# US 10,499,870 B2 · Methods and apparatuses for quantifying vascular fluid motions from DSA

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- Number: US 10,499,870 B2
- Document ID: US10499870B2
- Status: Granted
- Kind: B2
- Application number: 15/600,493
- Filed: 2017-05-19
- Published: 2018-11-22
- Granted: 2019-12-10
- Earliest claimed: 2017-05-19
- Assignees: The Chinese University of Hong Kong
- Applicants: The Chinese University of Hong Kong
- Inventors: Defeng Wang; Ka Long Ko
- Web page: https://patentgrep.com/patent/US10499870B2

## CPC

- A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data}
- A61B 6/481: {involving the use of contrast agents}
- A61B 6/482: {involving multiple energy imaging}
- A61B 6/487: {involving fluoroscopy}
- A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography}
- A61B 6/507: {for determination of haemodynamic parameters, e.g. perfusion CT}
- A61B 6/5235: {combining images from the same or different ionising radiation imaging techniques, e.g. PET and CT}
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/254: involving subtraction of images
- G06T 7/269: using gradient-based methods
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G06T 2207/10016: Video; Image sequence
- G06T 2207/10064: Fluorescence image
- G06T 2207/20016: Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
- G06T 2207/20224: Image subtraction
- G06T 2207/30101: Blood vessel; Artery; Vein; Vascular
- G06T 2207/30104: Vascular flow; Blood flow; Perfusion
- G06T 2211/404: Angiography
- G06T 2211/424: Iterative
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Disclosed is a method and an apparatus for quantifying vascular fluid motions from digital subtraction angiography (DSA) images, comprising: calculating an optical flow field between two temporal consecutive DSA images; and estimating a displacement of blood or tissue between the two temporal consecutive DSA images from the calculated optical flow field, wherein the optical flow field is calculated by solving a minimization problem of a CLG energy function, wherein the CLG energy function combines the temporally extended variant of Horn-Schunck approach with Lucas-Kanade approach non-linearly in spatiotemporal approach. The present disclosure provides a new optical flow solution significantly reducing the computation cost with a high robustness for quantifying vascular fluid motions from DSA.

## Claims

### Claim 1

A method for quantifying vascular fluid motions from digital subtraction angiography (DSA) images, comprising:
calculating an optical flow field between two temporal consecutive DSA images; and
estimating a displacement of blood or tissue between the two temporal consecutive DSA images from the calculated optical flow field,
wherein the optical flow field is calculated by solving a minimization problem of a combined local global (CLG) energy function, wherein the CLG energy function combines the temporally extended variant of Horn-Schunck approach with Lucas-Kanade approach non-linearly in spatiotemporal approach,
wherein the CLG energy function is established by:
establishing an energy function by Horn-Schunck approach, which includes an energy term and a regularization term, wherein the energy term is determined by an estimated optical flow increment field and a gradient of an intensity field, and the intensity field is extended from a spatial field to a spatial-temporal field according to the two temporal consecutive DSA images; and
transforming the gradient of the intensity field in a spatial-temporal vector space to a localized spatial-temporal derivative smoothing tensor by a convolution kernel, wherein the localized spatial-temporal derivative smoothing tensor is defined as Jρ(∇3I)=Kρ*(∇3I∇3IT), wherein ρ denotes for a localized constant flow assumed in Lucas-Kanade approach, Kρ denotes for a Gaussian kernel with standard deviation ρ, I denotes for the intensity field of DSA images, ∇3I=[Ix, Iy, It]T, denoting for the gradient of the intensity field of DSA images, wherein
I
x
=
∂
I
∂
x
,
I
y
=
∂
I
∂
y
⁢
⁢
and
⁢
⁢
I
t
=
∂
I
∂
t
,
x and y denote for a two-dimensional spatial position, t denotes for a time.

### Claim 2 (depends on 1)

The method according to claim 1, wherein calculating the optical flow field comprising:
setting a plurality of image pyramid levels from coarse to fine;
at each of the plurality of image pyramid levels, calculating an optical flow increment between the two temporal consecutive DSA images which minimizes the CLG energy function; and
calculating an interpolated sum of the optical flow increment fields calculated over all the plurality of image pyramid levels so as to obtain the optical flow field.

### Claim 3 (depends on 2)

The method according to claim 2, wherein the CLG energy function is further established by:
wrapping the intensity field by the estimated optical flow increment field.

### Claim 4 (depends on 3)

The method according to claim 3, further comprising:
assuming a localized flow to be constant and representing the localized flow as a Gaussian kernel convolution in the CLG energy function.

### Claim 5 (depends on 3)

The method according to claim 3, wherein the regularization term is determined by a regularization parameter and an optical flow field wrapped by the estimated optical flow increment field according to the two temporal consecutive DSA images.

### Claim 6 (depends on 5)

The method according to claim 5, wherein at each level of image pyramid, the localized spatial-temporal derivative smoothing tensor is further defined as a term determined by ∇3I(p+δw(m)),
wherein p denotes for a spatial-temporal position including the two-dimensional spatial position and the time point, δw(m) denotes for a estimated optical flow increment field at an image pyramid level m, I(p+δw(m)) denotes for the second DSA image wrapped by δw(m).

### Claim 7 (depends on 6)

The method according to claim 6, wherein at each level of image pyramid, the regularization term is defined as α(∥∇(w(m)+δw(m))∥2,
wherein α denotes for the regularization parameter, w(m) denotes for a optical flow field at the image pyramid level m, w(m)+δw(m) denotes for the optical flow field at the image pyramid level m wrapped by δw(m).

### Claim 8 (depends on 7)

The method according to claim 7, wherein the minimization problem of the CLG energy function is solved by successive over-relaxation approach.

### Claim 9 (depends on 8)

The method according to claim 8, wherein the successive over-relaxation approach comprising: at each image pyramid level,
setting an initial optical flow field;
iteratively renewing the optical flow field, wherein in each iterative step, a new velocity at pixel i within the image domain is determined by a current velocity at the pixel i, relaxation parameter ω, a current velocity at neighbor pixels of the pixel i, the localized spatial-temporal derivative smoothing tensor, and the regularization term α; and
stopping the iteration and obtaining a final optical flow field at current pyramid level when the criterion for stopping iteration is met.

### Claim 10

An apparatus for quantifying vascular fluid motions from digital subtraction angiography (DSA) images, comprising:
at least one processor; and
a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:
calculating an optical flow field between two temporal consecutive DSA images; and
estimating a displacement of blood or tissue between the two temporal consecutive DSA images from the calculated optical flow field,
wherein the optical flow field is calculated by solving a minimization problem of a combined local global (CLG) energy function, wherein the CLG energy function combines the temporally extended variant of Horn-Schunck approach with Lucas-Kanade approach non-linearly in spatiotemporal approach,
wherein the CLG energy function is established by:
establishing an energy function by Horn-Schunck approach, which includes an energy term and a regularization term, wherein the energy term is determined by an estimated optical flow increment field and a gradient of an intensity field, and the intensity field is extended from a spatial field to a spatial-temporal field according to the two temporal consecutive DSA images; and
transforming the gradient of the intensity field in a spatial-temporal vector space to a localized spatial-temporal derivative smoothing tensor by a convolution kernel, wherein the localized spatial-temporal derivative smoothing tensor is defined as Jρ(∇3I)=Kρ*(∇3I∇3IT), wherein ρ denotes for a localized constant flow assumed in Lucas-Kanade approach, Kρ denotes for a Gaussian kernel with standard deviation ρ, I denotes for the intensity field of DSA images, ∇3I=[Ix, Iy, It]T, denoting for the gradient of the intensity field of DSA images, wherein
I
x
=
∂
I
∂
x
,
I
y
=
∂
I
∂
y
⁢
⁢
and
⁢
⁢
I
t
=
∂
I
∂
t
,
x and y denote for a two-dimensional spatial position, t denotes for a time.

### Claim 11 (depends on 10)

The apparatus according to claim 10, wherein the operation of calculating the optical flow field comprises:
setting a plurality of image pyramid levels from coarse to fine;
calculating an optical flow increment between the two temporal consecutive DSA images which minimizes the CLG energy function at each of the plurality of image pyramid levels; and
calculating an interpolated sum of the optical flow increment fields calculated over all the plurality of image pyramid levels so as to obtain the optical flow field.

### Claim 12 (depends on 11)

The apparatus according to claim 11, wherein the CLG energy function is further established by wrapping the intensity field by the estimated optical flow increment field.

### Claim 13 (depends on 12)

The apparatus according to claim 12, wherein the operations further comprise assuming a localized flow to be constant and representing a localized flow as a Gaussian kernel convolution in the CLG energy function.

### Claim 14 (depends on 12)

The apparatus according to claim 12, wherein the regularization term is determined by a regularization parameter and an optical flow field wrapped by the estimated optical flow increment field according to the two temporal consecutive DSA images.

### Claim 15 (depends on 14)

The apparatus according to claim 14, wherein at each level of image pyramid, the localized spatial-temporal derivative smoothing tensor is further defined as a term determined by ∇3I(p+δw(m)),
wherein p denotes for a spatial-temporal position including the two-dimensional spatial position and the time point, δw(m) denotes for a estimated optical flow increment field at an image pyramid level m, I(p+δw(m)) denotes for the second DSA image wrapped by δw(m).

### Claim 16 (depends on 15)

The apparatus according to claim 15, wherein at each level of image pyramid, the regularization term is defined as α(∥∇(w(m)+δw(m))∥2,
wherein α denotes for the regularization parameter, w(m) denotes for a optical flow field at the image pyramid level m, w(m)+δw(m) denotes for the optical flow field at the image pyramid level m wrapped by δw(m).

### Claim 17 (depends on 16)

The apparatus according to claim 16, wherein:
the minimization problem of the CLG energy function is solved by successive over-relaxation approach.

### Claim 18 (depends on 17)

The apparatus according to claim 17, wherein the successive over-relaxation approach comprises: at each image pyramid level,
setting an initial optical flow field;
iteratively renewing the optical flow field, wherein in each iterative step, a new velocity at pixel i within the image domain is determined by a current velocity at the pixel i, relaxation parameter ω, a current velocity at neighbor pixels of the pixel i, the localized spatial-temporal derivative smoothing tensor, and the regularization term α; and
stopping the iteration and obtaining a final optical flow field at current pyramid level when the criterion for stopping iteration is met.