Vascular characteristic determination with correspondence modeling of a vascular tree
Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.
1. A method of calculating a fractional flow reserve, comprising:
receiving a plurality of 2-D images, each said 2-D image comprising a plurality of image regions each containing a vascular segment;
automatically determining groups of said image regions based on a comparison of the image regions for at least one feature-level vascular homology, the image regions of each group including a vascular segment in common;
automatically selecting at least one image region from each of a plurality of said groups, the plurality of groups comprising a group for a stenotic vascular segment, and groups for one or more vascular segments comprising vascular branches in a crown of the stenotic vascular segment;
automatically measuring vascular characteristics from the selected image regions; and
automatically calculating fractional flow reserve of the stenotic vascular segment, based on said vascular characteristics.
2. The method of claim 1 , wherein said vascular characteristics comprise vascular width.
3. The method of claim 1 , wherein said vascular characteristics comprise vascular width as a function of position along a length of a vascular segment.
4. The method of claim 1 , wherein said vascular characteristics comprise vascular resistance.
5. The method according to claim 1 , wherein said 2-D images are X-ray angiography images.
6. The method of claim 1 , wherein said selecting comprises determination of the image region of least foreshortening.
7. The method of claim 1 , wherein comparison of the image regions comprises determining parameters of rays projecting from respective planes at which the image regions were acquired to a radiant imaging source.
8. The method of claim 1 , wherein comparison of the image regions comprises registration of the image regions.
9. The method of claim 1 , wherein comparison of the image regions comprises comparison of characteristics of the contained vascular segments.
10. The method of claim 1 , wherein said vascular segment comprises an image of a branch of the cardiac artery.
11. The method of claim 1 , wherein said calculating comprises calculating the vascular characteristics from a model of a vasculature including the vascular segments, said calculating comprising:
providing a model including:
said groups of image regions, the regions being arranged with respect to one another, wherein
each said group represents a modeled location in said vasculature as a sample group;
selecting from said model one or more of said vascular location for the calculation of said vascular characteristic;
selecting for each said selected vascular location at least one of said sample groups associated with said vascular location; and
determining automatically said vascular characteristic by calculation based on said selected sample groups.
12. The method of claim 11 , wherein each of said plurality of sample groups comprises at least a portion of a 2-D image of said vasculature, and wherein said vascular characteristics comprise measurements of vascular tortuosity based on the curvature of a pathway between two or more regions in said 2-D image, said pathway substantially following the course of samples comprised in a vascular segment region.
13. A method of calculating and using vascular characteristics calculated from 2-D images of a vascular tree, comprising:
automatically associating by comparison to each other portions of said 2-D images comprising images of common segments of said vascular tree for at least one feature-level vascular homology;
automatically performing calculations to generate said vascular characteristics from different groups of said associated portions; and
automatically using said vascular characteristics to estimate a functional parameter of the imaged vascular tree.
14. The method of claim 13 , further comprising relating segment-associated values calculated based on new image data to said vascular tree of a vasculature, said relating comprises:
determining that a region of a first digital image and a region of a second digital image record a common segment of said vascular tree, the region of the first digital region being already identified as recording a particular segment;
calculating said vascular characteristic from the region of the second digital image; and
associating said vascular characteristic to the particular segment.
15. The method of claim 14 , wherein said associating, performing, and using is without reference to a 3-D reconstruction of the vascular tree.