Passive acoustic mapping (PAM) is a promising imaging method that enables real-time three-dimensional monitoring of ultrasound therapy through the reconstruction of acoustic emissions passively received on an array of ultrasonic sensors. A passive beamforming method is presented that provides greatly improved spatial accuracy over the conventionally used time exposure acoustics (TEA) PAM reconstruction algorithm. Both the Capon beamformer and the robust Capon beamformer (RCB) for PAM are suggested as methods to reduce interference artifacts and improve resolution, which has been one of the experimental issues previously observed with TEA. Simulation results that replicate the experimental artifacts are shown to suggest that bubble interactions are the chief cause. Analysis is provided to show that these multiple bubble artifacts are generally not reduced by TEA, while Capon-based methods are able to reduce the artifacts. This is followed by experimental results from in vitro experiments and in vivo oncolytic viral therapy trials that show improved results in PAM, where RCB is able to more accurately localize the acoustic activity than TEA.
Passive acoustic mapping significantly outperformed the conventional hyperecho technique as an ultrasound-based HIFU monitoring method, as both a detector of lesion occurrence and a method of mapping the position of ablated tissue.
Modern speech enhancement algorithms achieve remarkable noise suppression by means of large recurrent neural networks (RNNs). However, large RNNs limit practical deployment in hearing aid hardware (HW) form-factors, which are battery powered and run on resource-constrained microcontroller units (MCUs) with limited memory capacity and compute capability. In this work, we use model compression techniques to bridge this gap. We define the constraints imposed on the RNN by the HW and describe a method to satisfy them. Although model compression techniques are an active area of research, we are the first to demonstrate their efficacy for RNN speech enhancement, using pruning and integer quantization of weights/activations. We also demonstrate state update skipping, which reduces the computational load. Finally, we conduct a perceptual evaluation of the compressed models to verify audio quality on human raters. Results show a reduction in model size and operations of 11.9× and 2.9×, respectively, over the baseline for compressed models, without a statistical difference in listening preference and only exhibiting a loss of 0.55dB SDR. Our model achieves a computational latency of 2.39ms, well within the 10ms target and 351× better than previous work.
Cold case squads have garnered much attention; however, they have yet to undergo significant empirical scrutiny. In the present study, the authors interviewed investigators and reviewed 189 solved and unsolved cold cases in Washington, D.C., to determine whether there are factors that can predict cold case solvability. In the interviews, new information from witnesses or information from new witnesses was cited as the most prevalent reason for case clearance. The case reviews determined that there were factors in each of the following domains that predicted whether cases would be solved during cold case investigations: Crime Context, Initial Investigation Results, Basis for Opening Cold Case, and Cold Case Investigator Actions. The results suggest that it is possible to prioritize cold case work based on the likelihood of investigations leading to clearances.
Passive acoustic mapping (PAM) has been recently demonstrated as a method of monitoring focused ultrasound therapy by reconstructing the emissions created by inertially cavitating bubbles (Jensen et al 2012 Radiology 262 252-61). The published method sums energy emitted by cavitation from the focal region within the tissue and uses a threshold to determine when sufficient energy has been delivered for ablation. The present work builds on this approach to provide a high-intensity focused ultrasound (HIFU) treatment monitoring software that displays both real-time temperature maps and a prediction of the ablated tissue region. This is achieved by determining heat deposition from two sources: (i) acoustic absorption of the primary HIFU beam which is calculated via a nonlinear model, and (ii) absorption of energy from bubble acoustic emissions which is estimated from measurements. The two sources of heat are used as inputs to the bioheat equation that gives an estimate of the temperature of the tissue as well as estimates of tissue ablation. The method has been applied to ex vivo ox liver samples and the estimated temperature is compared to the measured temperature and shows good agreement, capturing the effect of cavitation-enhanced heating on temperature evolution. In conclusion, it is demonstrated that by using PAM and predictions of heating it is possible to produce an evolving estimate of cell death during exposure in order to guide treatment for monitoring ablative HIFU therapy.
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