| 摘要 |
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. |
| 主权项 |
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 {αt, 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 ŝiα1(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 ŝiα1(n) via Fourier frequency-domain filtering according to said cardiac motion signal cycle Nc thereby obtaining a cardiac motion curve ĉiα(n); (4) obtaining a residual motion curve ŝ1α2(n)=ŝ1α1 (n)−ĉiα(n) with no translational motion signal or cardiac motion signal according to said synthetic motion curve ŝiα1(n) and said cardiac motion curve ĉiα(n), processing said residual motion curve ŝiα(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 Nαr with respect to a current simulated translation curve using a fitting curve {circumflex over (r)}′iα(n) closest to said residual motion curve ŝiα2(n) as an optimum criteria; (5) detecting whether an amplitude of a curve ĥ′iα(n)=ŝiα2(n)−{circumflex over (r)}′iα(n) obtained according to said residual motion curve ŝiα2(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 ŝiα2(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 ĥiα(n) and an optimum high-frequency motion signal cycle {circumflex over (N)}αh 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 ŝiα(n)=dα(n)+{circumflex over (r)}iα(n)+ĉiα(n)+ĥiα(n) according to said simulated translation curve dα(n), said respiratory motion curve {circumflex over (r)}iα(n), said cardiac motion curve ĉiα(n), and said high-frequency motion curve ĥiα(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 ŝiα(n) as an optimum criteria. |
