Calculating the motion vector field for a reconstructed CT scan image

Patent metadata

Field	Value
Publication number	US-9636074-B2
Application number	US-201514918594-A
Country	US
Kind code	B2
Filing date	Oct 21, 2015
Priority date	Oct 21, 2014
Publication date	May 2, 2017
Grant date	May 2, 2017

Abstract

A method for calculating a motion vector field (MVF) in a reconstructed CT scan image, comprises: determining a region of interest (ROI) based on a vessel centreline in a first reconstructed CT scan image, wherein a motion vector field (MVF) for the ROI is to be calculated; determining at least two time control points for calculating the MVF; calculating a motion level factor of the ROI; calculating motion amounts of the MVF corresponding to each of the time control points; determining the direction of the MVF of the ROI; and calculating the MVF of the ROI according to the motion level factor, the motion amounts of the MVF corresponding to each of the time control points and the direction of the MVF.

First claim

The invention claimed is:

1. A method for calculating the motion vector field (MVF) in a reconstructed Computed Tomography (CT) scan image, comprises:
determining a region of interest (ROI) based on a vessel centreline in a first reconstructed CT scan image, wherein a motion vector field (MVF) for the ROI is to be calculated;
determining time control points for calculating the MVF;
calculating a motion level factor of the ROI;
calculating motion amounts of the MVF corresponding to each of the time control points;
determining the direction of the MVF of the ROI;
calculating the MVF of the ROI according to the motion level factor, the motion amounts of the MVF corresponding to each of the time control points and the direction of the MVF; and
reconstructing a target reconstructed image by performing a motion compensation on the first reconstructed CT scan image according to the MVF of the ROI.

2. The method of claim 1 , further comprises:
determining an extension factor for adjusting the MVF after the direction of the MVF is determined; and then
calculating the MVF of the ROI according to the motion level factor, the motion amounts of the MVF corresponding to each of the time control points, the direction of the MVF and the extension factor.

3. The method of claim 2 , wherein, a formula for calculating the MVF of the ROI according to the motion level factor, the motion amounts of the MVF corresponding to each of the time control points, the direction of the MVF and the extension factor is as following:
C t n ,x =ω×c t n ,x =ω×η×φ×c′ t n ,x ,
C t n ,y =ω×c t n ,y =ω×η×φ×c′ t n ,y ,
wherein, c′ t n ,x ,c′ t n ,y are the motion amounts of the MVF corresponding to the time control points t n ,
C t n ,x ,C t n ,y are the motion vector corresponding to the time control points t n , and the MVF of the ROI comprises the motion vectors corresponding to each of the time control points,
ω is the extension factor,
η is the direction of the MVF, and
φ is the motion level factor.

4. The method of claim 2 , wherein, determining the extension factor for adjusting the MVF comprises:
calculating an initial MVF according to the motion level factor, the motion amounts of the MVF corresponding to each of the time control points and the direction of the MVF;
setting at least two assumed extension factor with equal intervals, and calculating extended MVFs extended from the initial MVF by each of the assumed extension factors;
reconstructing extended images corresponding to each of the assumed extension factors by performing motion compensations on the ROI with each of the extended MVFs, and calculating the edge gradient standard deviations of each of the extended images;
fitting a standard deviation variation curve based on each of the assumed extension factors and the edge gradient standard deviations corresponding to each of the assumed extension factors; and
determining an extension factor which corresponds to the maximum edge gradient standard deviation along the standard deviation variation curve as the extension factor.

5. The method of claim 1 , wherein, calculating the motion level factor of the ROI comprises:
calculating the edge gradient standard deviation of the ROI; and
determining the motion level factor based on the edge gradient standard deviation and a preset threshold.

6. The method of claim 5 , wherein, the edge gradient standard deviation of the ROI is calculated based on the absolute value of the edge gradient, and a formula for calculating the absolute value of the edge gradient of the ROI is as follows:

E
⁡

(

a
,
b

)

=



f
⁡

(

a
,
b

)

-

f
⁡

(

a
+
1

,
b

)



Δ
⁢

⁢
x

+



f
⁡

(

a
,
b

)

-

f
⁡

(

a
,

b
+
1

)



Δ
⁢

⁢
y

wherein, E(a,b) is the absolute value of the edge gradient on the pixel (a,b);
Δx is the pixel sampling interval on the X-axis, and Δy is the pixel sampling interval on the Y-axis;
a is the X-axis index of the pixel (a,b), and b is the Y-axis index of the pixel (a,b); and
f(a,b) is the value of the pixel (a,b) on the image of the ROI.

7. The method of claim 4 , wherein, a formula for determining the motion level factor based on the edge gradient standard deviation and the preset threshold is:

φ
=

{

0

StdE
≥

φ
Th

φ

Assignees

• Shenyang Neusoft Medical Sys

Inventors

• Lou Shanshan
• Zheng Han

Classifications

• G06T12/30

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

• G06T7/246

  using feature-based methods, e.g. the tracking of corners or segments · CPC title

• G06T2211/412

  Dynamic · CPC title

• A61B6/5264

  due to motion · CPC title

• A61B6/481

  involving the use of contrast agents · CPC title