Lumen Morphology and Vascular Resistance Measurements Data Collection Systems, Apparatus and Methods

• US 20110071404A1
• Filed: 09/22/2010
• Published: 03/24/2011
• Est. Priority Date: 09/23/2009
• Status: Active Grant

• Current Assignee: LightLab Imaging, Inc. (Abbott Laboratories)
• Original Assignee: LightLab Imaging, Inc. (Abbott Laboratories)
• Inventors: Schmitt, Joseph M., Friedman, Joel M., Petroff, Christopher, Elbasiony, Amr
• Granted Patent: US 9,138,147 B2
• Time in Patent Office: Days
• US Class Current: 600/479
• CPC Class Codes: A61B 2034/102 Modelling of surgical devices, implants or prosthesis
• CPC Class Codes: A61B 5/0066 Optical coherence imaging
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/6852 Catheters
• CPC Class Codes: A61F 2/82 Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents stent-grafts for tubular structures of the body other than blood vessels A61F2/04; stent-grafts for blood vessels A61F2/07; instruments specially adapted for placement or removal of stents or stent-grafts A61F2/95; for closing wounds, or holding wounds closed A61B17/04 - A61B17/115; dilators A61M29/00
• CPC Class Codes: G06T 2200/24 involving graphical user interfaces [GUIs]
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10101 Optical tomography; Optical coherence tomography [OCT]
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A method and apparatus of automatically locating in an image of a blood vessel the lumen boundary at a position in the vessel and from that measuring the diameter of the vessel. From the diameter of the vessel and estimated blood flow rate, a number of clinically significant physiological parameters are then determined and various user displays of interest generated. One use of these images and parameters is to aid the clinician in the placement of a stent. The system, in one embodiment, uses these measurements to allow the clinician to simulate the placement of a stent and to determine the effect of the placement. In addition, from these patient parameters various patient treatments are then performed.

Claims

1. An automated computer-based method of evaluating a segment of a lumen of a blood vessel, the method comprising the steps of:

a. collecting a set of data regarding a lumen of length L using an optical coherence tomography system, the set comprising a plurality of cross sectional areas at a plurality of positions along the length;

b. determining a vascular resistance ratio (VRR) using a processor and at least a portion of the set of data; and

c. determining a characteristic of at least a portion of the segment disposed along the length L in response to the vascular resistance ratio.

2. The method of claim 1 wherein the region contains a stenotic lesion and the characteristic is a value denoting a stenotic lesion.

3. The method of claim 2 further comprising the step of displaying at least one of a numerical or graphical measure of a stent length and diameter used to treat the stenotic lesion.

4. The method of claim 1 wherein the step of determining the vascular resistance ratio is performed using a lumped resistor model.

14. The method of claim 1 wherein the image is an OCT image,

31. The method of claim 1 further comprising the steps of:

calculating FRR from VRR; and

determining if the value of FRR is clinically significant.

5. A method for automatically identifying the luminal border in a vascular image comprises the steps of:

a. generating a mask of the lumen image using a computer;

b. defining a plurality of scan lines in said mask;

c. identifying a region as tissue on each scan line;

d. defining contour segments in response to the plurality of scan lines and the region of tissue on each scan line;

e. identifying valid neighboring contour segments;

f. interpolating missing contour data between valid neighboring contour segments; and

g. fitting a contour to the missing contour data.

6. The method of claim 5 further comprising the step of detecting and removing guide wire and similar artifacts.

7. The method of claim 5 wherein said step of identifying the tissue region comprises the steps of:

a. finding a plurality of start/stop pairs on each scan line;

b. calculating thickness and gap of each said start/stop pair;

c. calculating weight based on said thickness and said gap; and

d. defining the tissue region based on the largest weight of tissue and gap.

8. The method of claim 5 wherein said step of defining connected contour comprises the steps of:

a. finding the scan line with the largest weight;

b. searching for discontinuities in both directions from the scan line to define a valid segment; and

c. identifying the root of the contour as the longest of the valid segments.

9. The method of claim 5 wherein said step of identifying valid neighboring contour comprises finding the nearest clockwise and counter-clockwise neighbors of each of the contour segments that pass angular, radial, and Euclidean distance thresholds.

10. The method of claim 6 wherein said step of detecting and removing guide wire shadow artifact comprises the steps of:

a. calculating the line-to-line slope of the contour;

b. calculating the expected smooth contour slope moving from line-to-line;

c. calculating the difference between the expected smooth slope and the line-to-line slope; and

d. removing contour points that in response to a threshold for the difference.

11. The method of claim 5 wherein said step of interpolating missing data comprises the steps of:

a. identifying required interpolation control points with valid contour data on both ends of the missing contour segment; and

b. using the control points to interpolate the missing contour segment data.

12. The method of claim 11 wherein the steps are performed on all missing contour segments that need to be interpolated.

13. The method of claim 8 wherein the step of searching for discontinuities comprises the steps of:

a. calculating a scan line-to-scan line offset change histogram;

b. smoothing said histogram;

c. identifying the smallest change with zero count from the histogram; and

d. using the smallest change as a continuity measure.

15. An automated method for quantifying a vascular resistance comprising the steps of:

a. selecting proximal and distal frames of an in-situ vascular image;

b. calculating actual vascular resistance of the vascular segment enclosed by said proximal and said distal frames;

c. calculating a total vascular resistance of the vascular segment; and

d. calculating vascular resistance ratio using said actual vascular resistance and said total vascular resistance of a blood vessel.

16. The method of claim 15 wherein said step of calculating actual vascular resistance comprises the steps of:

a. extracting luminal contours of all frames enclosed by the proximal and the distal frames inclusive;

b. calculating cross-sectional areas from the extracted contours;

c. constructing a smooth area graph; and

d. calculating the actual vascular resistance calculation in response to the smooth area graph.

17. The method of claim 15 wherein said step of calculating the total vascular resistance comprises the steps of:

a. fitting a shape between said proximal and said distal frames; and

b. calculating cross-sectional areas of the shape at all frame positions enclosed by said proximal and said distal frames inclusive.

18. The method of claim 16 wherein said step of constructing a smooth area graph comprises the steps of:

a. constructing a graph using the cross-sectional areas;

b. interpolating missing area values on the graph; and

c. smoothing the resulting graph.

19. The method of claim 15 wherein the image is an OCT image.

20. A computer-based method of placing a stent comprising:

a. measuring flow parameters in the region of interest in an image of a vessel;

b. simulating the placement of the stent in the region of interest;

c. recalculating the flow parameters in the region of interest; and

d. repeating steps b and c until the desired flow parameter result is obtained.

21. The method of claim 20 further comprising the measuring of stent malapposition in the simulated placement.

22. The method of claim 21 further comprising replacement of the stent until the stent malapposition is within an acceptable value.

23. A method of calculating LSRI comprising the steps of:

determining the difference in pressure between a first location and a second location within a vessel of interest;

determining the velocity of blood flow through the first and second locations within the vessel of interest;

integrating the Poiseuille equation between the first location and the second location to obtain the laminar flow pressure drop between the first and the second locations; and

solving the following equation;

LSRI=(pressure difference between the first and second location/velocity of blood flow)−

laminar flow pressure drop between the first and the second locations.

24. A computer-based system for evaluating a region of a lumen, the system comprising:

an image acquisition system for collecting a set of data regarding a lumen of length L, the set comprising a plurality of cross sectional areas at a plurality of positions along the length l;

a memory storing the set of cross sectional areas at a plurality of positions along the length L; and

a processor in communication with the memory, the processor configured to;

determine a vascular resistance ratio (VRR) for the length of vessel L in response to at least a portion of the set of data in memory; and

determine a characteristic of at least a portion of the region disposed along the length L in response to the vascular resistance ratio.

25. The system of claim 24 further comprising a display for displaying at least one of a numerical or graphical measure of stent length used to treat a stenotic lesion.

26. A computer based system for automatically identifying the luminal border in an in-situ vascular image comprising:

an acquisition system for collecting an image in situ of a vascular lumen;

a memory storing the image; and

a processor in communication with the memory, the processor configured to;

generate a mask of the lumen image;

define a plurality of scan lines in said mask;

identify a region as tissue on each scan line;

define contour segments in response to the plurality of scan lines and the region of tissue on each scan line;

identify valid neighboring contour segments;

interpolate missing contour data between valid neighboring contour segments; and

fit a contour to the missing contour data.

27. An automated OCT system for quantifying a vascular resistance comprising:

an OCT acquisition system comprising an interferometer and a catheter for collecting a plurality of images in situ to view a vascular lumen;

a memory storing the image; and

a processor in communication with the memory, the processor configured to;

select proximal and distal frames of an OCT image;

calculate actual vascular resistance of the vascular segment enclosed by said proximal and said distal frames;

calculate total vascular resistance of the vascular segment; and

calculate vascular resistance ratio using said actual vascular resistance and said total vascular resistance.

28. The system of claim 27 wherein the processor further determines a characteristic of the region of the lumen in response to the vascular resistance ratio.

29. A computer-based system for placing a stent comprising:

an image acquisition system for collecting images in situ of a region of interest of a vascular lumen;

a memory storing the images; and

a processor in communication with the memory, the processor configured to;

measure flow parameters in the region of interest in the images;

simulate the placement of the stent in the region of interest;

recalculate the flow parameters in the region of interest; and

repeat the simulation and recalculation until the desired flow parameter result is obtained.

30. The computer based system of claim 29 wherein the image is an OCT image and the image acquisition system comprises an interferometer and a catheter for collecting in-situ vascular images.