METHOD FOR SEPARATING AND ESTIMATING MULTIPLE MOTION PARAMETERS IN X-RAY ANGIOGRAM IMAGE
First Claim
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 si(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 dα
(n) according to a variation frame sequence {Nt, 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 Nc according to said X-ray angiogram image sequence, obtaining a multi-motion synthetic motion curve ŝ
iα
1(n)=si(n)−
dα
(n) without translational motion according to said tracing curve si(n) and said simulated translation curve dα
(n), and processing said synthetic motion curve ŝ
iα
1(n) via Fourier frequency-domain filtering according to said cardiac motion signal cycle Nc 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 [3Nc, 10Nc], 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 Nα
r 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 [1/7Nc, 5/7Nc], 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)}α
h 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)=dα
(n)+{circumflex over (r)}iα
(n)+ĉ
iα
(n)+ĥ
iα
(n) according to said simulated translation curve dα
(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 si(n) closest to said synthetic motion estimation curve ŝ
iα
(n) as an optimum criteria.
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Accused Products
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.
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Citations
1 Claim
-
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 si(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 dα
(n) according to a variation frame sequence {Nt, 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 Nc according to said X-ray angiogram image sequence, obtaining a multi-motion synthetic motion curve ŝ
iα
1(n)=si(n)−
dα
(n) without translational motion according to said tracing curve si(n) and said simulated translation curve dα
(n), and processing said synthetic motion curve ŝ
iα
1(n) via Fourier frequency-domain filtering according to said cardiac motion signal cycle Nc 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 [3Nc, 10Nc], 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 Nα
r 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 [1/7Nc, 5/7Nc], 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)}α
h 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)=dα
(n)+{circumflex over (r)}iα
(n)+ĉ
iα
(n)+ĥ
iα
(n) according to said simulated translation curve dα
(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 si(n) closest to said synthetic motion estimation curve ŝ
iα
(n) as an optimum criteria.
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Specification