A model for the propagation and scattering of ultrasound in tissue

Electronics Institute, Technical University of Denmark, Lyngby.
The Journal of the Acoustical Society of America (Impact Factor: 1.5). 02/1991; 89(1):182-90. DOI: 10.1121/1.400497
Source: PubMed


An inhomogeneous wave equation is derived describing propagation and scattering of ultrasound in an inhomogeneous medium. The scattering term is a function of density and propagation velocity perturbations. The integral solution to the wave equation is combined with a general description of the field from typical transducers used in clinical ultrasound to yield a model for the received pulse-echo pressure field. Analytic expressions are found in the literature for a number of transducers, and any transducer excitation can be incorporated into the model. An example is given for a concave, nonapodized transducer in which the predicted pressure field is compared to a measured field.

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Available from: Jørgen Arendt Jensen, Sep 11, 2014
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    • "Transducer and scattering object enter the mathematical formulation as boundary conditions whose type depend on the medium and the scattering material. In medical ultrasound, e.g., the scattering object is a density inhomogeneity in the medium, which requires a special theoretical model [18]. In our situation we consider two simpler special cases: scattering by a rigid object in a fluid medium, and scattering by a cavity in a solid medium. "
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    ABSTRACT: This article investigates the restoration of ultrasonic pulse-echo C-scan images by means of deconvolution with a point spread function (PSF). The deconvolution concept from linear system theory (LST) is linked to the wave equation formulation of the imaging process, and an analytic formula for the PSF of planar transducers is derived. For this analytic expression, different numerical and analytic approximation schemes for evaluating the PSF are presented. By comparing simulated images with measured C-scan images, we demonstrate that the assumptions of LST in combination with our formula for the PSF are a good model for the pulse-echo imaging process. To reconstruct the object from a C-scan image, we compare different deconvolution schemes: the Wiener filter, the ForWaRD algorithm, and the Richardson-Lucy algorithm. The best results are obtained with the Richardson-Lucy algorithm with total variation regularization. For distances greater or equal twice the near field distance, our experiments show that the numerically computed PSF can be replaced with a simple closed analytic term based on a far field approximation.
    IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control 03/2015; 62(3):531-544. DOI:10.1109/TUFFC.2014.006717 · 1.51 Impact Factor
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    • "We investigate in this section the processing of simple US synthetic data simulated with a realistic PSF. A synthetic US signal was generated with the FIELD II software [21]. The true PSF h has a central frequency of f = 3 MHz whereas the input PSF h 0 central frequency was set to f 0 = 3.1 MHz in order to model the PSF estimation error. "
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    ABSTRACT: In the field of ultrasound imaging, resolution enhancement is an up-to-date challenging task. Many device-based approaches have been proposed to overcome the low resolution nature of ultrasound images but very few works deal with post-processing methods. This paper investigates a novel approach based on semi-blind deconvolution formulation and alternating direction method framework in order to perform the ultrasound image restoration task. The algorithm performance is addressed using optical images and synthetic ultrasound data for a various range of criteria. The results demonstrate that our technique is more robust to uncertainties in the a priori ultrasonic pulse than classical non-blind deconvolution methods.
    2013 20th IEEE International Conference on Image Processing (ICIP); 09/2013
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    • "Enhanced delivery requires precise control over both ultrasound and microbubble parameters. Although ultrasound parameters are easily controlled and models exist for the propagation of acoustic waves through tissue (Jensen 1991; Zemp et al. 2003), the properties of microbubbles are less predictable. First, the majority of current microbubble production techniques use agitation methods (Klibanov 2002), which generate microbubbles with a wide range of diameters (i.e., polydisperse) "
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    ABSTRACT: Focal drug delivery to a vessel wall facilitated by intravascular ultrasound and microbubbles holds promise as a potential therapy for atherosclerosis. Conventional methods of microbubble administration result in rapid clearance from the bloodstream and significant drug loss. To address these limitations, we evaluated whether drug delivery could be achieved with transiently stable microbubbles produced in real time and in close proximity to the therapeutic site. Rat aortic smooth muscle cells were placed in a flow chamber designed to simulate physiological flow conditions. A flow-focusing microfluidic device produced 8 μm diameter monodisperse microbubbles within the flow chamber, and ultrasound was applied to enhance uptake of a surrogate drug (calcein). Acoustic pressures up to 300 kPa and flow rates up to 18 mL/s were investigated. Microbubbles generated by the flow-focusing microfluidic device were stabilized with a polyethylene glycol-40 stearate shell and had either a perfluorobutane (PFB) or nitrogen gas core. The gas core composition affected stability, with PFB and nitrogen microbubbles exhibiting half-lives of 40.7 and 18.2 s, respectively. Calcein uptake was observed at lower acoustic pressures with nitrogen microbubbles (100 kPa) than with PFB microbubbles (200 kPa) (p < 0.05, n > 3). In addition, delivery was observed at all flow rates, with maximal delivery (>70% of cells) occurring at a flow rate of 9 mL/s. These results demonstrate the potential of transiently stable microbubbles produced in real time and in close proximity to the intended therapeutic site for enhancing localized drug delivery.
    Ultrasound in medicine & biology 04/2013; 39(7). DOI:10.1016/j.ultrasmedbio.2013.01.023 · 2.21 Impact Factor
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