Volume 125, Issue 4, April 2009
Index of content:
- BIOACOUSTICS 
125(2009); http://dx.doi.org/10.1121/1.3087427View Description Hide Description
Modeling the acoustical process of soft biological tissue imaging and understanding the consequences of the approximations required by such modeling are key steps for accurately simulating ultrasonic scanning as well as estimating the scattering coefficient of the imaged matter. In this document, a linear solution to the inhomogeneous ultrasonicwave equation is proposed. The classical assumptions required for linearization are applied; however, no approximation is made in the mathematical development regarding density and speed of sound. This leads to an expression of the scattering term that establishes a correspondence between the signal measured by an ultrasound transducer and an intrinsic mechanical property of the imaged tissues. This expression shows that considering the scattering as a function of small variations in the density and speed of sound around their mean values along with classical assumptions in this domain is equivalent to associating the acoustical acquisition with a measure of the relative longitudinal bulk modulus. Comparison of the model proposed to Jensen’s earlier model shows that it is also appropriate to perform accurate simulations of the acoustical imaging process.
125(2009); http://dx.doi.org/10.1121/1.3081393View Description Hide Description
Both mechanically induced acoustic cavitation and thermally induced boiling can occur during high intensity focused ultrasound (HIFU) medical therapy. The goal was to monitor the temperature as boiling was approached using magnetic resonance imaging (MRI). Tissue phantoms were heated for 20 s in a 4.7-T magnet using a 2-MHz HIFU source with an aperture and radius of curvature of 44 mm. The peak focal pressure was 27.5 MPa with corresponding beam width of 0.5 mm. The temperature measured in a single MRI voxel by water proton resonance frequency shift attained a maximum value of only after 7 s of continuous HIFU exposure when boiling started. Boiling was detected by visual observation, by appearance on the MR images, and by a marked change in the HIFU source power. Nonlinear modeling of the acoustic field combined with a heat transfer equation predicted after 7 s of exposure. Averaging of the calculated temperature field over the volume of the MRI voxel yielded a maximum of that agreed with the MR thermometry measurement. These results have implications for the use of MRI-determined temperature values to guide treatments with clinical HIFU systems.
125(2009); http://dx.doi.org/10.1121/1.3089589View Description Hide Description
Auditory evoked potentials (AEPs) were recorded during echolocation in a false killer whale Pseudorca crassidens. An electronically synthesized and played-back (“phantom”) echo was used. Each electronic echo was triggered by an emitted biosonar pulse. The echo had a spectrum similar to that of the emitted biosonar clicks, and its intensity was proportional to that of the emitted click. The attenuation of the echo relative to the emitted click and its delay was controlled by the experimenter. Four combinations of echoattenuation and delay were tested (−31 dB, 2 ms), (−40 dB, 4 ms), (−49 dB, 8 ms), and (−58 dB, 16 ms); thus, attenuation and delay were associated with a rate of 9 dB of increased attenuation per delay doubling. AEPs related to emitted clicks displayed a regular amplitude dependence on the click level. Echo-related AEPs did not feature amplitude dependence on echoattenuation or emitted click levels, except in a few combinations of the lowest values of these two variables. The results are explained by a hypothesis that partial forward masking of the echoes by the preceding emitted sonar pulses serves as a kind of automatic gain control in the auditory system of echolocating odontocetes.
125(2009); http://dx.doi.org/10.1121/1.3089588View Description Hide Description
This article presents a method for reducing the computation time required for estimating cumulative sound exposure levels. Sound propagation has to be computed from every source position to every desired receiver location; so if there are many source positions, then the problem can quickly become computationally expensive. The authors' solution to this problem is to extract all possible source-receiver pathways and to cluster these with a self-organizing neural net. Sound propagation is modeled only for the cluster centroids and extrapolated for the entire geographic region. The tool is illustrated for the example of a marine seismic survey over a tropical coral reef. Resident fish species were expected not to flee the reef, but to stay among the corals for the entire duration of the survey. In such cases, the modeling of cumulative sound exposure levels is sometimes requested as part of environmental impact assessments. The tool developed combines a seismic sourcemodel, a near-field sound propagationmodel, and a far-field sound propagationmodel. The neural network reduces the computation time by a factor of 55. The cost is an error in modeled received levels of less than .