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

Abella, Mónica; Martinez, Cristobal; Desco, Manuel; Vaquero, J.J.; Fessler, Jeffrey A.

doi:10.1109/tmi.2019.2921929

Cited by 15 publications

(7 citation statements)

References 28 publications

(47 reference statements)

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“…The execution times are primarily affected by forward and back-projections of the volume (here, a total of 3 forward and 3 back-projections implemented per iteration) and the application of the spectral model. To address these bottlenecks, we are currently investigating: (i) multiresolution reconstruction that employs a finer voxel grid in the regions of interest (Cao et al 2016, Sisniega et al 2021, and (ii) polynomial approximation of the polyenergetic forward model (Abella et al 2019). Another important consideration for clinical translation is ensuring robust convergence of the algorithm.…”

Section: Discussionmentioning

confidence: 99%

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

Liu¹,

Tivnan²,

Osgood³

et al. 2022

Phys. Med. Biol.

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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. 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 ~5-10x lower Normalized Root Mean Squared Error (NRMSE) in simulations with anthropomorphic knee phantoms (depending on the complexity of the metal component) and ~2-2.5x 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.

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Section: Discussionmentioning

confidence: 99%

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

Liu¹,

Tivnan²,

Osgood³

et al. 2022

Phys. Med. Biol.

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“…Several sophisticated and computationally expensive algorithms exist, mainly developed in the context of medical imaging, that account for the non-linear effects which are either not applicable, or are too computationally expensive to be adapted as mainstream technique with standard XCT systems. Model based iterative reconstruction (MBIR) with statistical parameter estimation 54,55 can be accurate but are computationally expensive. Metal artifact reduction (MAR) approaches [56][57][58] are not suitable for object comprised of only dense materials, and works when the metal part can be identified perfectly from a preliminary scan.…”

Section: Cad Model-assisted Beam-hardening Correctionmentioning

confidence: 99%

Enabling Rapid X-ray CT Characterisation for Additive Manufacturing Using CAD models and Deep Learning-based Reconstruction

Ziabari

Singanallur²,

Snow

et al. 2022

Preprint

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Metal additive manufacturing (AM) offers significant opportunities for flexible, efficient, and cost-effective printing of highly complex parts. Despite recent successes, metal AM is still limited to relatively few alloys. In order to qualify new alloys for metal AM, process parameters must be optimised to produce high-quality and consistent components. However, process parameter optimisation is often a challenging and cost-prohibitive task. In this work, we propose a novel, deep learning-based approach that allows for fast, high-quality, non-destructive characterisation of AM parts from sparse X-ray computed tomography (CT) measurements. The proposed approach rapidly produces high-quality reconstructions from fast X-ray CT scans of metal AM parts by leveraging digital models of the component and physics-based simulations of nonlinear interactions between X-ray radiation and metals, significantly reducing beam hardening, metal, and other common X-ray CT artifacts. The method allows for batch characterisation of hundreds of AM parts printed on the same plate with different printing parameters, paving the way for identifying the optimum printing parameters for new AM materials. We present results for an experimental case study illustrating the practical utility of our method. The proposed approach not only produced accurate 3D volumetric reconstruction images of the parts from their respective sparsely measured X-ray CT scans but also resulted in both a 300% reduction in total scan time and more than 4X improvement in probability of flaw detection in post-processing analysis of the data. This has enabled us to rapidly search for and identify useful process parameters that can lead to high-quality parts made from a novel AlCe alloy.

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“…soft tissue, bone, and so on. The LAC function 𝜇 𝑗 (𝜀) at the 𝑗 𝑡ℎ pixel of the image is decomposed as [3]:…”

Section: Materials Decompositionmentioning

confidence: 99%

Fully utilizing contrast enhancement on lung tissue as a novel basis material for lung nodule characterization by multi-energy CT

Chang

Gao

Pomeroy

et al. 2022

7th International Conference on Image Formation in X-Ray Computed Tomography

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Based on well-established X-ray physics in computed tomography (CT) imaging, the spectral responses of different materials contained in lesions are different, which brings richer contrast information at various energy bins. Hence, obtaining the material decomposition of different tissue types and exploring its spectral information for lesion diagnosis becomes extremely valuable. The lungs are housed within the torso and consist of three natural materials, i.e., soft tissue, bone, and lung tissue. To benefit the lung nodule differentiation, this study innovatively proposed to use lung tissue as one basis material along with soft tissue and bone. This set of basis materials will yield a more accurate composition analysis of lung nodules and benefit the following differentiation. Moreover, a corresponding machine learning (ML)-based computer-aided diagnosis framework for lung nodule classification is also proposed and used for evaluation. Experimental results show the advantages of the virtual monoenergetic images (VMIs) generated with lung tissue material over the VMIs without lung tissue and conventional CT images in differentiating the malignancy from benign lung nodules. The gain of 9.63% in area under the receiver operating characteristic curve (AUC) scores indicated that the energy-enhanced tissue features from lung tissue have a great potential to improve lung nodule diagnosis performance.

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

Abstract: Simplified statistical image reconstruction for X-ray CT with beam-hardening artifact compensation. IEEE Transactions on Medical Imaging. [Early access], [8] p.

Cited by 15 publications

References 28 publications

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

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

Enabling Rapid X-ray CT Characterisation for Additive Manufacturing Using CAD models and Deep Learning-based Reconstruction

Fully utilizing contrast enhancement on lung tissue as a novel basis material for lung nodule characterization by multi-energy CT

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