Reduction of metal artefacts from bilateral hip prostheses during lower extremity computed tomography angiography: an experimental phantom study.

INTRODUCTION: Image quality reduction due to metallic artefacts is a significant challenge during vascular computed tomography (CT) imaging of the lower extremities in patients with hip prostheses. This study aims to analyse various reconstruction algorithms' ability to reduce metal artefacts due to two types of hip prostheses during lower extremity CT angiography examinations. METHODS: A pelvis phantom was fabricated with the insertion of a tube filled with contrast media to simulate the femoral artery, and the phantom was then CT scanned with and without hip prostheses. Multimodal images were acquired using different kilovoltage peak (kVp) settings and reconstructed with different algorithms, such as filtered back projection (FBP), iterative reconstruction (iDose4), iterative model-based reconstruction (IMR) and orthopaedic metal artefact reduction (O-MAR). Image quality was assessed based on image noise, signal-to-noise ratio (SNR) and Hounsfield unit (HU) deviation. RESULTS: The IMR approach significantly improved image quality compared to iDose4 and FBP. For the vascular region, O-MAR improves SNR by 5 ± 1, 23 ± 5 and 42 ± 9 for FBP, iDose4 and IMR respectively, and improves HU precision towards the baseline values by 49% and 83% for FBP and IMR, respectively. The noise reduction was 71% and 89% for FBP and IMR, and 57% for iDose4. O-MAR greatly enhances SNR corrections among the most severe artefacts, with 29 ± 1 and 43 ± 4 for FBP and IMR, compared to iDose4 by 37 ± 7. CONCLUSION: IMR combined with O-MAR could improve the CT angiography of the lower extremities of patients with a hip prosthesis.

Estimation of Traffic Stream Density Using Connected Vehicle Data: Linear and Nonlinear Filtering Approaches.

The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates-with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application.

Developing a Neural-Kalman Filtering Approach for Estimating Traffic Stream Density Using Probe Vehicle Data.

This paper presents a novel model for estimating the number of vehicles along signalized approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to produce reliable traffic vehicle count estimates, considering real-time estimates of the system noise characteristics. The AKF utilizes only real-time probe vehicle data. The AKF is demonstrated to outperform the traditional Kalman filter, reducing the prediction error by up to 29%. In addition, the paper introduces a novel approach that combines the AKF with a neural network (AKFNN) to enhance the vehicle count estimates, where the neural network is employed to estimate the probe vehicles' market penetration rate. Results indicate that the accuracy of vehicle count estimates is significantly improved using the AKFNN approach (by up to 26%) over the AKF. Moreover, the paper investigates the sensitivity of the proposed AKF model to the initial conditions, such as the initial estimate of vehicle counts, initial mean estimate of the state system, and the initial covariance of the state estimate. The results demonstrate that the AKF is sensitive to the initial conditions. More accurate estimates could be achieved if the initial conditions are appropriately selected. In conclusion, the proposed AKF is more accurate than the traditional Kalman filter. Finally, the AKFNN approach is more accurate than the AKF and the traditional Kalman filter since the AKFNN uses more accurate values of the probe vehicle market penetration rate.