Quantitative Comparison of Virtual Monochromatic Images of Dual Energy Computed Tomography Systems: Beam Hardening Artifact Correction and Variance in Computed Tomography Numbers: A Phantom Study

Wu, Rongli; Watanabe, Yoshiyuki; Satoh, Kazuhiko; Liao, Yuan-Mei; Takahashi, Hiroto; Tanaka, Hisashi; Tomiyama, Noriyuki

doi:10.1097/rct.0000000000000726

Cited by 11 publications

(2 citation statements)

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“…The rate of photoelectric absorption in GM is higher than that in WM because WM contains 8% more carbon and 8% less oxygen than GM for myelinization, resulting in a lower effective atomic number of WM compared to that of GM [ 19 ]. Additionally, as the energy level decreases in the VMIs, noise increases [ 7 ], but we found that image noise in the VMIs at all energy levels tested was significantly lower than that of conventional CT images. These observations are comparable to those from previous reports of lower noise in VMIs compared with that in conventional images in DL-DECT [ 20 21 ].…”

Section: Discussionmentioning

confidence: 92%

“…Dual-energy CT (DECT) images are derived by a combination of two separate polychromatic image data, which enables the creation of virtual monochromatic images (VMIs) at arbitrary energy (keV) levels and material-specific images such as iodine density images [ 3 4 5 6 ]. VMIs represent a simulation of the attenuation behavior of an ideal monochromatic X-ray beam, and they can better suppress both beam-hardening and energy-shift phenomena compared with conventional polychromatic CT images [ 7 8 9 10 ]. Thus, VMIs can be used to obtain constituent images of varying contrasts by changing the monochromatic energy levels.…”

Section: Introductionmentioning

confidence: 99%

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Virtual Monochromatic Image Quality from Dual-Layer Dual-Energy Computed Tomography for Detecting Brain Tumors

et al. 2021

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Objective To evaluate the usefulness of virtual monochromatic images (VMIs) obtained using dual-layer dual-energy CT (DL-DECT) for evaluating brain tumors. Materials and Methods This retrospective study included 32 patients with brain tumors who had undergone non-contrast head CT using DL-DECT. Among them, 15 had glioblastoma (GBM), 7 had malignant lymphoma, 5 had high-grade glioma other than GBM, 3 had low-grade glioma, and 2 had metastatic tumors. Conventional polychromatic images and VMIs (40–200 keV at 10 keV intervals) were generated. We compared CT attenuation, image noise, contrast, and contrast-to-noise ratio (CNR) between tumor and white matter (WM) or grey matter (GM) between VMIs showing the highest CNR (optimized VMI) and conventional CT images using the paired t test. Two radiologists subjectively assessed the contrast, margin, noise, artifact, and diagnostic confidence of optimized VMIs and conventional images on a 4-point scale. Results The image noise of VMIs at all energy levels tested was significantly lower than that of conventional CT images ( p < 0.05). The 40-keV VMIs yielded the best CNR. Furthermore, both contrast and CNR between the tumor and WM were significantly higher in the 40 keV images than in the conventional CT images ( p < 0.001); however, the contrast and CNR between tumor and GM were not significantly different ( p = 0.47 and p = 0.31, respectively). The subjective scores assigned to contrast, margin, and diagnostic confidence were significantly higher for 40 keV images than for conventional CT images ( p < 0.01). Conclusion In head CT for patients with brain tumors, compared with conventional CT images, 40 keV VMIs from DL-DECT yielded superior tumor contrast and diagnostic confidence, especially for brain tumors located in the WM.

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