venous susceptibility measurements are unknown. Full flow compensation is generally recommended to correct for the influence of venous blood flow, although the effect of flow compensation on the accuracy of venous susceptibility values has not been systematically evaluated. In this study, we investigated the effect of different acquisition sequences, including different flow compensation schemes, and different QSM reconstruction methods on venous susceptibilities. Ten healthy subjects were scanned with five or six distinct QSM sequence designs using monopolar readout gradients and different flow compensation schemes. All data sets were processed using six different QSM pipelines and venous blood susceptibility was evaluated in whole-brain segmentations of the venous vasculature and single veins. The quality of vein segmentations and the accuracy of venous susceptibility values were analyzed and compared between all combinations of sequences and reconstruction methods. The influence of the QSM reconstruction method on average venous susceptibility values was found to be 2.7–11.6 times greater than the influence of the acquisition sequence, including flow compensation. The majority of the investigated QSM reconstruction methods tended to underestimate venous susceptibility values in the vein segmentations that were obtained. In summary, we found that multi-echo gradient-echo acquisition sequences without full flow compensation yielded venous susceptibility values comparable to sequences with full flow compensation. However, the QSM reconstruction method had a great influence on susceptibility values and thus needs to be selected carefully for accurate venous QSM