Purpose: To assess the feasibility of a selective flow-tracking cartographic procedure applied to four-dimensional (4D) flow imaging and to demonstrate its usefulness in the characterization of dural arteriovenous fistulas (DAVFs).
Materials And Methods: Institutional review board approval was obtained, and all patients provided written informed consent. Eight patients (nine DAVFs) underwent 3.0-T magnetic resonance (MR) imaging and digital subtraction angiography (DSA). Imaging examinations were performed within 24 hours of each other. 4D flow MR imaging was performed by using a 4D radial phase-contrast vastly undersampled isotropic projection reconstruction pulse sequence with an isotropic spatial resolution of 0.86 mm (5 minutes 35 seconds). Two radiologists independently reviewed images from MR flow-tracking cartography and reported the location of arterial feeder vessels and the venous drainage type and classified DAVFs according to the risk of rupture (Cognard classification). These results were compared with those at DSA. Quadratic weighted κ statistics with their 95% confidence intervals (CIs) were used to test intermodality agreement in the identification of arterial feeder vessels, draining veins, and Cognard classification.
Results: Interreader agreement for shunt location on MR images was perfect (κ = 1), with good-to-excellent interreader agreement for arterial feeder vessel identification (κ = 0.97; 95% CI = 0.92, 1.0), and matched in all cases with shunt location defined at DSA. There was good-to-excellent agreement between MR cartography and DSA in the definition of the main feeding arteries (κ = 0.92; 95% CI = 0.83, 1.0), presence of retrograde flow in dural sinuses (κ = 1), presence of retrograde cortical venous drainage (κ = 1), presence of venous ectasia (κ = 1), and final Cognard classification of DAVFs (κ = 1, standard error = 0.35).
Conclusion: MR selective flow-tracking cartography enabled the noninvasive characterization of cranial DAVFs.
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http://dx.doi.org/10.1148/radiol.13130507 | DOI Listing |
Opt Express
November 2023
Reconstruction of moving target surfaces based on active image sensing techniques, such as phase-shifting profilometry, has attracted intensive research in recent years. The measurement error caused by object motion can be addressed successfully by tracking the object movement. However, it either requires high-cost color imaging equipment or is limited by the assumption of 2D translation movement.
View Article and Find Full Text PDFBioinspir Biomim
February 2023
Mechanical and Materials Engineering Department, Queen's University, Kingston, Ontario, Canada.
The effective natural transport of seeds in turbulent atmospheric flows is found across a myriad of shapes and sizes. However, to develop a sensitive passive sensor required for large-scale () flow tracking measurements, systems suffer from inertial lag due to the increased size and mass needed for optical visibility, or by carrying a sensor payload, such as an inertial measurement unit (IMU). While IMU-based flow sensing is promising for beyond visual line-of-sight applications, the size and mass of the sensor platform results in reduced flow fidelity and, hence, measurement error.
View Article and Find Full Text PDFMagn Reson Med
May 2022
Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Purpose: Streamlines from 4D-flow MRI have been used clinically for intracranial blood-flow tracking. However, deterministic and stochastic errors degrade streamline quality. The purpose of this study is to integrate displacement corrections, probabilistic streamlines, and novel fluid constraints to improve selective blood-flow tracking and emulate "virtual bolus injections.
View Article and Find Full Text PDFIndirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 (Mur-Artal and Tardós, 2017) is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors.
View Article and Find Full Text PDFSensors (Basel)
April 2019
School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China.
The research field of visual-inertial odometry has entered a mature stage in recent years. However, unneglectable problems still exist. Tradeoffs have to be made between high accuracy and low computation for users.
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