3D Analytic Cone-Beam Reconstruction for Multiaxial CT Acquisitions

A conventional 3rd generation Computed Tomography (CT) system with a single circular source trajectory is limited in terms of longitudinal scan coverage since extending the scan coverage beyond 40 mm results in significant cone-beam artifacts. A multiaxial CT acquisition is achieved by combining multiple sequential 3rd generation axial scans or by performing a single axial multisource CT scan with multiple longitudinally offset sources. Data from multiple axial scans or multiple sources provide complementary information. For full-scan acquisitions, we present a window-based 3D analytic cone-beam reconstruction algorithm by tessellating data from neighboring axial datasets. We also show that multi-axial CT acquisition can extend the axial scan coverage while minimizing cone-beam artifacts. For half-scan acquisitions, one cannot take advantage of conjugate rays. We propose a cone-angle dependent weighting approach to combine multi-axial half-scan data. We compute the relative contribution from each axial dataset to each voxel based on the X-ray beam collimation, the respective cone-angles, and the spacing between the axial scans. We present numerical experiments to demonstrate that the proposed techniques successfully reduce cone-beam artifacts at very large volumetric coverage

Volumetric reconstruction in the microCAT tomography system

A new system for x-ray cone-beam micro-tomography has been developed to screen mice for internal phenotypic abnormalities at the Oak Ridge National Laboratory Mammalian Genetics Facility. Currently this system uses an image reconstruction algorithm that is based on two-dimensional (fan-beam) reconstruction techniques. The disparity between the actual scanner geometry and that assumed for reconstruction purposes introduces artifacts into the reconstruction volume that become increasingly worse the further their axial distance from the midplane. In order to reconcile this disparity and reduce axial distortion artifacts, a volumetric reconstruction algorithm based on cone beam geometry was implemented. The volumetric algorithm is derived and its heuristic implementation is explained within the constraints of the system, which limit the arclength of the scanning trajectory. Reconstructions using the volumetric algorithm are analyzed and compared to reconstructions from the current method. We show that our implementation produces images of equivalent quality in the midplane, and a marked decrease in axial distortion elsewhere Volume reconstruction times are shown to be comparable to those currently achieved. The theoretical foundations are given for future work to optimize the implementation through parallelization and by overcoming the data sufficiency problem

Multi-GPU Acceleration of Iterative X-ray CT Image Reconstruction

X-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in X-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice X-ray CT reconstruction problems is their slow convergence rate and associated computational time. We seek to design and develop fast parallel SIR algorithms for clinical X-ray CT scanners. Each of the following approaches is implemented on real clinical helical CT data acquired from a Siemens Sensation 16 scanner and compared to the straightforward implementation of the Alternating Minimization (AM) algorithm of O’Sullivan and Benac [1]. We parallelize the computationally expensive projection and backprojection operations by exploiting the massively parallel hardware architecture of 3 NVIDIA TITAN X Graphical Processing Unit (GPU) devices with CUDA programming tools and achieve an average speedup of 72X over a straightforward CPU implementation. We implement a multi-GPU based voxel-driven multislice analytical reconstruction algorithm called Feldkamp-Davis-Kress (FDK) [2] and achieve an average overall speedup of 1382X over the baseline CPU implementation by using 3 TITAN X GPUs. Moreover, we propose a novel adaptive surrogate-function based optimization scheme for the AM algorithm, resulting in more aggressive update steps in every iteration. On average, we double the convergence rate of our baseline AM algorithm and also improve image quality by using the adaptive surrogate function. We extend the multi-GPU and adaptive surrogate-function based acceleration techniques to dual-energy reconstruction problems as well. Furthermore, we design and develop a GPU-based deep Convolutional Neural Network (CNN) to denoise simulated low-dose X-ray CT images. Our experiments show significant improvements in the image quality with our proposed deep CNN-based algorithm against some widely used denoising techniques including Block Matching 3-D (BM3D) and Weighted Nuclear Norm Minimization (WNNM). Overall, we have developed novel fast, parallel, computationally efficient methods to perform multislice statistical reconstruction and image-based denoising on clinically-sized datasets

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

Task-Driven Trajectory Design for Endovascular Embolization

Computed Tomography (CT) is one of the most useful and widely applied imaging modalities, employed in both diagnostic and treatment planning purposes in the medical field. Circular and spiral acquisition trajectories are traditionally employed and work well in many cases. The advent of technologies such as robotic C-arms in interventional imaging allow for more complex data acquisitions, which enables potential improvements in image quality, increased field of view, and sampling. This capability has particular potential crucial in interventional cases where images may be compromised by complex anatomy or surgical tools. In this work, we present a paradigm that uses custom non-circular orbits and prior patient information along with segmentation and registration techniques to account for surgical tools and/or implants, to improve image quality. The framework leverages the anatomical model to optimize a parameterized source-detector trajectory for a variety of specific imaging tasks. We propose an overall workflow for orbit customization with investigations of the various workflow stages as well as the overall performance of the framework

Spatial Resolution Analysis of a Variable Resolution X-ray Cone-beam Computed Tomography System

A new cone-beam computed tomography (CBCT) system is designed and implemented that can adaptively provide high resolution CT images for objects of different sizes. The new system, called Variable Resolution X-ray Cone-beam CT (VRX-CBCT) uses a CsI-based amorphous silicon flat panel detector (FPD) that can tilt about its horizontal (u) axis and vertical (v) axis independently. The detector angulation improves the spatial resolution of the CT images by changing the effective size of each detector cell. Two components of spatial resolution of the system, namely the transverse and axial modulation transfer functions (MTF), are analyzed in three different situations: (1) when the FPD is tilted only about its vertical axis (v), (2) when the FPD is tilted only about its horizontal axis (u), and (3) when the FPD is tilted isotropically about both its vertical and horizontal axes. Custom calibration and MTF phantoms were designed and used to calibrate and measure the spatial resolution of the system for each case described above. A new 3D reconstruction algorithm was developed and tested for the VRX-CBCT system, which combined with a novel 3D reconstruction algorithm, has improved the overall resolution of the system compared to an FDK-based algorithm