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

Abstract

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.