Investigating the Impact of Susceptibility Artifacts on Adjacent Tumors in PET/MRI through Simulated Tomography Experiments

For quantitative PET imaging, attenuation correction (AC) is mandatory. Currently, all main vendors of hybrid PET/MRI systems apply a segmentation-based approach to compute a Dixon AC-map based on fat and water images derived from in- and opposed-phase MR-images. Changes in magnetic susceptibility pose major problems for MRI, which may lead to artifacts resulting in tissue misclassification in the segmented AC-map. Cases have been reported where the liver has been misidentified as lung tissue due to iron overload, e.g. from hemochromatosis or iron oxide MR contrast agents, resulting in severe underestimation of PET-quantification. In this thesis, simulated tomography experiments were conducted to investigate the impact of susceptibility artifacts on adjacent tumors, focusing on the misclassification of liver tissue as lung tissue. A digital phantom was programmed, and synthetic tumors and artifacts were introduced into a realistic PET/MRI patient dataset. The data were reconstructed with attenuation maps both with and without artifacts to compute the relative error (RE) in tumor uptake. It was shown that relevant errors can be introduced to tumors adjacent to the artifact. A strong inverse square relationship between the distance (d) of the center points of a tumor and an artifact was found with the RE. Further, because the RE was known to be proportional to the volume (V) of misclassified tissue, it was shown that it is possible to obtain a linear equation describing the RE using only V and d. However, this assumes similar information, i.e activity and attenuation, along the common line of responses (LORs) of the artifact and tumor. A correction method was developed to correct for lung-liver misclassifications. The proposed method uses the already acquired opposed-phase Dixon images, which are less sensitive to susceptibility changes. It successfully corrected 96% of misclassified tissue down to a 50% MR-signal reduction from the liver. The method benefits from using already acquired data to correct the artifacts, and may be made fully automatic to function in real-time

Hydraulic pressure and flow control of injection moulding

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN041811 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Real-time Autonomous Cruise Control of Connected Plug-in Hybrid Electric Vehicles Under Uncertainty

Advances in embedded digital computing and communication networks have enabled the development of automated driving systems. Autonomous cruise control (ACC) and cooperative ACC (CACC) systems are two popular types of these technologies, which can be implemented to enhance safety, traffic flow, driving comfort and energy economy. This PhD thesis develops robust and adaptive controllers for plug-in hybrid electric vehicles (PHEVs), with the Toyota Plug-in Prius as the baseline vehicle, in order to enable them to perform safe and robust car-following and platooning with improved vehicle performance. Three controllers are designed here to achieve three main goals. The first goal of this thesis is the development of a real-time Ecological ACC (Eco-ACC) system for PHEVs, that is robust to uncertainties. A novel adaptive tube-based nonlinear model predictive control (AT-NMPC) approach to the design of Eco-ACC systems is proposed. Through utilizing two separate models to define the constrained optimal control problem, this method takes into account uncertainties, modeling errors and delayed data in the design of the controller and guaranties robust constraint handling for the assumed uncertainty bounds. {In addition, it adapts to changes in order to improve the control performance when possible.} Furthermore, a Newton/GMRES fast solver is employed to implement the designed AT-NMPC in real-time. The second goal is the development of a real-time Ecological CACC (Eco-CACC) system that can simultaneously satisfy the frequency-domain and time-domain platooning criteria. A novel distributed reference governor (RG) approach to the constraint handling of vehicle platoons equipped with CACC is presented. RG sits behind the controlled string stable system and keeps the output inside the defined constraints. Furthermore, to improve the platoon's energy economy, a controller is presented for the leader's control using NMPC method, assuming it is a PHEV. The third objective of this thesis is the control of heterogeneous platoons using an adaptive control approach. A direct model reference adaptive controller (MRAC) is designed that enforces a string stable behavior on the vehicle platoon despite different dynamical models of the platoon members and the external disturbances acting on the systems. The proposed method estimates the controller coefficients on-line to adapt to the disturbances such as wind, changing road grade and also to different vehicle dynamic behaviors. The main purpose of all three controllers is to maintain the driving safety of connected vehicles in car-following and platooning while being real-time implementable. In addition, when there is a possibility for performance enhancement without sacrificing safety, ecological improvement is also considered. For each designed controller, Model-in-the-Loop (MIL) simulations and Hardware-in-the-Loop (HIL) experiments are performed using high-fidelity vehicle models in order to validate controllers' performance and ensure their real-time implementation capability