Fast voxel line update for time-space image reconstruction

Recent applications of model-based iterative reconstruction(MBIR) algorithm to time-space Computed Tomography (CT) have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent(ICD) has been found to have relatively low overall computational requirements due to its fast convergence. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. This disadvantage is especially prominent in time-space reconstruction because of the large volume of data. This thesis presents a new data structure, called VL-Buffer , for time-space reconstruction that significantly improves the cache locality while retaining good parallel performance. Experimental results show an average speedup of 40% using VL-Buffer

Advanced Statistical Modeling for Model-Based Iterative Reconstruction for Single-Energy and Dual-Energy X-Ray CT

Model-based iterative reconstruction (MBIR) has been increasingly broadly applied as an improvement over traditional, analytical image reconstruction methods in X-ray CT, primarily due to its significant advantage in drastic dose reduction without diagnostic loss. Early success of the method in conventional CT has encouraged the extension to a wide range of applications that includes more advanced imaging modalities, such as dual-energy X-ray CT, and more challenging imaging conditions, such as low-dose and sparse-sampling scans, each requiring refined statistical models including the data model and the prior model. In this dissertation, we developed an MBIR algorithm for dual-energy CT that included a joint data-likelihood model to account for correlated data noise. Moreover, we developed a Gaussian-Mixture Markov random filed (GM-MRF) image model that can be used as a very expressive prior model in MBIR for X-ray CT reconstruction. The GM-MRF model is formed by merging individual patch-based Gaussian-mixture models and therefore leads to an expressive MRF model with easily estimated parameters. Experimental results with phantom and clinical datasets have demonstrated the improvement in image quality due to the advanced statistical modeling

Plug-and-play priors for model based reconstruction

Abstract-Model-based reconstruction is a powerful framework for solving a variety of inverse problems in imaging. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an identity operator. Similarly, great progress has been made in improving model-based inversion when the forward model corresponds to complex physical measurements in applications such as X-ray CT, electron-microscopy, MRI, and ultrasound, to name just a few. However, combining state-of-theart denoising algorithms (i.e., prior models) with state-of-the-art inversion methods (i.e., forward models) has been a challenge for many reasons. In this paper, we propose a flexible framework that allows state-of-the-art forward models of imaging systems to be matched with state-of-the-art priors or denoising models. This framework, which we term as Plug-and-Play priors, has the advantage that it dramatically simplifies software integration, and moreover, it allows state-of-the-art denoising methods that have no known formulation as an optimization problem to be used. We demonstrate with some simple examples how Plug-and-Play priors can be used to mix and match a wide variety of existing denoising models with a tomographic forward model, thus greatly expanding the range of possible problem solutions