IDENTIFYING AND MONITORING THE ROLES OF CAVITATION IN HEATING FROM HIGH-INTENSITY FOCUSED ULTRASOUND

For high-intensity focused ultrasound (HIFU) to continue to gain acceptance for cancer treatment it is necessary to understand how the applied ultrasound interacts with gas trapped in the tissue. The presence of bubbles in the target location have been thought to be responsible for shielding the incoming pressure and increasing local heat deposition due to the bubble dynamics. We lack adequate tools for monitoring the cavitation process, due to both limited visualization methods and understanding of the underlying physics. The goal of this project was to elucidate the role of inertial cavitation in HIFU exposures in the hope of applying noise diagnostics to monitor cavitation activity and control HIFU-induced cavitation in a beneficial manner. A number of approaches were taken to understand the relationship between inertial cavitation signals, bubble heating, and bubble shielding in agar-graphite tissue phantoms. Passive cavitation detection (PCD) techniques were employed to detect inertial bubble collapses while the temperature was monitored with an embedded thermocouple. Results indicate that the broadband noise amplitude is correlated to bubble-enhanced heating. Monitoring inertial cavitation at multiple positions throughout the focal region demonstrated that bubble activity increased prefocally as it diminished near the focus. Lowering the HIFU duty cycle had the effect of maintaining a more or less constant cavitation signal, suggesting the shielding effect diminished when the bubbles had a chance to dissolve during the HIFU off-time. Modeling the effect of increasing the ambient temperature showed that bubbles do not collapse as violently at higher temperatures due to increased vapor pressure inside the bubble. Our conclusion is that inertial cavitation heating is less effective at higher temperatures and bubble shielding is involved in shifting energy deposition at the focus. The use of a diagnostic ultrasound imaging system as a PCD array was explored. Filtering out the scattered harmonics from the received RF signals resulted in a spatially- resolved inertial cavitation signal, while the amplitude of the harmonics showed a correlation with temperatures approaching the onset of boiling. The result is a new tool for detecting a broader spectrum of bubble activity and thus enhancing HIFU treatment visualization and feedback.

A FLOW-THROUGH ACOUSTIC WAVEGUIDE FOR TWO-PHASE BUBBLY FLOW VOID FRACTION MEASUREMENT

In the Spallation Neutron Source (SNS) facility at Oak Ridge National Laboratory (ORNL), the deposition of a high-energy proton beam into the liquid mercury target forms bubbles whose asymmetric collapse cause Cavitation Damage Erosion (CDE) to the container walls, thereby reducing its usable lifetime. One proposed solution for mitigation of this damage is to inject a population of microbubbles into the mercury, yielding a compliant and attenuative medium that will reduce the resulting cavitation damage. This potential solution presents the task of creating a diagnostic tool to monitor bubble population in the mercury flow in order to correlate void fraction and damage. Details of an acoustic waveguide for the eventual measurement of two-phase mercury-helium flow void fraction are discussed. The assembly’s waveguide is a vertically oriented stainless steel cylinder with 5.08cm ID, 1.27cm wall thickness and 40cm length. For water experiments, a 2.54cm thick stainless steel plate at the bottom supports the fluid, provides an acoustically rigid boundary condition, and is the mounting point for a hydrophone. A port near the bottom is the inlet for the fluid of interest. A spillover reservoir welded to the upper portion of the main tube allows for a flow-through design, yielding a pressure release top boundary condition for the waveguide. A cover on the reservoir supports an electrodynamic shaker that is driven by linear frequency sweeps to excite the tube. The hydrophone captures the frequency response of the waveguide. The sound speed of the flowing medium is calculated, assuming a linear dependence of axial mode number on modal frequency (plane wave). Assuming that the medium has an effective-mixture sound speed, and that it contains bubbles which are much smaller than the resonance radii at the highest frequency of interest (Wood’s limit), the void fraction of the flow is calculated. Results for water and bubbly water of varying void fraction are presented, and serve to demonstrate the accuracy and precision of the apparatus.

The War Between Mice and Elephants

Recent measurement based studies reveal that most of the Internet connections are short in terms of the amount of traffic they carry (mice), while a small fraction of the connections are carrying a large portion of the traffic (elephants). A careful study of the TCP protocol shows that without help from an Active Queue Management (AQM) policy, short connections tend to lose to long connections in their competition for bandwidth. This is because short connections do not gain detailed knowledge of the network state, and therefore they are doomed to be less competitive due to the conservative nature of the TCP congestion control algorithm. Inspired by the Differentiated Services (Diffserv) architecture, we propose to give preferential treatment to short connections inside the bottleneck queue, so that short connections experience less packet drop rate than long connections. This is done by employing the RIO (RED with In and Out) queue management policy which uses different drop functions for different classes of traffic. Our simulation results show that: (1) in a highly loaded network, preferential treatment is necessary to provide short TCP connections with better response time and fairness without hurting the performance of long TCP connections; (2) the proposed scheme still delivers packets in FIFO manner at each link, thus it maintains statistical multiplexing gain and does not misorder packets; (3) choosing a smaller default initial timeout value for TCP can help enhance the performance of short TCP flows, however not as effectively as our scheme and at the risk of congestion collapse; (4) in the worst case, our proposal works as well as a regular RED scheme, in terms of response time and goodput.

Recognition of Human Action Using Moment-Based Feature

The performance of different classification approaches is evaluated using a view-based approach for motion representation. The view-based approach uses computer vision and image processing techniques to register and process the video sequence. Two motion representations called Motion Energy Images and Motion History Image are then constructed. These representations collapse the temporal component in a way that no explicit temporal analysis or sequence matching is needed. Statistical descriptions are then computed using moment-based features and dimensionality reduction techniques. For these tests, we used 7 Hu moments, which are invariant to scale and translation. Principal Components Analysis is used to reduce the dimensionality of this representation. The system is trained using different subjects performing a set of examples of every action to be recognized. Given these samples, K-nearest neighbor, Gaussian, and Gaussian mixture classifiers are used to recognize new actions. Experiments are conducted using instances of eight human actions (i.e., eight classes) performed by seven different subjects. Comparisons in the performance among these classifiers under different conditions are analyzed and reported. Our main goals are to test this dimensionality-reduced representation of actions, and more importantly to use this representation to compare the advantages of different classification approaches in this recognition task.