Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin

Multi-Energy Blended CBCT Spectral Imaging Using a Spectral Modulator with Flying Focal Spot (SMFFS)

Cone-beam CT (CBCT) spectral imaging has great potential in medical and industrial applications, but it is very challenging as scatter and spectral effects are seriously twisted. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the X-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections. Based on an in-depth analysis of optimal energy separation from different combinations of modulator materials and thicknesses, we present a practical design of a mixed two-dimensional spectral modulator that can generate multi-energy blended CBCT spectral projections. To deal with the twisted scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method by taking advantage of a scatter similarity in SMFFS. A Monte Carlo simulation is conducted to validate the strong similarity of X-ray scatter distributions across the flying focal spot positions. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a GAMMEX multi-energy CT phantom, are carried out to demonstrate the feasibility of our proposed SDMD method for CBCT spectral imaging with SMFFS. In the physics experiments, the mean relative errors in selected ROI for virtual monochromatic image (VMI) are 0.9\% for SMFFS, and 5.3\% and 16.9\% for 80/120 kV dual-energy cone-beam scan with and without scatter correction, respectively. Our preliminary results show that SMFFS can effectively improve the quantitative imaging performance of CBCT.Comment: 10 pages, 13 figure

Development of energy selective techniques in x-ray computed tomography

X-ray micro computed tomography (Micro-CT) has emerged as a powerful tool in petroleum industry for non-destructive 3D imaging of rock samples, that offers micron-scale spatial resolution images of the distribution of porosity, permeability, and fluid phases of the specimens. Micro-CT obtain the radiographic projections of a sample at different angles and use a mathematical procedure to reconstruct a 3D tomogram of the sample's X-ray attenuation coefficients. Through my thesis, the aim was to investigate and improve two main issue which micro-CT suffers from: 1) beam hardening (BH) artefacts and, 2) the requirement of material characterisation. This thesis contributes in addressing these fundamental issues by providing the "energy selective techniques" as follows. Chapter 1 provides an overview of the basics of tomography including physics of X-rays and energy dependent form of attenuation coefficient. Chapter 2 reviews the BH effects and the existing correction methods, followed by a brief review of the material characterisation methods. Chapter 3 assess the accuracy of five different linearisation BH correction models including polynomial, bimodal, power law, cubic spline and zero-order using the sample that have been imaged at ANU CT facility by measuring the BH curves directly and remapping the inverse of the models to data. Chapter 4 is based on a published conference proceeding paper [1] that applies the power law BH correction method of chapter 3 to correct the artefacts of specimens composed of concentric cylinders, e.g., a rock core within a container. Chapter 5 is based on a published paper in the Journal of Applied Physics [2] that uses dual-energy CT and the Alvarez and Macovski [3] transmitted intensity (AMTI) model to estimate the maps of density (rho) and atomic number (Z) of mineralogical samples. In this method, the attenuation coefficients are represented in the form of the two most important interactions of X-rays with atoms that is, PE and CS. This enables material discrimination as PE and CS are respectively dependent on Z and rho of materials [3]. Chapter 6 implements two simplified form of the full model of chapter 5: 1) Alvarez and Macovski polynomial (AMP) model [3], Alvarez and Macovski presented the full model but used a polynomial simplified form of it to estimate rho and Z of materials, 2) Siddiqui and Khamees (SK) model [4] that simplified the attenuation model, by assuming two monochromatic radiations. Chapter 7 presents a method to estimate the properties of sample materials from measurements of transmitted intensity and its statistical variance (TIV model). The method only requires single energy imaging, i.e., eliminates the need for requirements of dual-energy imaging for AMTI method and its simplified forms. The registered intensity on the detector is proportional to a form of "average" energy of detected quanta of X-ray spectra. The variance images can serve the same purpose as the higher energy information required in dual-energy imaging. Chapter 8 modified the TIV model of chapter 7 to apply it directly for BH correction without necessarily estimation of the properties of sample materials. The chapter also presents a simplified form of TIV model (STIV) that normalises the average intensity image

Advances in dual-energy computed tomography imaging of radiological properties

Dual-energy computed tomography (DECT) has shown great potential in the reduction of uncertainties of proton ranges and low energy photon cross section estimation used in radiation therapy planning. The work presented herein investigated three contributions for advancing DECT applications. 1) A linear and separable two-parameter DECT, the basis vector model (BVM) was used to estimate proton stopping power. Compared to other nonlinear two-parameter models in the literature, the BVM model shows a comparable accuracy achieved for typical human tissues. This model outperforms other nonlinear models in estimations of linear attenuation coefficients. This is the first study to clearly illustrate the advantages of linear model not only in accurately mapping radiological quantities for radiation therapy, but also in providing a unique model for accurate linear forward projection modelling, which is needed by the statistical iterative reconstruction (SIR) and other advanced DECT reconstruction algorithms. 2) Accurate DECT requires knowledge of x-ray beam properties. Using the Birch-Marshall1 model and beam hardening correction coefficients encoded in a CT scanner’s sinogram header files, an efficient and accurate way to estimate the x-ray spectrum is proposed. The merits of the proposed technique lie in requiring no physical transmission measurement after a one-time calibration against an independently measured spectrum. This technique can also be used in monitoring the aging of x-ray CT tubes. 3) An iterative filtered back projection with anatomical constraint (iFBP-AC) algorithm was also implemented on a digital phantom to evaluate its ability in mitigating beam hardening effects and supporting accurate material decomposition for in vivo imaging of photon cross section and proton stopping power. Compared to iFBP without constraints, both algorithms demonstrate high efficiency of convergence. For an idealized digital phantom, similar accuracy was observed under a noiseless situation. With clinically achievable noise level added to the sinograms, iFBP-AC greatly outperforms iFBP in prediction of photon linear attenuation at low energy, i.e., 28 keV. The estimated mean errors of iFBP and iFBP-AC for cortical bone are 1% and 0.7%, respectively; the standard deviations are 0.6% and 5%, respectively. The achieved accuracy of iFBP-AC shows robustness versus contrast level. Similar mean errors are maintained for muscle tissue. The standard deviation achieved by iFBP-AC is 1.2%. In contrast, the standard deviation yielded by iFBP is about 20.2%. The algorithm of iFBP-AC shows potential application of quantitative measurement of DECT. The contributions in this thesis aim to improve the clinical performance of DECT