Ultrasound speckle decorrelation estimation of lung motion and ventilation
A method of estimating lung motion includes collecting multiple ultrasound image data captured at one or more locations of a sample region of tissue. The method further includes comparing the multiple ultrasound image data and determining temporal correlation coefficients between each of the multiple ultrasound image data. The method still further includes displaying an image of the sample region of the tissue with the temporal correlation coefficients identified, thereby indicating lung motion. In further methods, the determined temporal correlation coefficients are used to determine an amount of decorrelation, which can be used to determine strain of the tissue over the sample region and to calculate lung displacements and lung shape changes representing ventilation.
1. A method of estimating lung motion, the method comprising:
capturing, using an ultrasound probe, multiple ultrasound image data at one or more locations of a sample region of tissue;
comparing, using a processor, the multiple ultrasound image data and determining temporal correlation coefficients between each of the multiple ultrasound image data;
displaying an image of the sample region of the tissue with the temporal correlation coefficients identified thereby indicating lung motion;
determining, using the processor, the temporal correlation coefficients at a plurality of lag times; and
determining, using the processor, an amount of ventilation of the sample region from combining the temporal correlation coefficients at the plurality of lag times by changing the relative magnitudes of decorrelation due to motion/displacement and decorrelation due to shape change/deformation.
2. The method of claim 1 , further comprising:
Collecting, using the processor, the multiple ultrasound image data at a plurality of locations of the sample region; and
Comparing, using the processor, the multiple ultrasound image data and determining temporal correlation coefficients for each of the plurality of locations of the sample region.
3. The method of claim 1 , further comprising:
Identifying, using input hardware in communication with the processor, a surface of the sample region based on the determined temporal correlation coefficients.
4. The method of claim 1 , further comprising:
identifying, using the processor, an internal structure of the sample region based on the determined temporal correlation coefficients.
5. The method of claim 1 , wherein collecting, using the the processor, the multiple ultrasound image data captured at the one or more locations of the sample region of tissue comprises collecting, using the processor, multiple ultrasound image data captured from a 1D ultrasound probe, a 2D ultrasound probe, or a 3D ultrasound probe.
6. The method of claim 1 , wherein collecting, using the processor, the multiple ultrasound image data captured at the one or more locations of the sample region of tissue comprises collecting, using the processor, multiple ultrasound image data captured from a plurality of ultrasound probes.
7. The method of claim 1 , further comprising: determining, using the processor, the temporal correlation coefficients between each of the multiple ultrasound image data at different successive lag times.
8. The method of claim 1 , further comprising: determining from the determined temporal correlation coefficients, using the processor, regions within the sample region that demonstrate no motion.
9. The method of claim 8 , wherein the regions of no motion are identified as regions of pneumothoraces.
10. The method of claim 1 , further comprising: determining from the determined temporal correlation coefficients, using the processor, 2D lung motion, or 3D lung motion.
11. The method of claim 1 , wherein the sample region is a region of a lung.
12. The method of claim 1 , further comprising:
continuously monitoring local respiratory motion at multiple sites across the tissue.
13. The method of claim 1 , further comprising:
determining from the determined temporal correlation coefficients, using a processor, an amount of decorrelation between each of the multiple ultrasound image data;
identifying, using input hardware in communication with the processor, two sample sites in the tissue spaced by a distance;
determining, using the processor, displacement differences at the two sample sites based on the amount of decorrelation; and
from the displacement differences and the distance, determining, using the processor, strain of the tissue over the sample region.
14. The method of claim 1 , further comprising:
Determining, using the processor, decorrelation using two different beam correlation widths; and
calculating, using the processor, lung displacements and lung shape changes representing ventilation from the two different bema correlation widths.
15. The method of claim 1 , further comprising:
Identifying, using the processor, local displacements and ventilation by determining amounts of decorrelation for two different beam correlation widths or point spread functions.
16. The method of claim 15 , further comprising:
wherein the amounts of amounts of decorrelation for two different beam correlation widths or point spread functions are determined using:
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where TD (i) and TD (j) are the total decorrelation for the i th sampling and j th sampling, respectively, PSF (i) and PSF (j) are the point spread function during the i th sampling and j th sampling, respectively, LD is the lung surface displacement during the sampling period, which is constant, and SD is the shape induced decorrelation during the same period.
17. The method of claim 1 , further comprising:
identifying from the determined temporal correlation coefficients, using the processor, decorrelation between first ultrasound image data for the sample region; and
identifying from the determined temporal correlation coefficients, using the processor, re-correlation between second ultrasound image data for the sample region.
18. The method of claim 1 , further comprising:
determining, using the processor, the temporal correlation coefficients between each of the multiple ultrasound image data for a surface of the sample region.
19. The method of claim 1 , further comprising:
determining, using the processor, the temporal correlation coefficients between each of the multiple ultrasound image data for a portion of the sample region beneath a surface of the sample region.
20. The method of claim 1 , further comprising:
determining, using the processor, a mean of the temporal correlation coefficients; and
displaying, using the processor, the mean of the temporal correlation coefficients.
21. The method of claim 1 , further comprising:
determining, using the processor, a sum of the temporal correlation coefficients; and
displaying, on a visual display in communication with the processor, the sum of the temporal correlation coefficients.