Method and system for improved hemodynamic computation in coronary arteries

Patent metadata

Field	Value
Publication number	US-9747525-B2
Application number	US-201514706142-A
Country	US
Kind code	B2
Filing date	May 7, 2015
Priority date	Jun 16, 2014
Publication date	Aug 29, 2017
Grant date	Aug 29, 2017

Abstract

Systems and methods for non-invasive assessment of an arterial stenosis, comprising include segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data. A hemodynamic index for the stenosis region is computed in each of the plurality of mesh candidates. It is determined whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region.

First claim

The invention claimed is:

1. A method for non-invasive assessment of an arterial stenosis, comprising:
segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data;
computing a hemodynamic index for the stenosis region in each of the plurality of mesh candidates; and
determining whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region by determining whether the variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is entirely below the threshold for the clinical decision or is entirely above the threshold for the clinical decision.

2. The method as recited in claim 1 , wherein segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data comprises:
segmenting the artery from the medical imaging data to generate the anatomical model;
determining one or more candidate locations for each vertex of the anatomical model; and
generating the plurality of mesh candidates based on the one or more candidate locations.

3. The method as recited in claim 2 , wherein determining one or more candidate locations for each vertex of the anatomical model comprises:
determining a probability that a respective voxel is a boundary point for each voxel along a surface normal within a predetermined distance at each vertex.

4. The method as recited in claim 3 , wherein determining one or more candidate locations for each vertex of the anatomical model further comprises:
determining voxels associated with a probability above a threshold value as the one or more candidate locations for each vertex.

5. The method as recited in claim 3 , wherein generating the plurality of mesh candidates based on the one or more candidate locations comprises:
assigning a confidence score to each of the plurality of mesh candidates by averaging the probability associated with each vertex in a respective mesh candidate; and
applying cluster analysis to reduce a number of the plurality of mesh candidates based on the confidence score.

6. The method as recited in claim 2 , wherein generating the plurality of mesh candidates based on the one or more candidate locations comprises:
generating the plurality of mesh candidates by enforcing a plurality of connection rules for connecting the one or more candidate locations of neighboring vertices of the anatomical model, wherein each of the plurality of connection rules results in a corresponding mesh candidate.

7. The method as recited in claim 6 , wherein generating the plurality of mesh candidates based on the one or more candidate locations further comprises:
projecting each of the plurality of mesh candidates onto a learned shape space of the artery using an active shape model.

8. The method as recited in claim 1 , further comprising:
in response to determining that the variation among values is not significant, displaying results of the hemodynamic index without receiving user input.

9. The method as recited in claim 1 , further comprising:
in response to determining that the variation among values is significant:
displaying at least one of the plurality of mesh candidates; and
receiving user input to select and/or edit the at least one of the plurality of mesh candidates.

10. The method as recited in claim 9 , wherein displaying at least one of the plurality of mesh candidates comprises:
displaying each of the plurality of mesh candidates overlaid on a same multiplanar reformatted planes of the artery.

11. The method as recited in claim 9 , wherein displaying at least one of the plurality of mesh candidates comprises:
displaying each of the plurality of mesh candidates overlaid on an instance of a same multiplanar reformatted planes of the artery in a side-by-side configuration.

12. The method as recited in claim 9 , wherein displaying at least one of the plurality of mesh candidates comprises:
displaying mesh candidates of the plurality of mesh candidates having a value of the hemodynamic index for the stenosis region above the threshold on a first portion of a display; and
displaying mesh candidates of the plurality of mesh candidates having the value of the hemodynamic index for the stenosis region below the threshold on a second portion of the display.

13. The method as recited in claim 1 , wherein computing a hemodynamic index for the stenosis region in each of the plurality of mesh candidates comprises:
simulating blood flow and pressure in each of the plurality of mesh candidates for the artery of the patient; and
computing a fractional flow reserve value for the stenosis region in each of the plurality of mesh candidates based on the blood flow and pressure simulations.

14. An apparatus for non-invasive assessment of an arterial stenosis, comprising:
means for segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data;
means for computing a hemodynamic index for the stenosis region in each of the plurality of mesh candidates; and
means for determining whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region by determining whether the variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is entirely below the threshold for the clinical decision or is entirely above the threshold for the clinical decision.

15. The apparatus as recited in claim 14 , wherein the means for segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data comprises:
means for segmenting the artery from the medical imaging data to generate the anatomical model;
means for determining one or more candidate locations for each vertex of the anatomical model; and
means for generating the plurality of mesh candidates based on the one or more candidate locations.

16. The apparatus as recited in claim 15 , wherein the means for determining one or more candidate locations for each vertex of the anatomical model comprises:
means for determining a probability that a respective voxel is a boundary point for each voxel along a surface normal within a predetermined distance at each vertex.

17. The apparatus as recited in claim 16 , wherein the means for determining one or more candidate locations for each vertex of the anatomical model further comprises:
means for determining voxels associated with a probability above a threshold value as the one or more candidate locations for each vertex.

18. The apparatus as recited in claim 16 , wherein the means for generating the plurality of mesh candidates based on the one or more candidate locations comprises:
means for assigning a confidence score to each of the plurality of mesh candidates by averaging the probability associated with each vertex in a respective mesh candidate; and
means for applying cluster analysis to reduce a number of the plurality of mesh candidates based on the confidence score.

19. The apparatus as recited in claim 15 , wherein the means for gen

Assignees

• Siemens Healthcare Gmbh

Inventors

• Sauer Frank
• Zheng Yefeng
• Sharma Puneet
• Georgescu Bogdan

Classifications

• G06T7/0012 Primary

  Biomedical image inspection · CPC title

• G06F18/23

  Clustering techniques · CPC title

• G06F18/2415

  based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06T2207/20124

  Active shape model [ASM] · CPC title