Wall-to-Wall Vessel Segmentation in US Imaging Using a Combination of VFI Data and US Imaging Data

• US 20180064421A1
• Filed: 09/08/2016
• Published: 03/08/2018
• Est. Priority Date: 09/08/2016
• Status: Active Grant

• Current Assignee: B-K Medicals Aps
• Original Assignee: B-K Medicals Aps
• Inventors: Moshavegh, Ramin, Jensen, Jorgen Arendt, Martins, Bo
• Granted Patent: US 10,456,113 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 8/06 Measuring blood flow measuring volume flow in general G01F, e.g. G01F1/66, G01F1/72; measuring speed of fluids in general G01P5/00
• CPC Class Codes: A61B 8/0891 for diagnosis of blood vessels
• CPC Class Codes: A61B 8/463 characterised by displaying multiple images or images and diagnostic data on one display composite display in devices for short-range imaging with acoustic waves G01S7/52074
• CPC Class Codes: A61B 8/5223 for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012
• CPC Class Codes: A61B 8/5246 combining images from the same or different imaging techniques, e.g. color Doppler and B-mode
• CPC Class Codes: A61B 8/5269 involving detection or reduction of artifacts for image enhancement algorithms G06T5/00
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

An ultrasound imaging system includes an image processor and a velocity processor configured to process beamformed ultrasound data representing structure flowing through a tubular object and generate, respectively, an image indicative of the tubular object and vector flow imaging data indicative of the structure flowing through the tubular object. The system further includes a segmentation processor configured to segment the tubular object from the image based on a combination of both the vector flow imaging data and the image, wherein a resulting segmentation extends from wall-to-wall of the tubular object. The system further includes a display configured to display the image with the segmentation and the vector flow imaging data superimposed thereover, with the vector flow imaging data extending from wall-to-wall within the tubular object. The system further includes the automatic computation of largest vessel diameter (Dmax), and/or quantitative flow measures such as Peak systolic velocity (PSV) and/or volume flow.

Claims

1. An ultrasound system, comprising:

an image processor configured to process beamformed ultrasound data representing structure flowing through a tubular object and generate an image indicative of the tubular object based thereon;

a velocity processor configured to process the beamformed ultrasound data and generate vector flow imaging data indicative of the structure flowing through the tubular object based thereon, wherein the vector flow imaging data includes at least an axial velocity component signal and one or more transverse velocity components indicating a direction and a speed of the structure flowing through the tubular object;

a segmentation processor configured to segment the tubular object from the image based on a combination of both the vector flow imaging data and the image, wherein a resulting segmentation extends from wall-to-wall of the tubular object; and

a display configured to display the image with the segmentation and the vector flow imaging data superimposed thereover, with the vector flow imaging data extending from wall-to-wall within the tubular object.

2. The system of claim 1, wherein the segmentation processor segments the tubular object by:

generating a marker image from velocity flow data and the image;

using the marker image with a marker-controlled region growing algorithm to delineate tubular object boundaries in the image;

rejecting non-tubular object structure; and

employing an inter-frame co-registration of segmentations across to ensure a tubular object segmented in each frame are in correspondence with a same tubular object segmented in neighboring frames in a sequence, wherein tubular objects matching in all frames are considered to be the actual tubular structure.

3. The system of claim 2, wherein the segmentation processor determines an inner marker from the vector flow imaging data, an outer marker from only the image and the vector flow imaging data, and combines the inner and outer markers to produce the marker image.

4. The system of claim 3, wherein the segmentation processor determines the inner marker by employing a gray scale skeletonization of the vector flow imaging data, wherein the skeletonization includes extracting a center line of the vector flow imaging data, which indicates locations inside the tubular object boundary.

5. The system of claim 4, wherein the segmentation processor extracts the center line by generating a distance map of an inverse of a velocity flow imaging binary mask, determining ridge lines on the distance map as an initial skeleton of the velocity flow imaging mask, and generating a final skeleton using only velocity flow imaging data pixels lying inside the initial skeleton and larger than a predetermined percent of the velocity flow imaging data.

6. The system of claim 3, wherein the segmentation processor determines the outer marker by extracting strong specular regions from the image, which are considered outside of the tubular structure.

7. The system of claim 2, wherein the segmentation processor employs a marker-controlled watershed image processing approach, using the marker image, to segment boundaries of tubular-like structures located by the marker image.

8. The system of claim 7, wherein the segmentation processor generates a binary mask containing strong specular regions, and multiplies the binary mask by an inverted and dilated version of a velocity flow information mask, which generates an outer marker mask that highlights the strong specular regions and ensures the mask does not cover an area with flow.

9. The system of claim 2, wherein the segmentation processor rejects non-tubular object structure based on at least one of a size, a shape, an amount of flow, and/or a vascularity to ensure only tubular structure is retained.

10. The system of claim 1, further comprising:

an operating system processor configured to adaptively adjust a pulse repetition frequency during imaging based on the wall-to-wall segmentation.

11. The system of claim 1, further comprising:

an operating parameter processor configured to tune echo cancelling during imaging based on the wall-to-wall segmentation.

12. The system of claim 1, further comprising:

a measurement processor configured to estimate a peak systolic velocity based on the wall-to-wall segmentation.

13. The system of claim 1, further comprising:

a measurement processor configured to estimate a largest vessel diameter based on the wall-to-wall segmentation.

14. The system of claim 1, further comprising:

a measurement processor configured to estimate a volume flow based on the wall-to-wall segmentation.

15. A method, comprising:

generating an image indicative of a tubular object from beamformed ultrasound data representing structure flowing through the tubular object;

generating vector flow imaging data indicative of the structure flowing through the tubular object from beamformed ultrasound, wherein the vector flow imaging data includes at least an axial velocity component signal and one or more transverse velocity components indicating a direction and a speed of the structure flowing through the tubular object;

generating a wall-to-wall segmentation of the tubular object from the image with a combination of the vector flow imaging data and the image; and

visually presenting the image with the segmentation and the vector flow imaging data superimposed thereover, with the vector flow imaging data extending from wall-to-wall within the tubular object.

16. The method of claim 15, wherein generating a wall-to-wall segmentation includes:

generating a marker image from velocity flow data and the image;

using the marker image with a marker-controlled region growing algorithm to delineate tubular object boundaries in the image;

rejecting non-tubular object structure; and

employing an inter-frame co-registration of segmentations across to make sure that a tubular object segmented in each frame are in correspondence with a same tubular object segmented in neighboring frames in a sequence.

17. The method of claim 16, wherein the generating the marker image includes:

determining an inner marker by employing gray scale skeletonization of the velocity flow imaging data, which extracts a center-line of flow data, which indicates locations inside the tubular object boundary;

determining an outer marker by extracting strong specular regions from the image, which are considered outside of the tubular structure; and

combining the inner and outer markers to produce the marker image.

18. The method of claim 17, wherein using the marker image with a marker-controlled region growing algorithm includes:

generating a binary mask containing strong specular regions; and

multiplying the binary mask by an inverted and dilated version of a velocity flow information mask, which generates an outer marker mask that highlights the strong specular regions and ensures the mask does not cover an area with flow.

19. The method of claim 18, wherein non-tubular object structure is rejected based on at least one of a size, a shape, an amount of flow, and/or a vascularity to ensure only tubular structure is retained.

20. The method of claim 19, wherein employing the inter-frame co-registration includes identifying only regions matching in at least 80% of the frames as the actual tubular structure.

21. The method of claim 16, further comprising:

at least one of adaptively adjusting a pulse repetition frequency during imaging based on the segmentation or tuning echo cancelling during imaging based on the segmentation.

22. The method of claim 16, further comprising:

at least one of estimating a peak systolic velocity based on the segmentation, estimating a largest vessel diameter based on the segmentation, or estimating a volume flow based on the segmentation.

23. A computer readable storage medium encoded with computer readable instructions, which, when executed by a processor, causes the processor to:

construct an image indicative of a tubular object from beamformed ultrasound data representing structure flowing through the tubular object;

estimate vector flow imaging data indicative of the structure flowing through the tubular object from beamformed ultrasound, wherein the vector flow imaging data includes at least an axial velocity component signal and one or more transverse velocity components indicating a direction and a speed of the structure flowing through the tubular object;

compute a wall-to-wall segmentation of the tubular object from the image with a combination of the vector flow imaging data and the image; and

display the image with the segmentation and the vector flow imaging data superimposed thereover, with the vector flow imaging data extending from wall-to-wall within the tubular object.