Abstract
BackgroundDual-layer spectral detector CT (SDCT) may provide several theoretical advantages over pre-existing DECT approaches in terms of adjustment-free sampling number and dose modulation, beam hardening correction, and production spectral images by post-processing. In addition, by adopting noise reduction algorithm, high contrast resolution was expected even in low keV level. We surmised that this improvement would be beneficial to obese people. Therefore, our aim of study is to compare image quality of virtual monochromatic spectral images (VMI) and polychromatic images reconstructed from SDCT with different body size and radiation dose using anthropomorphic liver phantom.MethodsOne small and one large size of body phantoms, each containing eight (four high- and four low-contrast) simulated focal liver lesions (FLLs) were scanned by SDCT (at 120 kVp) using different Dose Right Indexes (DRIs). VMI were reconstructed from spectral base images from 40 keV to 200 keV. Hybrid iterative reconstruction (iDose4) was used for polychromatic image reconstruction. Image noise and contrast to noise ratio (CNR) were compared. Five radiologists independently rated lesion conspicuity, diagnostic acceptability and subjective noise level in every image sets, and determined optimal keV level in VMI.ResultsCompare with conventional polychromatic images, VMI showed superior CNR at low keV level regardless of phantom size at every examined DRIs (Ps < 0.05). As body size increased, VMI had more gradual CNR decrease and noise increase than conventional polychromatic images. For low contrast FLLs in large phantom, lesion conspicuities at low radiation dose levels (DRI 16 and 19) were significantly increased in VMI (Ps < 0.05). Subjective image noise and diagnostic acceptabilities were significantly improved at VMI in both phantom size.ConclusionsVMI of dual-layer spectral detector CT with noise reduction algorithm provides improved CNR, noise reduction, and better subjective image quality in imaging of obese simulated liver phantom compared with polychromatic images. This may hold promise for improving detection of liver lesions and improved imaging of obese patients.
Highlights
Dual-layer spectral detector Computed tomography (CT) (SDCT) may provide several theoretical advantages over pre-existing Dual-energy CT (DECT) approaches in terms of adjustment-free sampling number and dose modulation, beam hardening correction, and production spectral images by post-processing
Quantitative analysis Attenuation and noise Phantom size and Dose Right Indexes (DRIs) values did not make a significant attenuation differences in virtual monochromatic spectral images (VMI) as well as polychromatic images, while attenuations of the liver parenchyma decreased as keV increased in VMI
contrast to noise ratio (CNR) of hypo- and hyper-attenuating Focal liver lesion (FLL) Compared to polychromatic images, the CNR values of hyper-attenuating FLLs in VMI were higher at low keV range in every DRI values (DRI 16, range 40–79 keV; DRI 19, range 40–75 keV; DRI 22, range 40–81 keV; DRI 25, 40–92 keV) (Fig. 4b)
Summary
Dual-layer spectral detector CT (SDCT) may provide several theoretical advantages over pre-existing DECT approaches in terms of adjustment-free sampling number and dose modulation, beam hardening correction, and production spectral images by post-processing. Previous reports have been published to demonstrate its clinical utility for evaluation of various abdominal diseases compared with conventional CT, including radiation dose reduction, iodine extraction, increased lesion conspicuity by increasing iodine contrast, reduced image artifacts such as beam-hardening artifacts, and improved tissue and material characterization [1, 3,4,5,6,7,8,9,10] These advantages of DECT are attributed to the fact that spectral decomposition of DECT data can differentiate intrinsic attenuation related to different atomic numbers and tissue density; whereas conventional polychromatic images from single energy CT cannot [11,12,13,14,15,16,17,18]. Virtual monochromatic images can be created in the projection (raw data) domain, which have a theoretic quality benefit compared to image-based methods regarding beam-hardening artifact correction [10]
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