With the advances of x-ray excitable nanophosphors, x-ray luminescence computed tomography (XLCT) has become a promising hybrid imaging modality. In particular, a cone-beam XLCT (CB-XLCT) system has demonstrated its potential in in vivo imaging with the advantage of fast imaging speed over other XLCT systems. Currently, the imaging models of most XLCT systems assume that nanophosphors emit light based on the intensity distribution of x-ray within the object, not completely reflecting the nature of the x-ray excitation process. To improve the imaging quality of CB-XLCT, an imaging model that adopts an excitation model of nanophosphors based on x-ray absorption dosage is proposed in this study. To solve the ill-posed inverse problem, a reconstruction algorithm that combines the adaptive Tikhonov regularization method with the imaging model is implemented for CB-XLCT reconstruction. Numerical simulations and phantom experiments indicate that compared with the traditional forward model based on x-ray intensity, the proposed dose-based model could improve the image quality of CB-XLCT significantly in terms of target shape, localization accuracy, and image contrast. In addition, the proposed model behaves better in distinguishing closer targets, demonstrating its advantage in improving spatial resolution.
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.
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.
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.
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, 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.