3-D vessel tree surface reconstruction

Patent metadata

Field	Value
Publication number	US-10140733-B1
Application number	US-201715702897-A
Country	US
Kind code	B1
Filing date	Sep 13, 2017
Priority date	Sep 13, 2017
Publication date	Nov 27, 2018
Grant date	Nov 27, 2018

Abstract

Reconstructing 3-D vessel geometry of a vessel includes: receiving a plurality of 2-D rotational X-ray images of the vessel; extracting vessel centerline points for normal cross sections of each of the plurality of 2-D images; establishing a correspondence of the centerline points from a registration of the centerline points with a computed tomography (CT) 3-D centerline, the registration being an affine or deformable transformation; constructing a 3-D centerline vessel tree skeleton of the vessel from the centerline points of the 2-D images; constructing an initial 3-D vessel surface having a uniform radius normal to the 3-D centerline vessel tree skeleton; defining sample points based sampling on median radii to the 3-D centerline vessel tree skeleton of the initial 3-D vessel surface; and constructing a target 3-D vessel surface by deforming the initial vessel surface using the sample points to provide a reconstructed 3-D vessel geometry of the vessel.

First claim

What is claimed is:

1. A method for reconstructing 3-D vessel geometry of a vessel, the method comprising:
receiving a plurality of 2-D rotational X-ray images of the vessel;

extracting vessel centerline points for normal cross sections of each of the plurality of 2-D images;
establishing a correspondence of the centerline points from a registration of the centerline points with a computed tomography (CT) 3-D centerline, the registration being an affine or deformable transformation;
constructing a 3-D centerline vessel tree skeleton of the vessel from the centerline points of the 2-D images;
constructing an initial 3-D vessel surface from a uniform radius normal to the 3-D centerline vessel tree skeleton;
defining sample points based sampling on median radii to the 3-D centerline vessel tree skeleton of the initial 3-D vessel surface; and
constructing a target 3-D vessel surface by deforming the initial vessel surface using the sample points to provide a reconstructed 3-D vessel geometry of the vessel.

2. The method of claim 1 , wherein the vessel is a coronary artery and the 2-D images have a same cardiac phase.

3. The method of claim 1 , wherein the 2-D images are angiography images.

4. The method of claim 1 , wherein:
the extracting vessel centerlines is accomplished in a form of ordered point sets, S i , from angiographic images, where i is the i th image, and each point set S i is a projection of a transformed 3-D centerline model M according to the following:
S i =P i T ( M,θ i ),

wherein P i is a camera projection operator corresponding to image i, and T(M, θ i ) is a defined transformation model;
wherein given M and S i , Gaussian mixture models (GMM) are employed to find transformations T(M, θ i ) that minimize the following cost function:
J ( S i ,M,θ i )=∫(GMM( S i )−GMM( P i T ( M,θ i ))) p dx,

wherein J(Si,M, θ i ) is an energy function that measures a distance of two Gaussian mixture models (GMM) where p∈[0, 2].

5. The method of claim 4 , wherein:
the establishing the correspondence of centerline points is accomplished using a bipartite graph that corresponds to a linear assignment process that finds a matching vessel point for every projected model point in each image, wherein the bipartite graph represents a first set of vertices (i) that represent transformed, projected model points of the vessel centerline points, and a second set of vertices (j) that represent associated segment points of the CT 3-D centerline from a CT volume and CT reconstruction, wherein weights at edges, cij, of paired vertices of the first and second sets of vertices are computed using a distance of spatial locations of the respective vertices d ij according to the following:
c i,j ={d i,j if d i,j <dth;
∞ if d i,j— d th },

wherein d th is a defined distance threshold.

6. The method of claim 5 , wherein:
the constructing the 3-D centerline vessel tree skeleton is accomplished according to the following:

min

a
j

,

b
i

⁢

∑

i
=
1

n

⁢

∑

j
=
1

m

⁢

k

i
,
j

⁢

d
⁡

(

Q
⁡

(

a
j

,

b
i

)

,

x

i
,
j

)

,

wherein: n is a number of 3-D points that are seen in m views; camera (j) is parameterized by a vector a j ; b i is the i th centerline point; Q(a j , b i ) is the projection of 3-D point b i on image (j); x i,j is the i th 2-D centerline point measurement on image (j); d(,) is a Euclidean distance between the associated points Q(a j , b i ) and x i,j ; and, k i,j ∈[0, 1] is a confidence measurement of x i,j .

7. The method of claim 6 , wherein:
the constructing the initial 3-D vessel surface is accomplished by generating a point cloud of circles around and perpendicular to a tangential direction of the associated centerline, each one of the point cloud of circles having a defined radius r, and computing an initial surface mesh via a Poisson surface reconstruction method using the point cloud of circles.

8. The method of claim 1 , wherein:
the extracting vessel centerlines is accomplished using a technique of model-guided extraction.

9. The method of claim 1 , wherein:
the establishing the correspondence of centerline points is accomplished using the affine transformation, the affine transformation comprising a 2-D to 2-D affine transformation or a 2-D to 3-D affine transformation.

10. The method of claim 1 , wherein:
the establishing the correspondence of centerline points is accomplished using the thin-plate spline transformation.

11. The method of claim 1 , wherein:
the constructing the 3-D centerline vessel tree skeleton is accomplished using 2-D points of the extracted vessel centerlines using a bundle adjustment based approach.

12. The method of claim 1 , wherein:
the defining the sample points is accomplished by clustering points of the initial surface mesh to nearest points of the 3-D centerline vessel tree skeleton, calculating the median radius for each of the points of the 3-D centerline vessel tree skeleton to the points of the initial surface mesh clustered to the point of the 3-D centerline vessel tree skeleton, and defining the sample points for each of the points of the 3-D centerline vessel tree skeleton as along a circle with the me

Assignees

• Siemens Healthcare Gmbh

Inventors

• Liu Yuan
• Sun Shanhui
• Tuysuzoglu Ahmet
• Lauritsch Guenter
• Chen Terrence

Classifications

• G06T2207/30101

  Blood vessel; Artery; Vein; Vascular · CPC title

• G06T2207/10116

  X-ray image · CPC title

• G06T2207/30172

  Centreline of tubular or elongated structure · CPC title

• G06T2207/10016

  Video; Image sequence · CPC title

• G06T7/0012

  Biomedical image inspection · CPC title