3-D vessel tree surface reconstruction method

Patent metadata

Field	Value
Publication number	US-9934566-B2
Application number	US-201615207540-A
Country	US
Kind code	B2
Filing date	Jul 12, 2016
Priority date	Jul 14, 2015
Publication date	Apr 3, 2018
Grant date	Apr 3, 2018

Abstract

A method for 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; constructing a 3-D centerline vessel tree skeleton of the vessel; constructing an initial 3-D vessel surface having a uniform radius normal to the 3-D centerline vessel tree skeleton; and constructing a target 3-D vessel surface by deforming the initial vessel surface 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, 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;
constructing a 3-D centerline vessel tree skeleton of the vessel;
constructing an initial 3-D vessel surface having a uniform radius normal to the 3-D centerline vessel tree skeleton; and
constructing a target 3-D vessel surface by deforming the initial vessel surface to provide a reconstructed 3-D vessel geometry of the vessel,
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 pϵ[0, 2].

2. The method of claim 1 , wherein:
the vessel is a coronary artery.

3. The method of claim 1 , wherein:
the 2-D images have a same cardiac phase.

4. The method of claim 1 , wherein:
the 2-D images are received using ECG signals or ECG gated acquisition signals.

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

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

7. The method of claim 1 , 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 a prior 3-D vessel shape of the vessel segmented from a DynaCT volume and DynaCT 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.

8. The method of claim 7 , 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 ϵ[0, 1] is a confidence measurement of x i,j .

9. The method of claim 8 , 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.

10. The method of claim 9 , wherein:
the constructing the target 3-D vessel surface is accomplished using 2-D information through 3-D to 2-D projection to deform the initial 3-D vessel surface according to the following:
T={S ( v i )| i= 1 . . . L},

wherein T represents the initial 3-D vessel surface having radius r, where L is the number of vertices (v i ), i is an iterative parameter, and S(v i ) is a sampling point along a searching profile for (v i ), wherein a minimum-surface cost function w(v i , S(v i )), subject to smoothness constraints |S(v i )−S(v j )|≤Δ, is solved via the following:

T
*

=

arg
⁢

⁢
min
⁢

⁢
Σ
⁢

⁢

w
⁡

Assignees

• Siemens Healthcare Gmbh

Inventors

• Sun Shanhui
• Kluckner Stefan
• Tuysuzoglu Ahmet
• Kapoor Ankur
• Lauritsch Günter
• Chen Terrence

Classifications

• A61B6/032

  Transmission computed tomography [CT] · CPC title

• A61B6/5211

  involving processing of medical diagnostic data · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G06T2211/404

  Angiography · CPC title

• A61B6/504 Primary

  for diagnosis of blood vessels, e.g. by angiography · CPC title