Image processing and patient-specific modeling of blood flow

Patent metadata

Field	Value
Publication number	US-2016296287-A1
Application number	US-201615183341-A
Country	US
Kind code	A1
Filing date	Jun 15, 2016
Priority date	Aug 12, 2010
Publication date	Oct 13, 2016
Grant date	—

Abstract

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

First claim

1 - 184 . (canceled)

185 . 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.

186 . The method as recited in claim 185 , 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.

187 . The method as recited in claim 186 , 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.

188 . The method as recited in claim 187 , 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 plurality of candidate locations for each vertex.

189 . The method as recited in claim 186 , 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 plurality of candidate locations of neighboring vertices of the anatomical model, wherein each of the plurality of connection rules results in a corresponding mesh candidate.

190 . The method as recited in claim 189 , 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.

191 . The method as recited in claim 185 , wherein 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 comprises:
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.

192 . The method as recited in claim 185 , further comprising:
in response to determining that the variation among values is not significant, displaying results of the hemodynamic index without receiving user input.

193 . The method as recited in claim 185 , 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.

194 . The method as recited in claim 193 , wherein displaying at least one of the plurality of mesh candidates comprises:
determining a cross-sectional area of one or more mesh candidates of the plurality of mesh candidates.

195 . The method as recited in claim 193 , wherein displaying at least one of the plurality of mesh candidates comprises:
simultaneously displaying two or more mesh candidates of the plurality of mesh candidates.

196 . The method as recited in claim 193 , 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.

197 . The method as recited in claim 185 , 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.

198 . 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.

199 . The apparatus as recited in claim 198 , 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.

200 . The apparatus as recited in claim 199 , 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.

201 . The apparatus as recited in claim 200 , 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 plurality of candidate locations for each vertex.

202 . The apparatus as recited in claim 199 , wherein the means for generating the plurality of mesh candidates based on the one or more candidate locations comprises:
means for generating the plurality of mesh candidates by enforcing a plurality of connection rules for connecting the plurality of candidate locations of neighboring vertices of the anatomical model, wherein each of the plurality of connection rules results in a corresponding mesh candidate.

203 . The apparatus as recited in claim 202 , wherein the means for generating the plurality of mesh candidates based on the one or more candidate locations further comprises:
means for projecting each of the plurality of mesh candidates onto a learned shape space of the artery using an active shape model.

204 . The apparatus as recited in claim 198 , wherein the means for determining whether a variation

Assignees

• Heartflow Inc

Inventors

• Taylor Charles A

Classifications

• G06T12/30

  Image post-processing, e.g. metal artefact correction · CPC title

• G06T11/10

  Texturing; Colouring; Generation of textures or colours (retouching, inpainting or scratch removal G06T5/77) · CPC title

• G16H70/00

  ICT specially adapted for the handling or processing of medical references · CPC title

• G16H30/20

  for handling medical images, e.g. DICOM, HL7 or PACS · CPC title

• G06F18/24

  Classification techniques · CPC title