Fast kVp-switching dual energy contrast-enhanced thorax and cardiac CT: A phantom study on the accuracy of iodine concentration and effective atomic number measurement

Abstract Background: Differentiating glioma recurrence from treatment-related changes can be challenging on conventional imaging. We evaluated the use of dual-energy spectral computed tomographic (CT) quantitative parameters for this differentiation. Methods: Twenty-eight patients were examined by dual-energy spectral imaging CT. The slope of the spectral Hounsfield unit curve (λ HU ), effective atomic number (Z eff ), normalized effective atomic number (Z eff-N ), iodine concentration (IC), and normalized iodine concentration (IC N ) in the post-treatment enhanced areas were calculated. Pathological results or clinicoradiologic follow-up of ≥2 months were used for final diagnosis. Nonparametric and t -tests were used to compare quantitative parameters between glioma recurrence and treatment-related changes. Positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated; sensitivity and specificity were calculated using receiver operating characteristic (ROC) curves. ROC curves were generated using predictive probabilities to evaluate the diagnostic value. Results: There were no significant differences in quantitative parameters based on examination of pre-contrast λ HU , Z eff , Z eff-N , IC, IC N and venous phase IC N ( P >0.05). Venous phase λ HU , Z eff , Z eff-N , and IC in glioma recurrence were higher than in treatment-related changes ( P <0.001). The optimal venous phase threshold was 1.03, 7.75, 1.04, and 2.85 mg/cm 3 , achieving 66.7%, 91.7%, 83.3%, and 91.7% sensitivity; 100.0%, 77.8%, 88.9%, and 77.8% specificity; 100.0%, 73.3%, 83.3%, and 73.3% PPV; 81.8%, 93.3%, 88.9%, and 93.3% NPV; and 86.7%, 83.3%, 86.7%, and 83.3% accuracy, respectively. The areas under the curve (AUC) were 0.912, 0.912, 0.931, and 0.910 in glioma recurrence and treatment-related changes, respectively. Conclusions: Dual-energy spectral CT imaging may provide quantitative values to aid in differentiation of glioma recurrence from treatment-related changes.

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Dual-energy Spectral CT Quantitative Parameters for the Differentiation of Glioma Recurrence from Treatment-Related Changes: A Preliminary Study

10.21203/rs.2.15990/v2 ◽

2019 ◽

Author(s):

Yanchun Lv ◽

Jian Zhou ◽

Xiaofei Lv ◽

Li Tian ◽

Haoqiang He ◽

...

Keyword(s):

Atomic Number ◽

Predictive Value ◽

Effective Atomic Number ◽

Roc Curves ◽

Iodine Concentration ◽

Dual Energy ◽

Spectral Ct ◽

Quantitative Parameters ◽

Glioma Recurrence ◽

Venous Phase

Abstract Background: Differentiating glioma recurrence from treatment-related changes can be challenging on conventional imaging. We evaluated the use of dual-energy spectral computed tomographic (CT) quantitative parameters for this differentiation. Methods: Twenty-eight patients were examined by dual-energy spectral imaging CT. The slope of the spectral Hounsfield unit curve (λHU), effective atomic number (Zeff), normalized effective atomic number (Zeff-N), iodine concentration (IC), and normalized iodine concentration (ICN) in the post-treatment enhanced areas were calculated. Pathological results or clinicoradiologic follow-up of ≥2 months were used for final diagnosis. Nonparametric and t-tests were used to compare quantitative parameters between glioma recurrence and treatment-related changes. Positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated; sensitivity and specificity were calculated using receiver operating characteristic (ROC) curves. ROC curves were generated using predictive probabilities to evaluate the diagnostic value. Results: There were no significant differences in quantitative parameters based on examination of pre-contrast λHU, Zeff, Zeff-N, IC, ICN and venous phase ICN (P>0.05). Venous phase λHU, Zeff, Zeff-N, and IC in glioma recurrence were higher than in treatment-related changes (P<0.001). The optimal venous phase threshold was 1.03, 7.75, 1.04, and 2.85 mg/cm3, achieving 66.7%, 91.7%, 83.3%, and 91.7% sensitivity; 100.0%, 77.8%, 88.9%, and 77.8% specificity; 100.0%, 73.3%, 83.3%, and 73.3% PPV; 81.8%, 93.3%, 88.9%, and 93.3% NPV; and 86.7%, 83.3%, 86.7%, and 83.3% accuracy, respectively. The areas under the curve (AUC) were 0.912, 0.912, 0.931, and 0.910 in glioma recurrence and treatment-related changes, respectively. Conclusions: Dual-energy spectral CT imaging may provide quantitative values to aid in differentiation of glioma recurrence from treatment-related changes.

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13. Dual Energy Computed Tomography for Perfusion Imaging

Inquiry@Queen's Undergraduate Research Conference Proceedings ◽

10.24908/iqurcp.10104 ◽

2018 ◽

Author(s):

Carly Pellow

Keyword(s):

Computed Tomography ◽

Atomic Number ◽

Perfusion Imaging ◽

Cancer Metastasis ◽

Dynamic Range ◽

Effective Atomic Number ◽

Dual Energy ◽

Dual Energy Ct ◽

Contrast Enhanced Ct ◽

Contrast Enhanced

The metastatic spreading ability of cancer heavily depends on microcirculation, requiring the formation of new blood vessels in the vascular network, or angiogenesis. Indirect measurement of angiogenesis can be performed by computed tomography (CT) perfusion imaging, where blood flow is observed using dynamic contrast-enhanced techniques. However, contrast-enhancement in tissues can be poor due to inadequate CT calibration, leading to an increase in radiation dose to the patient. It is hypothesized that the use of dual-energy CT will provide a more robust basis for calibration of contrast-enhanced imaging without increasing dose. The purpose of this study is to improve the sensitivity and accuracy of perfusion imaging by using atomic number extraction of Iodine contrast agent concentration from dual-energy datasets. A stoichiometric calibration for CT was done with the effective atomic number and relative electron density extracted in MATLAB, resulting in a wider dynamic range of effective atomic number per mgI/mL. Dual-energy CT as a tool for calibrating perfusion imaging shows promise of increasing the range and signal to noise ratio of contrast-enhanced CT imaging. Future work will extend the study from a static to dynamic quantification to explore dual-energy CT for perfusion imaging, with the goals of developing a robust stoichiometric calibration for quality assurance purposes, and further insights on tumour angiogenesis and cancer metastasis.

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Material Identification in Presence of Metal for Baggage Screening

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-294 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 294-1-294-8

Author(s):

Sandamali Devadithya ◽

David Castañón

Keyword(s):

Atomic Number ◽

Effective Atomic Number ◽

Simulated Data ◽

Dual Energy ◽

Basis Functions ◽

Total Variation Regularization ◽

Ct Scanner ◽

Edge Preserving ◽

Baggage Screening ◽

Dual Energy Imaging

Dual-energy imaging has emerged as a superior way to recognize materials in X-ray computed tomography. To estimate material properties such as effective atomic number and density, one often generates images in terms of basis functions. This requires decomposition of the dual-energy sinograms into basis sinograms, and subsequently reconstructing the basis images. However, the presence of metal can distort the reconstructed images. In this paper we investigate how photoelectric and Compton basis functions, and synthesized monochromatic basis (SMB) functions behave in the presence of metal and its effect on estimation of effective atomic number and density. Our results indicate that SMB functions, along with edge-preserving total variation regularization, show promise for improved material estimation in the presence of metal. The results are demonstrated using both simulated data as well as data collected from a dualenergy medical CT scanner.

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A Spectrum-Adaptive Decomposition Method for Effective Atomic Number Estimation Using Dual Energy CT

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-293 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 293-1-293-7

Author(s):

Ankit Manerikar ◽

Fangda Li ◽

Avinash C. Kak

Keyword(s):

Atomic Number ◽

Decomposition Method ◽

Traditional Approach ◽

Effective Atomic Number ◽

Dual Energy ◽

Performance Comparison ◽

Estimation Methods ◽

Dual Energy Ct ◽

Projection Data ◽

X Ray

Dual Energy Computed Tomography (DECT) is expected to become a significant tool for voxel-based detection of hazardous materials in airport baggage screening. The traditional approach to DECT imaging involves collecting the projection data using two different X-ray spectra and then decomposing the data thus collected into line integrals of two independent characterizations of the material properties. Typically, one of these characterizations involves the effective atomic number (Zeff) of the materials. However, with the X-ray spectral energies typically used for DECT imaging, the current best-practice approaches for dualenergy decomposition yield Zeff values whose accuracy range is limited to only a subset of the periodic-table elements, more specifically to (Z < 30). Although this estimation can be improved by using a system-independent ρe — Ze (SIRZ) space, the SIRZ transformation does not efficiently model the polychromatic nature of the X-ray spectra typically used in physical CT scanners. In this paper, we present a new decomposition method, AdaSIRZ, that corrects this shortcoming by adapting the SIRZ decomposition to the entire spectrum of an X-ray source. The method reformulates the X-ray attenuation equations as direct functions of (ρe, Ze) and solves for the coefficients using bounded nonlinear least-squares optimization. Performance comparison of AdaSIRZ with other Zeff estimation methods on different sets of real DECT images shows that AdaSIRZ provides a higher output accuracy for Zeff image reconstructions for a wider range of object materials.

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