An LMS-like algorithm is applied for estimating the time-varying parameter theta-n in the linear model y(n) = phi-n-tau-theta-n + upsilon-n, which is general in the sense that none of the probabilistic properties such...An LMS-like algorithm is applied for estimating the time-varying parameter theta-n in the linear model y(n) = phi-n-tau-theta-n + upsilon-n, which is general in the sense that none of the probabilistic properties such as stationarity, Markov property, independence and ergodicity is imposed on any of the processes {y(n)}, {phi-n}, {theta-n} and {upsilon-n}. It is shown that the alpha-th moment of the estimation error is of order of the alpha-th moment of the observation noise and the parameter variation w(n) change in equivalence theta-n - theta-n-1.展开更多
A high contrast to noise ratio(CNR)is always desirable for contrast-enhanced computed tomography angiography(CTA).To ensure a high CNR of the vascular images in CTA and potentially reduce the radiation exposure and co...A high contrast to noise ratio(CNR)is always desirable for contrast-enhanced computed tomography angiography(CTA).To ensure a high CNR of the vascular images in CTA and potentially reduce the radiation exposure and contrast usage,an adaptive bolus chasing method is proposed and evaluated compared to the existing constant-speed method.The proposed method is based on a local time and space parameter varying model of the contrast bolus.Optimal scan time for the next segment of the vasculature is estimated and predicted in real time and guides the computed tomography(CT)scanner table movement that guarantees that each segment of the vasculature is scanned with the maximum possible enhancement.Simulations and experimental results show that the proposed bolus chasing method outperforms the conventional constant-speed method substantially.展开更多
文摘An LMS-like algorithm is applied for estimating the time-varying parameter theta-n in the linear model y(n) = phi-n-tau-theta-n + upsilon-n, which is general in the sense that none of the probabilistic properties such as stationarity, Markov property, independence and ergodicity is imposed on any of the processes {y(n)}, {phi-n}, {theta-n} and {upsilon-n}. It is shown that the alpha-th moment of the estimation error is of order of the alpha-th moment of the observation noise and the parameter variation w(n) change in equivalence theta-n - theta-n-1.
基金The work was supported partially by NSF ECS-0555394 and NIH/NIBIB EB004287.
文摘A high contrast to noise ratio(CNR)is always desirable for contrast-enhanced computed tomography angiography(CTA).To ensure a high CNR of the vascular images in CTA and potentially reduce the radiation exposure and contrast usage,an adaptive bolus chasing method is proposed and evaluated compared to the existing constant-speed method.The proposed method is based on a local time and space parameter varying model of the contrast bolus.Optimal scan time for the next segment of the vasculature is estimated and predicted in real time and guides the computed tomography(CT)scanner table movement that guarantees that each segment of the vasculature is scanned with the maximum possible enhancement.Simulations and experimental results show that the proposed bolus chasing method outperforms the conventional constant-speed method substantially.
