Yi Tian

Zhejiang University, Hangzhou, Zhejiang Sheng, China

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Publications (5)3.53 Total impact

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    ABSTRACT: In Positron Emission Tomography (PET), an optimal estimate of the radioactivity concentration is obtained from the measured emission data under certain criteria. So far, all the well-known statistical reconstruction algorithms require exactly known system probability matrix a priori, and the quality of such system model largely determines the quality of the reconstructed images. In this paper, we propose an algorithm for PET image reconstruction for the real world case where the PET system model is subject to uncertainties. The method counts PET reconstruction as a regularization problem and the image estimation is achieved by means of an uncertainty weighted least squares framework. The performance of our work is evaluated with the Shepp-Logan simulated and real phantom data, which demonstrates significant improvements in image quality over the least squares reconstruction efforts.
    PLoS ONE 01/2012; 7(3):e32224. · 3.53 Impact Factor
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    ABSTRACT: For quantitative image reconstruction in positron emission tomography attenuation correction is necessary. A common technique used for attenuation correction is based on patient-specific attenuation maps reconstructed from transmission data acquired with external sources. However, the transmission process increases measurement time, costs and radiation exposure, and generates misregistration errors due to patient motion. In this paper, we propose a framework for simultaneous reconstruction of activity distribution together with the attenuation map from emission data alone. The estimation process is accomplished by solving a nonlinear stationary state space model with a divided difference filter. A Zubal digital thorax phantom data is used to demonstrate the benefits of such a reconstruction.
    11/2006: pages 301-308;
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    ABSTRACT: The importance of accurate attenuation correction in single photon emission computed tomography (SPECT) has been widely recognized. In this paper, we propose a novel scheme of simultaneous reconstruction of the tissue attenuation map and the radioactivity distribution from SPECT emission sinograms, which is obviously beneficial when the transmission data is missing for cost or efficiency reasons. Our strategy combines the SPECT image formation and data measurement models, whereas the attenuation parameters are treated as random variables with known prior statistics. After converting the models to state space representation, the extended Kalman filtering procedures are adopted to linearize the equations and to provide the joint estimates in an approximate optimal sense. Experiments have been performed on synthetic data and real scanning data to illustrate abilities and benefits of the method.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2006; 9(Pt 1):397-404.
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    ABSTRACT: Statistical iterative reconstruction algorithms have shown improved image quality over conventional nonstatistical methods in PET by using accurate system response models and measurement noise models. Strictly speaking, however, PET measurements, pre-corrected for accidental coincidences, are neither Poisson nor Gaussian distributed and thus do not meet basic assumptions of these algorithms. In addition, the difficulty in determining the proper system response model also greatly affects the quality of the reconstructed images. In this paper, we explore the usage of state space principles for the estimation of activity map in tomographic PET imaging. The proposed strategy formulates the organ activity distribution through tracer kinetics models, and the photon-counting measurements through observation equations, thus makes it possible to unify the dynamic reconstruction problem and static reconstruction problem into a general framework. Further, it coherently treats the uncertainties of the statistical model of the imaging system and the noisy nature of measurement data. Since H(infinity) filter seeks minimummaximum-error estimates without any assumptions on the system and data noise statistics, it is particular suited for PET image reconstruction where the statistical properties of measurement data and the system model are very complicated. The performance of the proposed framework is evaluated using Shepp-Logan simulated phantom data and real phantom data with favorable results.
    Information processing in medical imaging: proceedings of the ... conference 02/2005; 19:197-209.
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    ABSTRACT: In this paper, we explore the usage of state space principles for the estimation of activity map in tomographic PET imaging. The proposed strategy formulates the dynamic changes of the organ activity distribution through state space evolution equations and the photon-counting measurements through observation equations, thus makes it possible to unify the dynamic reconstruction problem and static reconstruction problem into a general framework. Further, it coherently treats the uncertainties of the statistical model of the imaging system and the noisy nature of measurement data. The state-space reconstruction problem is solved by both the popular but suboptimal Kalman filter (KF) and the robust H ∞ estimator. Since the H ∞ filter seeks the minimum-maximum-error estimates without any assumptions on the system and data noise statistics, it is particular suited for PET imaging where the measurement data is known to be Poisson distributed. The proposed framework is evaluated using Shepp-Logan simulated phantom data and compared to standard methods with favorable results.
    Medical Imaging and Augmented Reality: Second International Workshop, MIAR2004, Beijing, China, August 19-20, 2004. Proceedings; 01/2004