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Clinical Applications of Dual-Energy CT.

ABSTRACT: Dual-energy CT (DECT) provides insights into the material properties of tissues and can differentiate between tissues with similar attenuation on conventional single-energy imaging. In the conventional CT scanner, differences in the X-ray attenuation between adjacent structures are dependent on the atomic number of the materials involved, whereas in DECT, the difference in the attenuation is dependent on both the atomic number and electron density. The basic principle of DECT is to obtain two datasets with different X-ray energy levels from the same anatomic region and material decomposition based on attenuation differences at different energy levels. In this article, we discuss the clinical applications of DECT and its potential robust improvements in performance and postprocessing capabilities.

SUBMITTER: Hamid S

PROVIDER: S-EPMC8154785 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesTo evaluate inter- and intra- scan mode and scanner repeatability and reproducibility of radiomics features within and between single-energy CT (SECT) and dual-energy CT (DECT).MethodsA standardized phantom with sixteen rods of clinical-relevant densities was scanned on seven DECT-capable scanners and three SECT-only scanners. The acquisition parameters were selected to present typical abdomen-pelvic examinations with the same voxel size. Images of SECT at 120 kVp and corresponding 120 kVp-like virtual monochromatic images (VMIs) in DECT which were generated according to scanners were analyzed. Regions of interest were drawn with rigid registrations to avoid variations due to segmentation. Radiomics features were extracted via Pyradiomics platform. Test-retest repeatability was evaluated by Bland-Altman analysis for repeated scans. Intra-scanner reproducibility for different scan modes was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility among different scanners for same scan mode was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).ResultsThe test-retest analysis presented that 92.91% and 87.02% of the 94 assessed features were repeatable for SECT 120kVp and DECT 120 kVp-like VMIs, respectively. The intra-scanner analysis for SECT 120kVp vs DECT 120 kVp-like VMIs demonstrated that 10.76% and 10.28% of features were with ICC > 0.90 and CCC > 0.90, respectively. The inter-scanner analysis showed that 17.09% and 27.73% of features for SECT 120kVp were with CV < 10% and QCD < 10%, and 15.16% and 32.78% for DECT 120 kVp-like VMIs, respectively.ConclusionsThe majority of radiomics features were non-reproducible within and between SECT and DECT.Key points• Although the test-retest analysis showed high repeatability for radiomics features, the overall reproducibility of radiomics features within and between SECT and DECT was low. • Only about one-tenth of radiomics features extracted from SECT images and corresponding DECT images did match each other, even their average photon energy levels were considered alike, indicating that the scan mode potentially altered the radiomics features. • Less than one-fifth of radiomics features were reproducible among multiple SECT and DECT scanners, regardless of their fixed acquisition and reconstruction parameters, suggesting the necessity of scanning protocol adjustment and post-scan harmonization process.

Project description:The authors evaluate the energy dependent noise and bias properties of monoenergetic images synthesized from dual-energy CT (DECT) acquisitions. These monoenergetic images can be used to estimate attenuation coefficients at energies suitable for positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging. This is becoming more relevant with the increased use of quantitative imaging by PET/CT and SPECT/CT scanners. There are, however, potential variations in the noise and bias of synthesized monoenergetic images as a function of energy.The authors used analytic approximations and simulations to estimate the noise and bias of synthesized monoenergetic images of water-filled cylinders with different shapes and the NURBS-based cardiac-torso (NCAT) phantom from 40 to 520 keV, the range of SPECT and PET energies. The dual-kVp spectra were based on the GE Lightspeed VCT scanner at 80 and 140 kVp with added filtration of 0.5 mm Cu. The authors evaluated strategies of noise suppression with sinogram smoothing and dose minimization with reduction of tube currents at the two kVp settings. The authors compared the impact of DECT-based attenuation correction with single-kVp CT-based attenuation correction on PET quantitation for the NCAT phantom for soft tissue and high-Z materials of bone and iodine contrast enhancement.Both analytic calculations and simulations displayed the expected minimum noise value for a synthesized monoenergetic image at an energy between the mean energies of the two spectra. In addition the authors found that the normalized coefficient of variation in the synthesized attenuation map increased with energy but reached a plateau near 160 keV, and then remained constant with increasing energy up to 511 keV and beyond. The bias was minimal, as the linear attenuation coefficients of the synthesized monoenergetic images were within 2.4% of the known true values across the entire energy range. Compared with no sinogram smoothing, sinogram smoothing can dramatically reduce noise in the DECT-derived attenuation map. Through appropriate selection of tube currents for high and low kVp scans, DECT can deliver roughly the same amount of radiation dose as that of a single kVp CT scan, but could be used for PET attenuation correction with reduced bias in contrast agent regions by a factor of ≈ 2.6 and slightly reduced RMSE for the total image.When DECT is used for attenuation correction at higher energies, there is a noise amplification that is dependent on the energy of the synthesized monoenergetic image of linear attenuation coefficients. Sinogram smoothing reduces the noise amplification in DECT-derived attenuation maps without increasing bias. With an appropriate selection of CT techniques, a DECT scan with the same radiation dose as a single CT scan can result in a PET image with improved quantitative accuracy.

Project description:To evaluate and compare the theoretically achievable accuracy of two families of two-parameter photon cross-section models: basis vector model (BVM) and modified parametric fit model (mPFM).The modified PFM assumes that photoelectric absorption and scattering cross-sections can be accurately represented by power functions in effective atomic number and/or energy plus the Klein-Nishina cross-section, along with empirical corrections that enforce exact prediction of elemental cross-sections. Two mPFM variants were investigated: the widely used Torikoshi model (tPFM) and a more complex "VCU" variant (vPFM). For 43 standard soft and bony tissues and phantom materials, all consisting of elements with atomic number less than 20 (except iodine), we evaluated the theoretically achievable accuracy of tPFM and vPFM for predicting linear attenuation, photoelectric absorption, and energy-absorption coefficients, and we compared it to a previously investigated separable, linear two-parameter model, BVM.For an idealized dual-energy computed tomography (DECT) imaging scenario, the cross-section mapping process demonstrates that BVM more accurately predicts photon cross-sections of biological mixtures than either tPFM or vPFM. Maximum linear attenuation coefficient prediction errors were 15% and 5% for tPFM and BVM, respectively. The root-mean-square (RMS) prediction errors of total linear attenuation over the 20 keV to 1000 keV energy range of tPFM and BVM were 0.93% (tPFM) and 0.1% (BVM) for adipose tissue, 0.8% (tPFM) and 0.2% (BVM) for muscle tissue, and 1.6% (tPFM) and 0.2% (BVM) for cortical bone tissue. With exception of the thyroid and Teflon, the RMS error for photoelectric absorption and scattering coefficient was within 4% for the tPFM and 2% for the BVM. Neither model predicts the photon cross-sections of thyroid tissue accurately, exhibiting relative errors as large as 20%. For the energy-absorption coefficients prediction error, RMS errors for the BVM were less than 1.5%, while for the tPFM, the RMS errors were as large as 16%.Compared to modified PFMs, BVM shows superior potential to support dual-energy CT cross-section mapping. In addition, the linear, separable BVM can be more efficiently deployed by iterative model-based DECT image-reconstruction algorithms.

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Clinical Applications of Dual-Energy CT.

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