Analytic image reconstruction in local phase-contrast tomography.
ABSTRACT Phase-contrast tomography is a non-interferometric imaging technique for reconstructing the refractive index distribution of a weakly absorbing object from a set of tomographic projection measurements. In many practical situations, the spatial resolution of the reconstructed image can be increased by minimizing the field of view (FOV) of the imaging system. When the object of interest is larger than the FOV, the measured projections are truncated and one is faced with a local tomography reconstruction problem. In this work, we analytically and numerically investigate the problem of reconstructing tomographic images from truncated phase-contrast projection data. A simple backprojection algorithm for reconstructing object discontinuities from truncated phase-contrast projection data is proposed and investigated that involves no explicit filtering of the projection data. We also investigate the use of the filtered backprojection algorithm and a local tomography reconstruction algorithm developed for absorption CT. These reconstruction algorithms are implemented and numerically investigated to corroborate our theoretical assertions.
SourceAvailable from: Qiaofeng Xu
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ABSTRACT: Many new promising X-ray-based biomedical imaging technologies have emerged over the last two decades. Five different novel X-ray based imaging technologies are discussed in this dissertation: differential phase-contrast tomography (DPCT), grating-based phase-contrast tomography (GB-PCT), spectral-CT (K-edge imaging), cone-beam computed tomography (CBCT), and in-line X-ray phase contrast (XPC) tomosynthesis. For each imaging modality, one or more specific problems prevent them being effectively or efficiently employed in clinical applications have been discussed. Firstly, to mitigatethe long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods in DPCT, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction. Secondly, to improve image quality in grating-based phase-contrast tomography, we incorporate 2nd order statistical properties of the object property sinograms, including correlations between them, into the formulation of an advanced multi-channel (MC) image reconstruction algorithm, which reconstructs three object properties simultaneously. We developed an advanced algorithm based on the proximal point algorithm and the augmented Lagrangian method to rapidly solve the MC reconstruction problem. Thirdly, to mitigate image artifacts that arise from reduced-view and/or noisy decomposed sinogram data in K-edge imaging, we exploited the inherent sparseness of typical K-edge objects and incorporated the statistical properties of the decomposed sinograms to formulate two penalized weighted least square problems with a total variation (TV) penalty and a weighted sum of a TV penalty and an ℓ1-norm penalty with a wavelet sparsifying transform. We employed a fast iterative shrinkage/thresholding algorithm (FISTA) and splitting-based FISTA algorithm to solve these two PWLS problems. Fourthly, to enable advanced iterative algorithms to obtain better diagnostic images and accurate patient positioning information in image-guided radiation therapy for CBCT in a few minutes, two accelerated variants of the FISTA for PLS-based image reconstruction are proposed. The algorithm acceleration is obtained by replacing the original gradient-descent step by a sub-problem that is solved by use of the ordered subset concept (OS-SART). In addition, we also present efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units (GPUs). Finally, we employed our developed accelerated version of FISTA for dealing with the incomplete (and often noisy) data inherent to in-line XPC tomosynthesis which combines the concepts of tomosynthesis and in-line XPC imaging to utilize the advantages of both for biological imaging applications. We also investigate the depth resolution properties of XPC tomosynthesis and demonstrate that the z-resolution properties of XPC tomosynthesis is superior to that of conventional absorption-based tomosynthesis. To investigate all these proposed novel strategies and new algorithms in these different imaging modalities, we conducted computer simulation studies and real experimental data studies. The proposed reconstruction methods will facilitate the clinical or preclinical translation of these emerging imaging methods.04/2014, Degree: Ph.D., Supervisor: Mark A. Anastasio, Viktor Gruev, Lihong Wang, Mladen Victor Wickerhauser, Deshan Yang
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ABSTRACT: Propagation-based x-ray phase-contrast (PB XPC) tomosynthesis combines the concepts of tomosynthesis and XPC imaging to realize the advantages of both for biological imaging applications. Tomosynthesis permits reductions in acquisition times compared with full-view tomography, while XPC imaging provides the opportunity to resolve weakly absorbing structures. In this note, an investigation of the depth resolving properties of PB XPC tomosynthesis is conducted. The results demonstrate that in-plane structures display strong boundary-enhancement while out-of-plane structures do not. This effect can facilitate the identification of in-plane structures in PB XPC tomosynthesis that could normally not be distinguished from out-of-plane structures in absorption-based tomosynthesis.Physics in Medicine and Biology 04/2015; 60(8):N151-N165. DOI:10.1088/0031-9155/60/8/N151 · 2.92 Impact Factor