Transaxial compression technique for sound velocity estimation

1993 ◽  
Vol 93 (5) ◽  
pp. 3020-3020
Author(s):  
Jonathan Ophir ◽  
Youseph Yazdi
2018 ◽  
Vol 8 (11) ◽  
pp. 2133 ◽  
Author(s):  
Ken Inagaki ◽  
Shimpei Arai ◽  
Kengo Namekawa ◽  
Iwaki Akiyama

Since the sound velocity for medical ultrasound imaging is usually set at 1540 m/s, the ultrasound imaging of a patient with a thick layer of subcutaneous fat is degraded due to variations in the sound velocity. This study proposes a method of compensating for image degradation to correct beamforming. This method uses the sound velocity distribution measured in simultaneous ultrasound (US) and magnetic resonance (MR) imaging. Experiments involving simultaneous imaging of an abdominal phantom and a human neck were conducted to evaluate the feasibility of the proposed method using ultrasound imaging equipment and a 1.5 T MRI scanner. MR-visible fiducial markers were attached to an ultrasound probe that was developed for use in an MRI gantry. The sound velocity distribution was calculated based on the MRI cross section, which was estimated as a corresponding cross section of US imaging using the location of fiducial markers in MRI coordinates. The results of the abdominal phantom and neck imaging indicated that the estimated values of sound velocity distribution allowed beamform correction that yielded compensated images. The feasibility of the proposed method was then evaluated in terms of quantitative improvements in the spatial resolution and signal-to-noise ratio.


Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. U1-U8 ◽  
Author(s):  
Gerson Luis da Silva Ritter

It is known that the propagation velocity of sound waves in water can vary over time. For a 3D seismic survey, if data are acquired in adjacent lines but at different dates, this implies the same reflection point will be recorded at different times. To take this effect into account in seismic processing, it is necessary to measure the sound velocity in water. I have developed a 3D tomographic method that directly estimates it. It assumes a constant sound velocity for a group of shots belonging to a single sail line. Using a picked water-bottom reflection and an initial depth and velocity model, results good for use in subsequent processing can be obtained by estimating only two parameters: the variation of the propagation velocity and a constant vertical shift of the reflector depth in relation to the initial model. The method was tested with both synthetic and real data. The real data results were validated using two methods. First, I analyzed the histogram of the residuals of the final updated model. Second, I used a specially modified Kirchhoff migration algorithm to migrate the sea bottom. The main advantages of this method are that it takes into account the sea-bottom dips to estimate the velocities and it can be applied to each sail line separately. Also, the inversion is not ill-conditioned provided that data with large enough offsets are used. As a result, the method is simple to apply.


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