Characteristics of the deep learning-based virtual monochromatic image with fast kilovolt-switching CT: a phantom study.

Abstract

We assessed the physical properties of virtual monochromatic images (VMIs) obtained with different energy levels in various contrast settings and radiation doses using deep learning-based spectral computed tomography (DL-Spectral CT) and compared the results with those from single-energy CT (SECT) imaging. A Catphan® 600 phantom was scanned by DL-Spectral CT at various radiation doses. We reconstructed the VMIs obtained at 50, 70, and 100keV. SECT (120kVp) images were acquired at the same radiation doses. The standard deviations of the CT number and noise power spectrum (NPS) were calculated for noise characterization. We evaluated the spatial resolution by determining the 10% task-based transfer function (TTF) level, and we assessed the task-based detectability index (d'). Regardless of the radiation dose, the noise was the lowest at 70keV VMI. The NPS showed that the noise amplitude at all spatial frequencies was the lowest among other VMI and 120kVp images. The spatial resolution was higher for 70keV VMI compared to the other VMIs, except for high-contrast objects. The d' of 70keV VMI was the highest among the VMI and 120kVp images at all radiation doses and contrast settings. The d' of the 70keV VMIs at the minimum dose was higher than that at the maximum dose in any other image. The physical properties of the DL-Spectral CT VMIs varied with the energy level. The 70keV VMI had the highest detectability by far among the VMI and 120-kVp images. DL-Spectral CT may be useful to reduce radiation doses.