Motion compensated iterative reconstruction for cardiac X-ray tomography

Within this Ph.D. project, three-dimensional reconstruction methods for moving objects (with a focus on the human heart) from cone-beam X-ray projections using iterative reconstruction algorithms were developed and evaluated. This project was carried in collaboration with the Digital Imaging Group of Philips Research Europe – Hamburg. In cardiac cone-beam computed tomography (CT) a large effort is continuously dedicated to increase scanning speed in order to minimize patient or organ motion during acquisition. In particular, motion causes severe artifacts such as blurring and streaks in tomographic images. While for a large class of applications the current scanning speed is sufficient, in cardiac CT image reconstruction improvements are still required. Whereas it is currently feasible to achieve stable image quality in the resting phases of the cardiac cycle, in the phase of fast motion data acquisition is too slow. A variety of algorithms to reduce or compensate for motion artifacts have been proposed in literature. Most of the correction methods address the calculation of consistent projection data belonging to the same motion state (gated CT reconstruction). Even if gated CT leads to better results, not only with respect to the processing time but also regarding the image quality, it is also limited in its temporal and spatial resolution due to the mechanical movement of the gantry. This can lead to motion blurring, especially in the phases of fast cardiac motion during the RR interval. A motion-compensated reconstruction method for CT can be used to improve the resolution of the reconstructed image and to suppress motion blurring. Iterative techniques are a promising approach to solve this problem, since no direct inversion methods are known for arbitrarily moving objects. In this work, we therefore introduced motion compensation into image reconstruction. In order to determine the unknown cardiac motion, 3 different cardiac-motion estimation methodologies were implemented. Visual and quantitative assessment of the method in a number of applications, including: phantoms; cardiac CT reconstructions; Region of Interest (ROI) CT reconstructions of left and right coronaries of several clinical patients, confirmed its potential

Local Tomography and the Motion Estimation Problem

In this paper we study local tomography (LT) in the motion contaminated case. It is shown that microlocally, away from some critical directions, LT is equivalent to a pseudodifferential operator of order one. LT also produces nonlocal artifacts that are of the same strength as useful singularities. If motion is not accurately known, singularities inside the object f being scanned spread in different directions. A single edge can become a double edge. In such a case the image of f looks cluttered. Based on this observation we propose an algorithm for motion estimation. We propose an empiric measure of image clutter, which we call edge entropy. By minimizing edge entropy we find the motion model. The algorithm is quite flexible and is also used for solving the misalignment correction problem. The results of numerical experiments on motion estimation and misalignment correction are very encouraging

Motion compensated micro-CT reconstruction for in-situ analysis of dynamic processes

This work presents a framework to exploit the synergy between Digital Volume Correlation ( DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X-ray CT (4D-mu CT) and obtain quantitative results from the acquired dataset in the form of 3D strain maps which can be directly correlated to the material properties. Furthermore, we show that the developed framework is capable of strongly reducing motion artifacts even in a dataset containing a single 360 degrees rotation

Toward time resolved 4D cardiac CT imaging with patient dose reduction: estimating the global heart motion

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An Algorithm for Motion Estimation from the Tomographic Data

In this paper we study local tomography (LT) in the motion contaminated case. It is shown that microlocally, away from some critical directions, LT is equivalent to a pseudo-differential operator of order one. LT also produces non-local artifacts that are of the same strength as useful singularities. If motion is not accurately known, singularities inside the object f being scanned spread in different directions. A single edge can become a double edge. In such a case the image of f looks cluttered. Based on this observation we propose an algorithm for motion estimation. We propose an empiric measure of image clutter, which we call edge entropy. By minimizing edge entropy we find the motion model. The algorithm is quite flexible and is used also for solving the misalignment correction problem. The results of numerical experiments on motion estimation and misalignment correction are very encouraging

Regularized 4D-CT reconstruction from a single dataset with a spatio-temporal prior

X-ray Computerized Tomography (CT) reconstructions can be severely impaired by the patient’s respiratory motion and cardiac beating. Motion must thus be recovered in addition to the 3D reconstruction problem. The approach generally followed to reconstruct dynamic volumes consists of largely increasing the number of projections so that independent reconstructions be possible using only subsets of projections from the same phase of the cyclic movement. Apart from this major trend, motion compensation (MC) aims at recovering the object of interest and its motion by accurately modeling its deformation over time, allowing to use the whole dataset for 4D reconstruction in a coherent way.We consider a different approach for dynamic reconstruction based on inverse problems, without any additional measurements, nor explicit knowledge of the motion. The dynamic sequence is reconstructed out of a single data set, only assuming the motion’s continuity and periodicity. This inverse problem is solved by the minimization of the sum of a data-fidelity term, consistent with the dynamic nature of the data, and a regularization term which implements an efficient spatio-temporal version of the total variation (TV). We demonstrate the potential of this approach and its practical feasibility on 2D and 3D+t reconstructions of a mechanical phantom and patient data

The Estimation and Correction of Rigid Motion in Helical Computed Tomography

X-ray CT is a tomographic imaging tool used in medicine and industry. Although technological developments have significantly improved the performance of CT systems, the accuracy of images produced by state-of-the-art scanners is still often limited by artefacts due to object motion. To tackle this problem, a number of motion estimation and compensation methods have been proposed. However, no methods with the demonstrated ability to correct for rigid motion in helical CT scans appear to exist. The primary aims of this thesis were to develop and evaluate effective methods for the estimation and correction of arbitrary six degree-of-freedom rigid motion in helical CT. As a first step, a method was developed to accurately estimate object motion during CT scanning with an optical tracking system, which provided sub-millimetre positional accuracy. Subsequently a motion correction method, which is analogous to a method previously developed for SPECT, was adapted to CT. The principle is to restore projection consistency by modifying the source-detector orbit in response to the measured object motion and reconstruct from the modified orbit with an iterative reconstruction algorithm. The feasibility of this method was demonstrated with a rapidly moving brain phantom, and the efficacy of correcting for a range of human head motions acquired from healthy volunteers was evaluated in simulations. The methods developed were found to provide accurate and artefact-free motion corrected images with most types of head motion likely to be encountered in clinical CT imaging, provided that the motion was accurately known. The method was also applied to CT data acquired on a hybrid PET/CT scanner demonstrating its versatility. Its clinical value may be significant by reducing the need for repeat scans (and repeat radiation doses), anesthesia and sedation in patient groups prone to motion, including young children