Magnetic resonance flow imaging techniques provide quantitative and qualitative information that can be attributed to flow related clinical pathologies. Clinical use of MR flow quantification requires fast acquisition and reconstruction schemes, and minimization of post processing errors. The purpose of this work is to provide improvements to the post processing of volumetric phase contrast MRI (PCMRI) data, identify a source of flow bias for cine PCMRI that has not been previously reported in the literature, and investigate a dynamic approach to image bulk cerebrospinal fluid (CSF) drainage in ventricular shunts. The proposed improvements are implemented as three research projects.

In the first project, the improvements to post processing are made by proposing a new approach to estimating noise statistics for a single spiral acquisition, and using the estimated noise statistics to generate a mask distinguishing flow regions from background noise and static tissue in an image volume. The mask is applied towards reducing the computation time of phase unwrapping. The proposed noise estimation is shown to have comparable noise statistics as that of a vendor specific noise dynamic scan, with the added advantage of reduced scan time. The sparse flow region subset of the image volume is shown to speed up phase unwrapping for multidirectional velocity encoded 3D PCMRI scans. The second research project explores the extent of bias in cine PCMRI based flow estimates is investigated for CSF flow in the cerebral aqueduct. The dependance of the bias on spatial and temporal velocity gradient components is described. A critical velocity threshold is presented to prospectively determine the extent of bias as a function of scan acquisition parameters.

Phase contrast MR imaging is not sensitive to measure bulk CSF drainage. A dynamic approach using a CSF label is investigated in the third project to detect bulk flow in a ventricular shunt. The proposed approach uses a preparatory pulse to label CSF signal and a variable delay between the preparatory pulse and data acquisition enables tracking of the CSF bulk flow.

Human operators have difficulty driving cranes quickly, accurately, and safely because of the slow response of heavy crane structures, non-intuitive control interfaces, and payload oscillations. Recently, a novel hand-motion crane control system has been proposed to improve performance by coupling an intuitive control interface with an element that reduces the complex oscillatory behavior of the payload. Hand-motion control allows operators to drive a crane by simply moving a hand-held radio-frequency tag through the desired path. Real-time location sensors are used to track the movements of the tag and the tag position is used in a feedback control loop to drive the crane. An input shaper is added to eliminate dangerous payload oscillations. However, tag position measurements are corrupted by noise. It is important to understand the noise properties so that appropriate filters can be designed to mitigate the effects of noise and improve tracking accuracy. This work discusses implementing filtering techniques to address the issue of noise in the operating environment. Five different filters are used on experimentally-acquired tag trajectories to reduce noise. The filtered trajectories are then used to drive crane simulations. Filter performance is evaluated with respect to the energy usage of the crane trolley, the settling time of the crane payload oscillations, and the safety corridor of the crane trajectory. The effects of filter window lengths on these parameters are also investigated. An adaptive filtering technique, namely the Kalman filter, adapts to the noise characteristics of the workspace to minimize the tag tracking error and performs better than the other filtering techniques examined.