Comparison of extracranial artery stenosis and cerebral blood flow, assessed by quantitative magnetic resonance, using digital subtraction angiography as the reference standard

Mathematical Models for Blood Flow Quantification in Dialysis Access Using Angiography: A Comparative Study

Blood flow rate in dialysis (vascular) access is the key parameter to examine patency and to evaluate the outcomes of various endovascular interve7ntions. While angiography is extensively used for dialysis access–salvage procedures, to date, there is no image-based blood flow measurement application commercially available in the angiography suite. We aim to calculate the blood flow rate in the dialysis access based on cine-angiographic and fluoroscopic image sequences. In this study, we discuss image-based methods to quantify access blood flow in a flow phantom model. Digital subtraction angiography (DSA) and fluoroscopy were used to acquire images at various sampling rates (DSA—3 and 6 frames/s, fluoroscopy—4 and 10 pulses/s). Flow rates were computed based on two bolus tracking algorithms, peak-to-peak and cross-correlation, and modeled with three curve-fitting functions, gamma variate, lagged normal, and polynomial, to correct errors with transit time measurement. Dye propagation distance and the cross-sectional area were calculated by analyzing the contrast enhancement in the vessel. The calculated flow rates were correlated versus an in-line flow sensor measurement. The cross-correlation algorithm with gamma-variate curve fitting had the best accuracy and least variability in both imaging modes. The absolute percent error (mean ± SEM) of flow quantification in the DSA mode at 6 frames/s was 21.4 ± 1.9%, and in the fluoroscopic mode at 10 pulses/s was 37.4 ± 3.6%. The radiation dose varied linearly with the sampling rate in both imaging modes and was substantially low to invoke any tissue reactions or stochastic effects. The cross-correlation algorithm and gamma-variate curve fitting for DSA acquisition at 6 frames/s had the best correlation with the flow sensor measurements. These findings will be helpful to develop a software-based vascular access flow measurement tool for the angiography suite and to optimize the imaging protocol amenable for computational flow applications.

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

Clinical application of high-resolution MRI in combination with digital subtraction angiography in the diagnosis of vertebrobasilar artery dissecting aneurysm: An observational study (STROBE compliant)

Vertebrobasilar artery dissecting aneurysm (VBA-DA) is associated with serious complications and poor prognosis in patients. High-resolution magnetic resonance imaging (HR-MRI) is a noninvasive method for the diagnosis of VBA-DA.VBA-DAs were classified according to the feature of HR-MRI in combination with digital subtraction angiography (DSA), and the clinical outcomes of different types of VBA-DAs were analyzed. Thirty-nine patients with 42 VBA-DAs were included and underwent HR-MRI, including three-dimensional T1 weighted image, three-dimensional T2 weighted image (3D-T2WI), three-dimensional time of flight MRA (3D-TOF-MRA), and three-dimensional fast imaging employing steady state acquisition (3D-FIESTA), and hematoma and flaps were compared. The follow-up was 3 to 25 months. The VBA-DAs were classified based on the images of HR-MRI and DSA, and the prognosis was analyzed.VBA-DAs more frequently occurred on the vertebral artery, especially on the dominant vertebral artery. 3D-TOF-MRA showed high signal from hematoma, and 3D-FIESTA showed high signal from flaps. Based on HR-MRI images in combination with DSA, VBA-DAs were classified into 4 types: classical, stenosis, spiral, and hemorrhagic. The patients with the classical VBA-DAs had a higher improvement rate and a lower exacerbation rate. The patients with spiral and hemorrhagic VBA-DAs had poor clinical outcomes. The patients with stenosis VBA-DAs had poorer clinical outcomes than classical types and better clinical outcomes than spiral and hemorrhagic types.The detection of intramural hematoma and dissection flap using HR-MRI provides basic information for the diagnosis of VBA-DA. Individualized therapeutic strategies can be designed for the treatment of VBA-DAs with different features of DSA and HR-MRI.

Predictors of short-term outcome in patients with acute middle cerebral artery occlusion: unsuitability of fluid-attenuated inversion recovery vascular hyperintensity scores

Fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH) is used to assess leptomeningeal collateral circulation, but clinical outcomes of patients with FVH can be very different. The aim of the present study was to assess a FVH score and explore its relationship with clinical outcomes. Patients with acute ischemic stroke due to middle cerebral artery M1 occlusion underwent magnetic resonance imaging and were followed up at 10 days (National Institutes of Health Stroke Scale) and 90 days (modified Rankin Scale) to determine short-term clinical outcomes. Effective collateral circulation indirectly improved recovery of neurological function and short-term clinical outcome by extending the size of the pial penumbra and reducing infarct lesions. FVH score showed no correlation with 90-day functional clinical outcome and was not sufficient as an independent predictor of short-term clinical outcome.