Improving statistical image reconstruction for cardiac X-ray computed tomography

X-ray computed tomography (CT) is one of the most widely used imaging modalities for medical diagnosis. Recent advancements in 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, statistical methods face several challenges that should be addressed before they can replace the FBP method universally. 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.;Rigorous regularization design methods in Fourier domain were proposed to achieve more isotropic and uniform spatial resolution or noise properties. The design framework is general so that users can control the spatial resolution and the noise characteristics of the estimator. Experimental results show the proposed method can achieve its goal with modest computation cost. In addition, a regularization design method based on the hypothetical geometry concept was introduced to improve resolution or noise uniformity. Proposed designs using the new concept effectively improved the spatial resolution or noise uniformity in the reconstructed image. The hypothetical geometry idea is general enough to be applied to other scan geometries.;We investigated various methods to reduce image artifacts in reconstructed images caused by the short-scan geometry. Statistical weighting modification, based on how much each detector element affects insufficiently sampled region, was proposed to reduce the artifacts without degrading the temporal resolution within the region-of-interest (ROI). We also proposed a new metric to compare the temporal resolution of the proposed method to that of the short-scan reconstruction. Another approach using an additional regularization term, that exploits information from the prior image, was investigated. Both methods effectively removed short-scan artifacts in the reconstructed image. Experimental results revealed advantages and disadvantages of each approach and their combination.;We accelerated the family of ordered-subsets algorithms by introducing a double surrogate so that faster convergence speed can be achieved. Furthermore, we present a variable splitting based algorithm for motion-compensated image reconstruction (MCIR) problem that provides faster convergence compared to the conjugate gradient (CG) method. Experimental results show that our proposed methods can achieve significant acceleration. The method was also extended to joint estimation of the motion parameters and the reconstructed image. A sinogram-based motion estimation method that does not require any additional measurements other than the short-scan amount of data was introduced to provide decent initial estimates for the joint estimation.;Overall, we proposed various methods that have potential to overcome the major challenges of statistical image reconstruction methods. They were evaluated using simulation and real patient data, and showed promising results. Future work will address more detailed investigation and improvements for these methods. Some of these methods can be combined to generate more complete solutions for CT imaging.