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Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction from limited data. However, its robustness is still a concern for clinical applications. Although the image quality of learning-based compensation schemes may be inadequate for clinical diagnosis, they can provide prior information for more accurate extrapolation than conventional heuristic extrapolation methods. With extrapolated projection, a conventional image reconstruction algorithm can be applied to obtain a final reconstruction. In this work, a general plug-and-play (PnP) method for truncation correction is proposed based on this idea, where various deep learning methods and conventional reconstruction algorithms can be plugged in. Such a PnP method integrates data consistency for measured data and learned prior image information for truncated data. This shows to have better robustness and interpretability than deep learning only. To demonstrate the efficacy of the proposed PnP method, two state-of-the-art deep learning methods, FBPConvNet and Pix2pixGAN, are investigated for truncation correction in cone-beam CT in noise-free and noisy cases. Their robustness is evaluated by showing false negative and false positive lesion cases. With our proposed PnP method, false lesion structures are corrected for both deep learning methods. For FBPConvNet, the root-mean-square error (RMSE) inside the FOV can be improved from 92HU to around 30HU by PnP in the noisy case. Pix2pixGAN solely achieves better image quality than FBPConvNet solely for truncation correction in general. PnP further improves the RMSE inside the FOV from 42HU to around 27HU for Pix2pixGAN. The efficacy of PnP is also demonstrated on real clinical head data. [ABSTRACT FROM AUTHOR]

In this paper, the accuracy of material decomposition (MD) using an energy discriminating photon counting detector was studied. An MD framework was established and validated using calcium hydroxyapatite (CaHA) inserts of known densities (50 mg/cm3, 100 mg/cm3, 250 mg/cm3, 400 mg/cm3), and diameters (1.2, 3.0, and 5.0 mm). These inserts were placed in a cardiac rod phantom that mimics a tissue equivalent heart and measured using an experimental photon counting detector cone beam computed tomography (PCD-CBCT) setup. The quantitative coronary calcium scores (density, mass, and volume) obtained from the MD framework were compared with the nominal values. In addition, three different calibration techniques, signal-to-equivalent thickness calibration (STC), polynomial correction (PC), and projected equivalent thickness calibration (PETC) were compared to investigate the effect of the calibration method on the quantitative values. The obtained MD estimates agreed well with the nominal values for density (mass) with mean absolute percent errors (MAPEs) 8 ± 11% (9 ± 15%) and 4 ± 6% (9 ± 14%) for STC and PETC calibration methods, respectively. PC displayed large MAPEs for density (27 ± 9%), and mass (25 ± 12%). Volume estimation resulted in large deviations between true and measured values with notable MAPEs for STC (40 ± 90%), PC (40 ± 80%), and PETC (40 ± 90%). The framework demonstrated the feasibility of quantitative CaHA mass and density scoring using PCD-CBCT. [ABSTRACT FROM AUTHOR]

The polychromatic X-ray spectrum and the energy-dependent attenuation coefficient of materials cause beam hardening artifacts in CT reconstructed volumes. These artifacts appear as cupping and streak artifacts depending on the material composition and the geometry of the imaged object. CT scanners employ projection linearization to transform polychromatic attenuation to monochromatic attenuation using a polynomial model. Polynomial coefficients are computed during calibration or using prior information such as X-ray spectrum and attenuation properties of the materials. In this paper, we are presenting a novel method to correct beam hardening artifacts by enforcing cone beam consistency conditions on the projection data. We used consistency conditions derived from Grangeat’s fundamental relation between cone beam projection data and 3-D Radon transform. The optimal polynomial coefficients for artifact reduction are iteratively estimated by minimizing the inconsistency of a set of projection pairs. The results from simulated and real datasets show the visible reduction of artifacts. Our studies also demonstrate the robustness of the algorithm when the projections are perturbed with other physical measurement and geometrical errors. The proposed method requires neither calibration nor prior information like X-ray spectrum, attenuation properties of the materials and detector response. The algorithm can be used for beam hardening correction in clinical, pre-clinical, and industrial CT systems. [ABSTRACT FROM AUTHOR]

A single-scan dual-energy low-dose cone-beam CT (CBCT) imaging technique that exploits a multi-slit filter is proposed in this paper. The multi-slit filter installed between the x-ray source and the scanned object is reciprocated during a scan. The x-ray beams through the slits would generate relatively low-energy x-ray projection data, while the filtered beams would make high-energy projection data. An iterative image reconstruction algorithm that uses an adaptive-steepest-descent method to minimize image total-variation under the constraint of data fidelity was applied to reconstructing the image from the low-energy projection data. Since the high-energy projection data suffer from a substantially high noise level due to the beam filtration, we have developed a new algorithm that exploits the joint sparsity between the low- and high-energy CT images for image reconstruction of the high-energy CT image. The proposed image reconstruction algorithm uses a gradient magnitude image (GMI) of the low-energy CT image by regularizing the difference of GMIs of the low- and high-energy CT images to be minimized. The feasibility of the proposed technique has been demonstrated by the use of various phantoms in the experimental CBCT setup. Furthermore, based on the proposed dual-energy imaging, a material differentiation was performed and its potential utility has been shown. The proposed imaging technique produced promising results for its potential application to a low-dose single-scan dual-energy CBCT. [ABSTRACT FROM PUBLISHER]

We propose a new method to measure directional modulation transfer function (MTF) using sphere phantoms in a cone beam computed tomography (CBCT) system. To measure spatially varying 3-D MTFs, we model FDK reconstruction in local regions and calculate the plane integrals of an ideal sphere phantom and reconstructed sphere phantoms. Then, we modify the Richardson-Lucy (RL) deconvolution method to relax the non-negativity constraint in RL deconvolution and apply it to estimate the directional plane spread functions (PlSFs). Directional MTFs are calculated by taking the modulus of the Fourier transform of the estimated directional PlSFs. To validate the proposed method, we simulate ideal 3-D MTFs and compare them with directional MTFs measured by simulation and experimental data along three major axes. For quantitative evaluation, we compare full-width at half-maximum (FWHM) and full-width at tenth-maximum (FWTM) of measured and ideal directional MTFs. The measured directional MTFs from simulation and experimental data show excellent agreement with the ideal directional MTFs, demonstrating the effectiveness of the proposed method to estimate directional MTFs in a CBCT system. [ABSTRACT FROM PUBLISHER]

A novel online method based on the symmetry property of the sum of projections (SOP) is proposed to obtain the geometric parameters in cone-beam computed tomography (CBCT). This method requires no calibration phantom and can be used in circular trajectory CBCT with arbitrary cone angles. An objective function is deduced to illustrate the dependence of the symmetry of SOP on geometric parameters, which will converge to its minimum when the geometric parameters achieve their true values. Thus, by minimizing the objective function, we can obtain the geometric parameters for image reconstruction. To validate this method, numerical phantom studies with different noise levels are simulated. The results show that our method is insensitive to the noise and can determine the skew (in-plane rotation angle of the detector), the roll (rotation angle around the projection of the rotation axis on the detector), and the rotation axis with high accuracy, while the mid-plane and source-to-detector distance will be obtained with slightly lower accuracy. However, our simulation studies validate that the errors of the latter two parameters brought by our method will hardly degrade the quality of reconstructed images. The small animal studies show that our method is able to deal with arbitrary imaging objects. In addition, the results of the reconstructed images in different slices demonstrate that we have achieved comparable image quality in the reconstructions as some offline methods. [ABSTRACT FROM PUBLISHER]