Improving statistical image reconstruction for cardiac X-ray computed tomography
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Recent advancements in computed tomography (CT) scanner technology have led to increased use of CT in various applications. Unfortunately, these technological advances in CT imaging pose new challenges such as increased X-ray radiation dose and complexity of image reconstruction. Statistical image reconstruction methods use realistic models that incorporate the physics of the measurements and the statistical properties of the measurement noise, and they have potential to provide better image quality and dose reduction compared to the conventional filtered back-projection (FBP) method. However, the statistical methods are facing several challenges to be considered as a practical replacement of FBP method. Such challenges include substantial computation time, anisotropic and nonuniform spatial resolution and noise properties, and other artifacts.
In this thesis, we develop various methods to overcome these challenges of statistical image reconstruction methods. We mainly focus on reducing image artifacts in reconstructed images caused by the short-scan geometry. Rigorous regularization design methods in Fourier domain were proposed to achieve more isotropic and uniform spatial resolution and noise properties. The design framework is general so that users can control the spatial resolution and the noise characteristics of the estimator. In addition, a regularization design method based on the hypothetical geometry concept was introduced to improve resolution and noise uniformity. We further investigated statistical weighting modification to reduce the artifacts without degrading the temporal resolution within the region-of-interest. Finally, an additional regularization term that exploits information from the prior image was investigated. Experiment results revealed advantages and disadvantages of each approach and its combination.
This thesis also addresses acceleration of statistical image reconstruction methods with and without motion-compensation. A double surrogate idea for the family of ordered-subsets algorithms and variable splitting methods were proposed to achieve these goals, respectively.