METHOD FOR SEPARATING AND ESTIMATING MULTIPLE MOTION PARAMETERS IN X-RAY ANGIOGRAM IMAGE

• US 20150317793A1
• Filed: 04/29/2015
• Published: 11/05/2015
• Est. Priority Date: 12/31/2013
• Status: Active Grant

• Current Assignee: Huazhong University of Science & Technology
• Original Assignee: Huazhong University of Science & Technology
• Inventors: ZHANG, Tianxu, WEI, Yaxun, HUANG, Yining, HUANG, Zhenghua, HAO, Longwei
• Granted Patent: US 9,286,676 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/20224 Image subtraction
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 2211/404 Angiography
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/262 using transform domain methods, e.g. Fourier domain methods

Abstract

A method for separating and estimating multiple motion parameters in an X-ray angiogram image. The method includes: determining a cardiac motion signal cycle and a variation frame sequence of translational motion according to an angiogram image sequence, tracing structure feature points of vessels in the angiogram image sequence whereby obtaining a motion sequence, processing the motion sequence via multivariable optimization and Fourier frequency-domain filtering, separating an optimum translational motion curve, a cardiac motion curve, a respiratory motion curve and a high-frequency motion curve according to the variation frame sequence of translational motion, a cycle of the cardiac motion signal, a range of a respiratory motion signal cycle, and a range of a high-frequency motion signal cycle.

Claims

1. A method for separating and estimating multiple motion parameters in an X-ray angiogram image, the method comprising:

(1) tracing structure feature points of vessels in a X-ray angiogram image sequence whereby obtaining tracing curves of said feature points s_i(n), i=1, . . . , I, n=1, . . . , N;

wherein I represents the number of the feature points, and N represents the number of image frames in said X-ray angiogram image sequence;

(2) obtaining a simulated translation curve d_α

(n) according to a variation frame sequence {N_t, t=1, . . . , T} and a slope angle sequence {α

_t, t=1, . . . , T−

1} of translational motion, where T represents the number of variations in motion directions;

(3) determining a cardiac motion signal cycle N_c according to said X-ray angiogram image sequence, obtaining a multi-motion synthetic motion curve ŝ

_iα

¹(n)=s_i(n)−

d_α

(n) without translational motion according to said tracing curve s_i(n) and said simulated translation curve d_α

(n), and processing said synthetic motion curve ŝ

_iα

¹(n) via Fourier frequency-domain filtering according to said cardiac motion signal cycle N_c thereby obtaining a cardiac motion curve ĉ

_iα

(n);

(4) obtaining a residual motion curve ŝ

_1α

²(n)=ŝ

_1α

¹ (n)−

ĉ

_iα

(n) with no translational motion signal or cardiac motion signal according to said synthetic motion curve ŝ

_iα

¹(n) and said cardiac motion curve ĉ

_iα

(n), processing said residual motion curve ŝ

_iα

(n) via Fourier frequency-domain filtering according to each respiratory motion signal cycle in a cycle range of [3N_c, 10N_c], thereby obtaining a corresponding respiratory motion curve, obtaining an optimum respiratory motion curve {circumflex over (r)}_iα

(n) and an optimum respiratory motion signal cycle N<sub>α

r</sub> with respect to a current simulated translation curve using a fitting curve {circumflex over (r)}′

_iα

(n) closest to said residual motion curve ŝ

_iα

²(n) as an optimum criteria;

(5) detecting whether an amplitude of a curve ĥ

′

_iα

(n)=ŝ

_iα

²(n)−

{circumflex over (r)}′

_iα

(n) obtained according to said residual motion curve ŝ

_iα

²(n) and said fitting curve {circumflex over (r)}′

_iα

(n) is less than three pixels, determining there is no high-frequency component if yes, otherwise processing said residual motion curve ŝ

_iα

²(n) via Fourier frequency-domain filtering according to each high-frequency motion signal cycle in a cycle range of [1/7N_c, 5/7N_c], thereby obtaining a corresponding high-frequency motion curve, obtaining an optimum high-frequency motion curve ĥ

_iα

(n) and an optimum high-frequency motion signal cycle {circumflex over (N)}<sub>α

h</sub> with respect to a current simulated translation curve using a fitting curve ĥ

′

_iα

(n) closest to said high-frequency motion curve as an optimum criteria;

(6) obtaining a synthetic motion estimation curve ŝ

_iα

(n)=d_α

(n)+{circumflex over (r)}_iα

(n)+ĉ

_iα

(n)+ĥ

_iα

(n) according to said simulated translation curve d_α

(n), said respiratory motion curve {circumflex over (r)}_iα

(n), said cardiac motion curve ĉ

_iα

(n), and said high-frequency motion curve ĥ

_iα

(n), and obtaining an optimum translational motion curve, a cardiac motion curve, a respiratory motion curve, and a high-frequency motion curve using a tracing curve s_i(n) closest to said synthetic motion estimation curve ŝ

_iα

(n) as an optimum criteria.