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CN-116309390-B - Identification of vascular branches

CN116309390BCN 116309390 BCN116309390 BCN 116309390BCN-116309390-B

Abstract

In part, the present invention relates to automated methods for branch detection of blood vessels imaged using intravascular modalities such as OCT, IVUS, or other imaging modalities. In one embodiment, representations of a-lines and frames generated using an intravascular imaging system are used to identify candidate branches of a vessel. One or more operators, such as filters, may be applied to remove false positives associated with other detections.

Inventors

  • Ajay Gopinat

Assignees

  • 光学实验室成像公司

Dates

Publication Date
20260505
Application Date
20170414
Priority Date
20160414

Claims (20)

  1. 1. A method of detecting one or more branches of a blood vessel, comprising: storing one or more intravascular image datasets of the vessel; Detecting a lumen boundary in an image generated from an image dataset of the blood vessel, wherein the image has a first dimension and a second dimension; Specifying a search distance T; Defining a search area bounded by the lumen boundary detected and a boundary offset from the lumen boundary by the search distance T; detecting edges in the search area, and Candidate branch regions are identified in response to the detected edges.
  2. 2. The method of claim 1, further comprising: Flattening the image using a first image processing operator; Applying median smoothing to the image using a second image processing operator, and Smoothing is applied to the image using a third image processing operator to generate a filtered image.
  3. 3. The method of claim 2, further comprising: A first min-max pair in the filtered image is identified, wherein one or more distances between the first min-max pair define a first search window.
  4. 4. A method as in claim 3, further comprising: a second min-max pair in the filtered image is identified, wherein one or more distances between the second min-max pair define a second search window.
  5. 5. A method as in claim 3, further comprising: Searching along the first dimension in the corresponding preprocessed input image within a first search window.
  6. 6. The method of claim 5, further comprising designating pixels below a noise floor threshold that are located in the first search window as corresponding to the candidate branch region.
  7. 7. The method of claim 6, wherein the noise floor threshold is less than 2mm.
  8. 8. The method of claim 6, further comprising dividing the candidate branch region into three bands, wherein the sum of the widths of the three bands is equal to T.
  9. 9. The method of claim 8, further comprising accumulating pixels corresponding to the candidate branch regions along each portion for each band.
  10. 10. The method of claim 9, wherein a particular portion in the band is marked as corresponding to a branch if the portion has more than 10% to 30% of the pixels marked as candidate branches.
  11. 11. The method of claim 10, further comprising outputting a set of portions of each band corresponding to the candidate branches.
  12. 12. The method of claim 3, further comprising generating a branching matrix using a pulled-back frame, the frame comprising angle data.
  13. 13. The method of claim 8, further comprising isolating pixels corresponding to groupings of all three bands from pixels corresponding to groupings of the first two bands to select pixels corresponding to side branches.
  14. 14. The method of claim 12, further comprising removing a guidewire region from the branching matrix.
  15. 15. The method of claim 14, further comprising eliminating branches that occur only in one frame.
  16. 16. The method of claim 12, further comprising replicating the branch matrix to account for zero crossing overlaps.
  17. 17. The method of claim 13, wherein the first band ranges from 0 to T/3, and wherein the second band ranges from T/3 to 2/3T, and wherein the third band ranges from 2/3T to T.
  18. 18. The method of claim 8, wherein the first band ranges from 0 to T/3, and wherein the second band ranges from T/3 to 2/3T, and wherein the third band ranges from 2/3T to T.
  19. 19. The method of claim 1, further comprising displaying the one or more detected branches in a user interface.
  20. 20. The method of claim 1, further comprising validating one or more candidate branches using a branch matrix generated using pixels selected from two or more bands, wherein a sum of the bands is T.

Description

Identification of vascular branches Technical Field The present invention relates generally to systems and methods suitable for use in the field of intravascular diagnosis and imaging, and more particularly to systems and methods that support the identification of side branches, intersections, or other portions or features of a blood vessel. Background Coronary artery disease is one of the leading causes of death worldwide. Better ability to diagnose, monitor and treat coronary artery disease can be of great importance to save lives. Intravascular optical coherence tomography (optical coherence tomography, OCT) is a catheter-based imaging modality that uses light rays to peep the coronary wall and generate images thereof for investigation. OCT can provide video-rate in vivo tomography with micron-scale resolution within diseased vessels using coherent light, interferometry, and micro-optical techniques. Viewing subsurface structures with high resolution using fiber optic probes makes OCT particularly useful for minimally invasive imaging of internal tissues and organs. OCT enables this level of detail, which enables a clinician to diagnose and monitor the progression of coronary artery disease. OCT images provide high resolution visualization of coronary morphology and can be used alone or in combination with other information (such as angiographic data and other sources of patient data) to aid diagnosis and treatment. OCT imaging of various parts of a patient's body provides a useful diagnostic tool for doctors and others. For example, coronary artery imaging by intravascular OCT may show narrowed or stenosed locations that reduce blood flow and increase the risk of ischemia. Such information facilitates the cardiologist's choice between invasive coronary bypass surgery and catheter-based minimally invasive surgery (such as angioplasty or stent delivery) to alleviate stenosis and restore blood flow. The presence of arterial side branches in the stenosed region also affects blood flow through the artery and is therefore an important factor in the design of a patient's treatment plan. Quantitative assessment of vascular pathology and its progression involves calculation of different quantitative measures, such as pressure drop, which may depend on accurate identification of fluid volume and lumen geometry (including side branch geometry). Side branches extending from the lumen are often not easily identified in OCT images. In part, this is because the side branches may be obscured by guide wires used in various OCT probes, or by stent struts, blood, and shadows. Furthermore, shadows and other imaging data artifacts can be difficult to resolve and eliminate. As a result, important landmarks along the length of the artery (such as side branches) may be mistaken for tissue or not identified at all. Considering that the placement of the stent on the side branch may be detrimental or should be done consciously at the time of execution, a reliable technique that can identify the side branch is needed. The present invention addresses these challenges and others. Disclosure of Invention In part, the present invention relates to a method of detecting one or more branches of a blood vessel. The method includes storing one or more intravascular image datasets of a vessel, each intravascular dataset including a plurality of A-lines, detecting a lumen boundary in a first A-line image generated from a set of A-lines from the plurality of A-lines, wherein the first A-line image has an r-dimension and an A-line dimension, designating a search distance T, defining a search region bounded by the detected lumen boundary and a boundary offset from the lumen boundary by a distance T, detecting an edge of the search region, and identifying candidate branch regions in response to the detected edge. In one embodiment, the method includes flattening an A-line image using a first image processing operator, applying median smoothing to the A-line image using a second image processing operator, and applying smoothing to the A-line image using a third image processing operator to generate a filtered image. In one embodiment, the method includes identifying a first min-max pair (min-max pair) in the filtered image, wherein one or more distances between the first min-max pair define a first search window. In one embodiment, the method includes identifying a second min-max pair in the filtered image, wherein one or more distances between the second min-max pair define a second search window. In one embodiment, the method includes searching along an r-dimension in the respective preprocessed input image within a first search window. In one embodiment, the method includes designating pixels below a noise floor threshold that are located in the first search window as corresponding to the candidate branch region. In one embodiment, the noise floor threshold is less than about 2mm. In one embodiment, the method includes dividing the candidate b