Simplified Statistical Image Reconstruction for X-ray CT With Beam-Hardening Artifact Compensation

Statistical image reconstruction with beam-hardening compensation for X-ray CT by a calibration step (2DIterBH)

BACKGROUND

The beam-hardening effect due to the polychromatic nature of the X-ray spectra results in two main artifacts in CT images: cupping in homogeneous areas and dark bands between dense parts in heterogeneous samples. Post-processing methods have been proposed in the literature to compensate for these artifacts, but these methods may introduce additional noise in low-dose acquisitions. Iterative methods are an alternative to compensate noise and beam-hardening artifacts simultaneously. However, they usually rely on the knowledge of the spectrum or the selection of empirical parameters.

PURPOSE

We propose an iterative reconstruction method with beam hardening compensation for small animal scanners that is robust against low-dose acquisitions and that does not require knowledge of the spectrum, overcoming the limitations of current beam-hardening correction algorithms.

METHODS

The proposed method includes an empirical characterization of the beam-hardening function based on a simple phantom in a polychromatic statistical reconstruction method. Evaluation was carried out on simulated data with different noise levels and step angles and on limited-view rodent data acquired with the ARGUS/CT system.

RESULTS

Results in small animal studies showed a proper correction of the beam-hardening artifacts in the whole sample, independently of the quantity of bone present on each slice. The proposed approach also reduced noise in the low-dose acquisitions and reduced streaks in the limited-view acquisitions.

CONCLUSIONS

Using an empirical model for the beam-hardening effect, obtained through calibration, in an iterative reconstruction method enables a robust correction of beam-hardening artifacts in low-dose small animal studies independently of the bone distribution.

Automated Defect Recognition for Additive Manufactured Parts Using Machine Perception and Visual Saliency

Metal additive manufacturing (AM) is known to produce internal defects that can impact performance. As the technology becomes more mainstream, there is a growing need to establish nondestructive inspection technologies that can assess and quantify build quality with high confidence. This article presents a complete, three-dimensional (3D) solution for automated defect recognition in AM parts using X-ray computed tomography (CT) scans. The algorithm uses a machine perception framework to automatically separate visually salient regions, that is, anomalous voxels, from the CT background. Compared with supervised approaches, the proposed concept relies solely on visual cues in 3D similar to those used by human operators in two-dimensional (2D) assuming no a priori information about defect appearance, size, and/or shape. To ingest any arbitrary part geometry, a binary mask is generated using statistical measures that separate lighter, material voxels from darker, background voxels. Therefore, no additional part or scan information, such as CAD files, STL models, or laser scan vector data, is needed. Visual saliency is established using multiscale, symmetric, and separable 3D convolution kernels. Separability of the convolution kernels is paramount when processing CT scans with potentially billions of voxels because it allows for parallel processing and thus faster execution of the convolution operation in single dimensions. Based on the CT scan resolution, kernel sizes may be adjusted to identify defects of different sizes. All adjacent anomalous voxels are subsequently merged to form defect clusters, which in turn reveals additional information regarding defect size, morphology, and orientation to the user, information that may be linked to mechanical properties, such as fatigue response. The algorithm was implemented in MATLAB™ using hardware acceleration, that is, graphics processing unit support, and tested on CT scans of AM components available at the Center for Innovative Materials Processing through Direct Digital Deposition (CIMP-3D) at Penn State's Applied Research Laboratory. Initial results show adequate processing times of just a few minutes and very low false-positive rates, especially when addressing highly salient and larger defects. All developed analytic tools can be simplified to accommodate 2D images.

Physics-informed sinogram completion for metal artifact reduction in CT imaging

Objective.Metal artifacts in the computed tomography (CT) imaging are unavoidably adverse to the clinical diagnosis and treatment outcomes. Most metal artifact reduction (MAR) methods easily result in the over-smoothing problem and loss of structure details near the metal implants, especially for these metal implants with irregular elongated shapes. To address this problem, we present the physics-informed sinogram completion (PISC) method for MAR in CT imaging, to reduce metal artifacts and recover more structural textures.Approach.Specifically, the original uncorrected sinogram is firstly completed by the normalized linear interpolation algorithm to reduce metal artifacts. Simultaneously, the uncorrected sinogram is also corrected based on the beam-hardening correction physical model, to recover the latent structure information in metal trajectory region by leveraging the attenuation characteristics of different materials. Both corrected sinograms are fused with the pixel-wise adaptive weights, which are manually designed according to the shape and material information of metal implants. To furtherly reduce artifacts and improve the CT image quality, a post-processing frequency split algorithm is adopted to yield the final corrected CT image after reconstructing the fused sinogram.Main results.We qualitatively and quantitatively evaluated the presented PISC method on two simulated datasets and three real datasets. All results demonstrate that the presented PISC method can effectively correct the metal implants with various shapes and materials, in terms of artifact suppression and structure preservation.Significance.We proposed a sinogram-domain MAR method to compensate for the over-smoothing problem existing in most MAR methods by taking advantage of the physical prior knowledge, which has the potential to improve the performance of the deep learning based MAR approaches.

Model-based three-material decomposition in dual-energy CT using the volume conservation constraint

Objective. We develop a model-based optimization algorithm for 'one-step' dual-energy (DE) CT decomposition of three materials directly from projection measurements.Approach.Since the three-material problem is inherently undetermined, we incorporate the volume conservation principle (VCP) as a pair of equality and nonnegativity constraints into the objective function of the recently reported model-based material decomposition (MBMD). An optimization algorithm (constrained MBMD, CMBMD) is derived that utilizes voxel-wise separability to partition the volume into a VCP-constrained region solved using interior-point iterations, and an unconstrained region (air surrounding the object, where VCP is violated) solved with conventional two-material MBMD. Constrained MBMD (CMBMD) is validated in simulations and experiments in application to bone composition measurements in the presence of metal hardware using DE cone-beam CT (CBCT). A kV-switching protocol with non-coinciding low- and high-energy (LE and HE) projections was assumed. CMBMD with decomposed base materials of cortical bone, fat, and metal (titanium, Ti) is compared to MBMD with (i) fat-bone and (ii) fat-Ti bases.Main results.Three-material CMBMD exhibits a substantial reduction in metal artifacts relative to the two-material MBMD implementations. The accuracies of cortical bone volume fraction estimates are markedly improved using CMBMD, with ∼5-10× lower normalized root mean squared error in simulations with anthropomorphic knee phantoms (depending on the complexity of the metal component) and ∼2-2.5× lower in an experimental test-bench study.Significance.In conclusion, we demonstrated one-step three-material decomposition of DE CT using volume conservation as an optimization constraint. The proposed method might be applicable to DE applications such as bone marrow edema imaging (fat-bone-water decomposition) or multi-contrast imaging, especially on CT/CBCT systems that do not provide coinciding LE and HE ray paths required for conventional projection-domain DE decomposition.

Simple beam hardening correction method (2DCalBH) based on 2D linearization

Objective. The polychromatic nature of the x-ray spectrum in computed tomography leads to two types of artifacts in the reconstructed image: cupping in homogeneous areas and dark bands between dense parts, such as bones. This fact, together with the energy dependence of the mass attenuation coefficients of the tissues, results in erroneous values in the reconstructed image. Many post-processing correction schemes previously proposed require either knowledge of the x-ray spectrum or the heuristic selection of some parameters that have been shown to be suboptimal for correcting different slices in heterogeneous studies. In this study, we propose and validate a method to correct the beam hardening artifacts that avoids such restrictions and restores the quantitative character of the image. Approach. Our approach extends the idea of the water-linearization method. It uses a simple calibration phantom to characterize the attenuation for different soft tissue and bone combinations of the x-ray source polychromatic beam. The correction is based on the bone thickness traversed, obtained from a preliminary reconstruction. We evaluate the proposed method with simulations and real data using a phantom composed of PMMA and aluminum 6082 as materials equivalent to water and bone. Main results. Evaluation with simulated data showed a correction of the artifacts and a recovery of monochromatic values similar to that of the post-processing techniques used for comparison, while it outperformed them on real data. Significance. The proposed method corrects beam hardening artifacts and restores monochromatic attenuation values with no need of spectrum knowledge or heuristic parameter tuning, based on the previous acquisition of a very simple calibration phantom.

Advances in micro-CT imaging of small animals

PURPOSE

Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research.

METHODS

Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-the-art hardware and ongoing challenges and promising research directions in the field.

RESULTS

Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT.

CONCLUSIONS

All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.