Wenlei Liu

Prior image based anisotropic edge guided TV minimization for few-view CT reconstruction

To improve the spatial resolution of the image reconstructed by the conventional total variation (TV) algorithm, we propose a prior image based anisotropic edge guided TV minimization (PIEGTV) algorithm for few-view CT reconstruction. In this study, an anisotropic edge of the prior image is detected using the proposed edge detector. Then the weights of the TV discretization term for the to-be-estimated image are updated by the anisotropic edge information. To solve the minimization problem of the PIEGTV reconstruction, a similar TV-based minimization implementation is developed to deal with the raw data fidelity and other constraints. The results with computer simulations for the Shepp-Logan phantom and experimental data from a physical phantom demonstrate that the proposed algorithm can yield images with noticeable gains in edge preserving and shape preserving for small structures, compared to the conventional and a few modified TV algorithms.

CT reconstruction from few-views by edge guided TV minimization

Based on compressed sensing (CS) recovery theory, total variation (TV) minimization has been successfully used in computed tomography (CT) reconstruction for sparse or limited angle data. When the number of projection views is much smaller or noise exists in the projection data, a conventional TV minimization algorithm often suffers from the decrease of spatial resolution especially in the edge area. Considering that the edge is an important index for image quality and it reflects the sparsity of an image to some extent, in this paper, we propose an edge guided TV (EGTV) minimization reconstruction algorithm for better edge preservation in CT reconstruction. EGTV with both isotropic and anisotropic weights of the TV discretization term is derived by importing edge information into TV calculating process. When an edge of the to-be-estimated image is detected, the associated weight of the TV additive element is adjusted. To solve the EGTV minimization reconstruction problem, a similar TV-based minimization implementation was developed to deal with the raw data fidelity and other constraints. The results with computer simulation reveals that EGTV minimization algorithm can improve the image quality and preserve the edge characteristics compared to conventional TV minimization algorithm.

Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography

Cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed as a new molecular imaging modality recently. It can obtain both anatomical and functional tomographic images of an object efficiently, with the excitation of nanophosphors in vivo or in vitro by cone-beam X-rays. However, the ill-posedness of the CB-XLCT inverse problem degrades the image quality and makes it difficult to resolve adjacent luminescent targets with different concentrations, which is essential in the monitoring of nanoparticle metabolism and drug delivery. To address this problem, a multi-voltage excitation imaging scheme combined with principal component analysis is proposed in this study. Imaging experiments performed on physical phantoms by a custom-made CB-XLCT system demonstrate that two adjacent targets, with different concentrations and an edge-to-edge distance of 0 mm, can be effectively resolved.

Computer simulation of low-dose CT with clinical lung image database: a preliminary study

Large samples of raw low-dose CT (LDCT) projections on lungs are needed for evaluating or designing novel and effective reconstruction algorithms suitable for lung LDCT imaging. However, there exists radiation risk when getting them from clinical CT scanning. To avoid the problem, a new strategy for producing large samples of lung LDCT projections with computer simulations is proposed in this paper. In the simulation, clinical images from the publicly available medical image database-the Lung Image Database Consortium(LIDC) and Image Database Resource Initiative (IDRI) database (LIDC/IDRI) are used as the projected object to form the noise-free sinogram. Then by adding a Poisson distributed quantum noise plus Gaussian distributed electronic noise to the projected transmission data calculated from the noise-free sinogram, different noise levels of LDCT projections are obtained. At last the LDCT projections are used for evaluating two reconstruction strategies. One is the conventional filtered back projection (FBP) algorithm and the other is FBP reconstruction from the filtered sinogram with penalized weighted least square criterion (PWLS-FBP). Images reconstructed with the LDCT simulations have shown that the PWLS-FBP algorithm performs better than the FBP algorithm in reducing streaking artifacts and preserving resolution. Preliminary results indicate that the feasibility of the proposed lung LDCT simulation strategy for helping to determine advanced reconstruction algorithms.

Algorithm for x-ray beam hardening and scatter correction in low-dose cone-beam CT: phantom studies

X-ray scatter poses a significant limitation to image quality in cone-beam CT (CBCT), as well as beam hardening, resulting in image artifacts, contrast reduction, and lack of CT number accuracy. Meanwhile the x-ray radiation dose is also non-ignorable. Considerable scatter or beam hardening correction methods have been developed, independently, and rarely combined with low-dose CT reconstruction. In this paper, we combine scatter suppression with beam hardening correction for sparse-view CT reconstruction to improve CT image quality and reduce CT radiation. Firstly, scatter was measured, estimated, and removed using measurement-based methods, assuming that signal in the lead blocker shadow is only attributable to x-ray scatter. Secondly, beam hardening was modeled by estimating an equivalent attenuation coefficient at the effective energy, which was integrated into the forward projector of the algebraic reconstruction technique (ART). Finally, the compressed sensing (CS) iterative reconstruction is carried out for sparse-view CT reconstruction to reduce the CT radiation. Preliminary Monte Carlo simulated experiments indicate that with only about 25% of conventional dose, our method reduces the magnitude of cupping artifact by a factor of 6.1, increases the contrast by a factor of 1.4 and the CNR by a factor of 15. The proposed method could provide good reconstructed image from a few view projections, with effective suppression of artifacts caused by scatter and beam hardening, as well as reducing the radiation dose. With this proposed framework and modeling, it may provide a new way for low-dose CT imaging.

Scattering-compensated cone beam x-ray luminescence computed tomography

X-ray luminescence computed tomography (XLCT) opens new possibilities to perform molecular imaging with x-ray. It is a dual modality imaging technique based on the principle that some nanophosphors can emit near-infrared (NIR) light when excited by x-rays. The x-ray scattering effect is a great issue in both CT and XLCT reconstruction. It has been shown that if the scattering effect compensated, the reconstruction average relative error can be reduced from 40% to 12% in the in the pencil beam XLCT. However, the scattering effect in the cone beam XLCT has not been proved. To verify and reduce the scattering effect, we proposed scattering-compensated cone beam x-ray luminescence computed tomography using an added leading to prevent the spare x-ray outside the irradiated phantom in order to decrease the scattering effect. Phantom experiments of two tubes filled with Y2O3:Eu3+ indicated that the proposed method could reduce the scattering by a degree of 30% and can reduce the location error from 1.8mm to 1.2mm. Hence, the proposed method was feasible to the general case and actual experiments and it is easy to implement.

Modulation transfer function determination using the edge technique for cone-beam micro-CT

Evaluating spatial resolution is an essential work for cone-beam computed tomography (CBCT) manufacturers, prototype designers or equipment users. To investigate the cross-sectional spatial resolution for different transaxial slices with CBCT, the slanted edge technique with a 3D slanted edge phantom are proposed and implemented on a prototype cone-beam micro-CT. Three transaxial slices with different cone angles are under investigation. An over-sampled edge response function (ERF) is firstly generated from the intensity of the slightly tiled air to plastic edge in each row of the transaxial reconstruction image. Then the oversampled ESF is binned and smoothed. The derivative of the binned and smoothed ERF gives the line spread function (LSF). At last the presampled modulation transfer function (MTF) is calculated by taking the modulus of the Fourier transform of the LSF. The spatial resolution is quantified with the spatial frequencies at 10% MTF level and full-width-half-maximum (FWHM) value. The spatial frequencies at 10% of MTFs are 3.1±0.08mm-1, 3.0±0.05mm-1, and 3.2±0.04mm-1 for the three transaxial slices at cone angles of 3.8°, 0°, and -3.8° respectively. The corresponding FWHMs are 252.8μm, 261.7μm and 253.6μm. Results indicate that cross-sectional spatial resolution has no much differences when transaxial slices being 3.8° away from z=0 plane for the prototype conebeam micro-CT.

Beam hardening correction for sparse-view CT reconstruction

Beam hardening, which is caused by spectrum polychromatism of the X-ray beam, may result in various artifacts in the reconstructed image and degrade image quality. The artifacts would be further aggravated for the sparse-view reconstruction due to insufficient sampling data. Considering the advantages of the total-variation (TV) minimization in CT reconstruction with sparse-view data, in this paper, we propose a beam hardening correction method for sparse-view CT reconstruction based on Brabant’s modeling. In this correction model for beam hardening, the attenuation coefficient of each voxel at the effective energy is modeled and estimated linearly, and can be applied in an iterative framework, such as simultaneous algebraic reconstruction technique (SART). By integrating the correction model into the forward projector of the algebraic reconstruction technique (ART), the TV minimization can recover images when only a limited number of projections are available. The proposed method does not need prior information about the beam spectrum. Preliminary validation using Monte Carlo simulations indicates that the proposed method can provide better reconstructed images from sparse-view projection data, with effective suppression of artifacts caused by beam hardening. With appropriate modeling of other degrading effects such as photon scattering, the proposed framework may provide a new way for low-dose CT imaging.

Sparse-view reconstruction from restored low-dose CT projections

To further alleviate the ionizing radiation damage of computed tomography (CT), we propose a method of sparse-view reconstruction based on low-dose CT projection data. It first utilizes a penalized weighted least square (PWLS) restoration for low-dose CT projection based on noise modeling. Then the CT images are reconstructed from fewer views of denoised projection data. Reconstruction results from a simulation experiment with a Shepp-Logan phantom and real experiment data indicate that the proposed method can obtain relatively good reconstructed images from low-dose sparse-view projection data, as well as effective suppression of noise and streak artifacts in reconstructed images.