Fast Variance Prediction for Iteratively Reconstructed CT with Applications to Tube Current Modulation.

X-ray computed tomography (CT) is an important, widely-used medical imaging modality. A primary concern with the increasing use of CT is the ionizing radiation dose incurred by the patient. Statistical reconstruction methods are able to improve noise and resolution in CT images compared to traditional filter backprojection (FBP) based reconstruction methods, which allows for a reduced radiation dose. Compared to FBP-based methods, statistical reconstruction requires greater computational time and the statistical properties of resulting images are more difficult to analyze. Statistical reconstruction has parameters that must be correctly chosen to produce high-quality images. The variance of the reconstructed image has been used to choose these parameters, but this has previously been very time-consuming to compute. In this work, we use approximations to the local frequency response (LFR) of CT projection and backprojection to predict the variance of statistically reconstructed CT images. Compared to the empirical variance derived from multiple simulated reconstruction realizations, our method is as accurate as the currently available methods of variance prediction while being computable for thousands of voxels per second, faster than these previous methods by a factor of over ten thousand. We also compare our method to empirical variance maps produced from an ensemble of reconstructions from real sinogram data. The LFR can also be used to predict the power spectrum of the noise and the local frequency response of the reconstruction. Tube current modulation (TCM), the redistribution of X-ray dose in CT between different views of a patient, has been demonstrated to reduce dose when the modulation is well-designed. TCM methods currently in use were designed assuming FBP-based image reconstruction. We use our LFR approximation to derive fast methods for predicting the SNR of linear observers of a statistically reconstructed CT image. Using these fast observability and variance prediction methods, we derive TCM methods specific to statistical reconstruction that, in theory, potentially reduce radiation dose by 20% compared to FBP-specific TCM methods.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111463/1/smschm_1.pd

Development and Implementation of Fully 3D Statistical Image Reconstruction Algorithms for Helical CT and Half-Ring PET Insert System

X-ray computed tomography: CT) and positron emission tomography: PET) have become widely used imaging modalities for screening, diagnosis, and image-guided treatment planning. Along with the increased clinical use are increased demands for high image quality with reduced ionizing radiation dose to the patient. Despite their significantly high computational cost, statistical iterative reconstruction algorithms are known to reconstruct high-quality images from noisy tomographic datasets. The overall goal of this work is to design statistical reconstruction software for clinical x-ray CT scanners, and for a novel PET system that utilizes high-resolution detectors within the field of view of a whole-body PET scanner. The complex choices involved in the development and implementation of image reconstruction algorithms are fundamentally linked to the ways in which the data is acquired, and they require detailed knowledge of the various sources of signal degradation. Both of the imaging modalities investigated in this work have their own set of challenges. However, by utilizing an underlying statistical model for the measured data, we are able to use a common framework for this class of tomographic problems. We first present the details of a new fully 3D regularized statistical reconstruction algorithm for multislice helical CT. To reduce the computation time, the algorithm was carefully parallelized by identifying and taking advantage of the specific symmetry found in helical CT. Some basic image quality measures were evaluated using measured phantom and clinical datasets, and they indicate that our algorithm achieves comparable or superior performance over the fast analytical methods considered in this work. Next, we present our fully 3D reconstruction efforts for a high-resolution half-ring PET insert. We found that this unusual geometry requires extensive redevelopment of existing reconstruction methods in PET. We redesigned the major components of the data modeling process and incorporated them into our reconstruction algorithms. The algorithms were tested using simulated Monte Carlo data and phantom data acquired by a PET insert prototype system. Overall, we have developed new, computationally efficient methods to perform fully 3D statistical reconstructions on clinically-sized datasets

Geometrical Calibration and Filter Optimization for Cone-Beam Computed Tomography

This thesis will discuss the requirements of a software library for tomography and will derive a framework which can be used to realize various applications in cone-beam computed tomography (CBCT). The presented framework is self-contained and is realized using the MATLAB environment in combination with native low-level technologies (C/C++ and CUDA) to improve its computational performance, while providing accessibility and extendability through to use of a scripting language environment. On top of this framework, the realization of Katsevich’s algorithm on multicore hardware will be explained and the resulting implementation will be compared to the Feldkamp, Davis and Kress (FDK) algorithm. It will also be shown that this helical reconstruction method has the potential to reduce the measurement uncertainty. However, misalignment artifacts appear more severe in the helical reconstructions from real data than in the circular ones. Especially for helical CBCT (H-CBCT), this fact suggests that a precise calibration of the computed tomography (CT) system is inevitable. As a consequence, a self-calibration method will be designed that is able to estimate the misalignment parameters from the cone-beam projection data without the need of any additional measurements. The presented method employs a multi-resolution 2D-3D registration technique and a novel volume update scheme in combination with a stochastic reprojection strategy to achieve a reasonable runtime performance. The presented results will show that this method reaches sub-voxel accuracy and can compete with current state-of-the-art online- and offline-calibration approaches. Additionally, for the construction of filters in the area of limited-angle tomography a general scheme which uses the Approximate Inverse (AI) to compute an optimized set of 2D angle-dependent projection filters will be derived. Optimal sets of filters are then precomputed for two angular range setups and will be reused to perform various evaluations on multiple datasets with a filtered backprojection (FBP)-type method. This approach will be compared to the standard FDK algorithm and to the simultaneous iterative reconstruction technique (SIRT). The results of the study show that the introduced filter optimization produces results comparable to those of SIRT with respect to the reduction of reconstruction artifacts, whereby its runtime is comparable to that of the FDK algorithm

Iterative Reconstruction of Cone-Beam Micro-CT Data

The use of x-ray computed tomography (CT) scanners has become widespread in both clinical and preclinical contexts. CT scanners can be used to noninvasively test for anatom- ical anomalies as well as to diagnose and monitor disease progression. However, the data acquired by a CT scanner must be reconstructed prior to use and interpretation. A recon- struction algorithm processes the data and outputs a three dimensional image representing the x-ray attenuation properties of the scanned object. The algorithms in most widespread use today are based on filtered backprojection (FBP) methods. These algorithms are rela- tively fast and work well on high quality data, but cannot easily handle data with missing projections or considerable amounts of noise. On the other hand, iterative reconstruction algorithms may offer benefits in such cases, but the computational burden associated with iterative reconstructions is prohibitive. In this work, we address this computational burden and present methods that make iterative reconstruction of high-resolution CT data possible in a reasonable amount of time. Our proposed techniques include parallelization, ordered subsets, reconstruction region restriction, and a modified version of the SIRT algorithm that reduces the overall run-time. When combining all of these techniques, we can reconstruct a 512 × 512 × 1022 image from acquired micro-CT data in less than thirty minutes