A study of stone fragmentation in shock wave lithotripsy by customizing the acoustic field and waveform shape

Shock wave lithotripsy is the preferred treatment modality for kidney stones in the United States. Despite clinical use for over twenty-five years, the mechanisms of stone fragmentation are still under debate. A piezoelectric array was employed to examine the effect of waveform shape and pressure distribution on stone fragmentation in lithotripsy. The array consisted of 170 elements placed on the inner surface of a 15 cm-radius spherical cap. Each element was driven independently using a 170 individual pulsers, each capable of generating 1.2 kV. The acoustic field was characterized using a fiber optic probe hydrophone with a bandwidth of 30 MHz and a spatial resolution of 100 μm. When all elements were driven simultaneously, the focal waveform was a shock wave with peak pressures p+ =65±3MPa and p−=−16±2MPa and the −6 dB focal region was 13 mm long and 2 mm wide. The delay for each element was the only control parameter for customizing the acoustic field and waveform shape, which was done with the aim of investigating the hypothesized mechanisms of stone fragmentation such as spallation, shear, squeezing, and cavitation. The acoustic field customization was achieved by employing the angular spectrum approach for modeling the forward wave propagation and regression of least square errors to determine the optimal set of delays. Results from the acoustic field customization routine and its implications on stone fragmentation will be discussed.

An Information Theoretic Framework for Field Monitoring Using Autonomously Mobile Sensors

We consider a mobile sensor network monitoring a spatio-temporal field. Given limited cache sizes at the sensor nodes, the goal is to develop a distributed cache management algorithm to efficiently answer queries with a known probability distribution over the spatial dimension. First, we propose a novel distributed information theoretic approach in which the nodes locally update their caches based on full knowledge of the space-time distribution of the monitored phenomenon. At each time instant, local decisions are made at the mobile nodes concerning which samples to keep and whether or not a new sample should be acquired at the current location. These decisions account for minimizing an entropic utility function that captures the average amount of uncertainty in queries given the probability distribution of query locations. Second, we propose a different correlation-based technique, which only requires knowledge of the second-order statistics, thus relaxing the stringent constraint of having a priori knowledge of the query distribution, while significantly reducing the computational overhead. It is shown that the proposed approaches considerably improve the average field estimation error by maintaining efficient cache content. It is further shown that the correlation-based technique is robust to model mismatch in case of imperfect knowledge of the underlying generative correlation structure.

Preferential Field Coverage Through Detour-Based Mobility Coordination

Controlling the mobility pattern of mobile nodes (e.g., robots) to monitor a given field is a well-studied problem in sensor networks. In this setup, absolute control over the nodes’ mobility is assumed. Apart from the physical ones, no other constraints are imposed on planning mobility of these nodes. In this paper, we address a more general version of the problem. Specifically, we consider a setting in which mobility of each node is externally constrained by a schedule consisting of a list of locations that the node must visit at particular times. Typically, such schedules exhibit some level of slack, which could be leveraged to achieve a specific coverage distribution of a field. Such a distribution defines the relative importance of different field locations. We define the Constrained Mobility Coordination problem for Preferential Coverage (CMC-PC) as follows: given a field with a desired monitoring distribution, and a number of nodes n, each with its own schedule, we need to coordinate the mobility of the nodes in order to achieve the following two goals: 1) satisfy the schedules of all nodes, and 2) attain the required coverage of the given field. We show that the CMC-PC problem is NP-complete (by reduction to the Hamiltonian Cycle problem). Then we propose TFM, a distributed heuristic to achieve field coverage that is as close as possible to the required coverage distribution. We verify the premise of TFM using extensive simulations, as well as taxi logs from a major metropolitan area. We compare TFM to the random mobility strategy—the latter provides a lower bound on performance. Our results show that TFM is very successful in matching the required field coverage distribution, and that it provides, at least, two-fold query success ratio for queries that follow the target coverage distribution of the field.