Calibration of multiple aperture ultrasound probes
The quality of ping-based ultrasound imaging is dependent on the accuracy of information describing the precise acoustic position of transmitting and receiving transducer elements. Improving the quality of transducer element position data can substantially improve the quality of ping-based ultrasound images, particularly those obtained using a multiple aperture ultrasound imaging probe, i.e., a probe with a total aperture greater than any anticipated maximum coherent aperture width. Various systems and methods for calibrating element position data for a probe are described.
1. A method of calibrating an ultrasound probe, comprising the steps of:
securing an ultrasound probe relative to an object, the probe containing an array of ultrasound transducer elements;
retrieving first transducer element position data defining a position of each transducer element of a first group of transducer elements relative to a common coordinate system;
imaging the object with the first group of transducer elements to obtain a reference image, wherein with the first group of transducer elements imaging is dependent on the first transducer element position data;
retrieving second transducer element position data defining a position of each transducer element of a second group of transducer elements relative to the common coordinate system, the second group of elements including elements not within the first group of elements;
imaging the object with the second group of transducer elements to obtain a test image, wherein imaging with the second group of transducer elements is dependent on the second position data;
quantifying a first error between the reference image and the test image; and
iteratively optimizing the second transducer element position data until the first error is at a minimum.
2. The method of claim 1 , further comprising storing raw echo data received while imaging the object with the second group of transducer elements.
3. The method of claim 2 , wherein the iteratively optimizing step comprises:
adjusting the second position data to create first adjusted position data;
re-beamforming the stored echo data using the first adjusted position data to form a second test image of the reflectors;
quantifying a second error between the second test image and the reference image; and
determining whether the second error is less than the first error.
4. The method of claim 3 , wherein adjusting the second position data comprises adjusting one or more of an x position variable, a y position variable, and a z position variable for a plurality of elements of the second group.
5. The method of claim 3 , wherein adjusting the second position data describing the position of the transducer elements of the second group of transducer elements includes adjusting a position of a reference point of the group of transducer elements and an angle of a surface of the group of transducer elements, but does not include adjusting a spacing between the elements of the second array.
6. The method of claim 5 , further comprising, after a first iteratively optimizing step, performing a second iteratively optimizing step comprising:
adjusting the first adjusted position data, including adjusting a spacing between at least two transducer elements of the second group of transducer elements to create second adjusted position data;
re-beamforming the stored echo data using the second adjusted position data to form a third test image of the reflectors;
quantifying a third error between the third test image and the reference image; and
determining whether the third error is less than the second error.
7. The method of claim 3 , wherein adjusting the second position data comprises changing one or more variables by a random quantity.
8. The method of claim 3 , further comprising, after determining whether the second error is less than the firs error, applying a second adjustment to the second position data to create second adjusted position data, wherein a magnitude or a direction of the second adjustment is based on the determination of whether the second error is less than the first error.
9. The method of claim 1 , wherein iteratively optimizing the second position data comprises optimizing using a least squares optimization process.
10. The method of claim 1 , wherein quantifying the first error comprises quantifying a distance between positions of reflectors in the reference image relative to positions of the same reflectors in the test image.
11. The method of claim 1 , wherein quantifying the first error comprises quantifying a difference in brightness between reflectors in the reference image and reflectors in the test image.
12. The method of claim 1 , wherein quantifying the first error comprises quantifying a difference between a pattern of reflectors and holes in the reference image compared with a pattern of holes and reflectors in the test image.
13. The method of claim 1 , wherein the reference image and the test image are three-dimensional volumetric images of a three-dimensional pattern of reflectors, holes, or both reflectors and holes.
14. The method of claim 1 , wherein the object comprises living tissue.
15. The method of claim 1 , further comprising identifying positions of reflectors in the object and fitting a mathematically defined curve to a detected pattern of reflectors.
16. The method of claim 15 , wherein the curve is a straight line.
17. The method of claim 15 , wherein the step of quantifying a first error comprises calculating a coefficient of determination that quantifies a degree of fit of the curve to the pattern of reflectors.