ObjectiveTo give a comprehensive overview of fetal doses reported in the literature when imaging the pregnant woman with suspected pulmonary embolism (PE).MethodsA comprehensive literature search in the PubMed, MEDLINE and EMBASE databases yielded a total of 1,687 papers that were included in the analysis and have been analysed with regard to fetal dose in suspected PE radiological imaging strategies.ResultsFetal dose in chest computed tomography (CT) ranges between 0.013 and 0.026 mGy in early and 0.06–0.1 mGy in late pregnancy compared with 99mTc-MAA perfusion scintigraphy with a fetal dose of 0.1–0.6 mGy in early and 0.6–0.8 mGy in late pregnancy. 99mTc-aerosol ventilation scintigraphy results in 0.1–0.3 mGy. However, there is concern about female breast irradiation in CT, which is higher than in scintigraphy. CT radiation risks for breast tissue remain unclear.ConclusionKnowledge of dosimetry and radiation risks is crucial in the radiological work-up of suspected PE in pregnancy. It is reasonable to reserve scintigraphy for pregnant patients with normal chest radiography findings and no history of asthma or chronic lung disease. Performing CT applying dose reduction instead of scintigraphy will minimise fetal radiation dose and maximise the diagnostic value.

The COVID-19 pandemic has challenged institutions' diagnostic processes worldwide. The aim of this study was to assess the feasibility of an artificial intelligence (AI)-based software tool that automatically evaluates chest computed tomography for findings of suspected COVID-19.Two groups were retrospectively evaluated for COVID-19-associated ground glass opacities of the lungs (group A: real-time polymerase chain reaction positive COVID patients, n = 108; group B: asymptomatic pre-operative group, n = 88). The performance of an AI-based software assessment tool for detection of COVID-associated abnormalities was compared with human evaluation based on COVID-19 reporting and data system (CO-RADS) scores performed by 3 readers.All evaluated variables of the AI-based assessment showed significant differences between the 2 groups (P < .01). The inter-reader reliability of CO-RADS scoring was 0.87. The CO-RADS scores were substantially higher in group A (mean 4.28) than group B (mean 1.50). The difference between CO-RADS scoring and AI assessment was statistically significant for all variables but showed good correlation with the clinical context of the CO-RADS score. AI allowed to predict COVID positive cases with an accuracy of 0.94.The evaluated AI-based algorithm detects COVID-19-associated findings with high sensitivity and may support radiologic workflows during the pandemic.Abbreviations: AI = artificial intelligence, AUC = area under the curve, CO-RADS = COVID-19 reporting and data system, CT = computed tomography, ROC = receiver operator characteristic curve, rt-PCR = real-time polymerase chain reaction.

Background/Aim: The quantitative evaluation of fat tissue, mainly for the determination of liver steatosis, is possible by using dual-energy computed tomography. Different photon energy acquisitions allow for estimation of attenuation coefficients. The effect of variation in radiation doses and reconstruction kernels on fat fraction estimation was investigated. Materials and Methods: A six-probephantom with fat concentrations of 0%, 20%, 40%, 60%, 80%, and 100% were scanned in Sn140/100 kV with radiation doses ranging between 20 and 200 mAs before and after calibration. Images were reconstructed using iterative kernels (I26,Q30,I70). Results: Fat fractions measured in dual-energy computed tomography (DECT) were consistent with the 20%-stepwise varying actual concentrations. Variation in radiation dose resulted in 3.1% variation of fat fraction. Softer reconstruction kernel (I26) underestimated the fat fraction (-9.1%), while quantitative (Q30) and sharper kernel (I70) overestimated fat fraction (10,8% and 13,1, respectively). Conclusion: The fat fraction in DECT approaches the actual fat concentration when calibrated to the reconstruction kerneö. Variation of radiation dose caused an acceptable 3% variation.Dual-energy computed tomography (DECT) has been applied increasingly in clinical routines over recent years (1, 2). Its application in clinical routine is mainly focused on material differentiation (e.g. bones, vessels, calcified plaqwies, bone marrow) and material optimization (e.g. metal artefact reduction) (3). Based on the technical concept, materials are simultaneously or consecutively scanned under two energy spectra (4). Material-specific information can be extracted in DECT based on the variance of the attenuation coefficient matrices within the different energy spectra. Unlike DECT, Hounsfield units (HU) measured in single energy computed tomography (SECT) show significant overlap for different tissue types and thus, tissue differentiation is poor.Two primary physical phenomena result in material attenuation measured in DECT: the photoelectric effect and Compton scattering. The reconstruction kernels, also referred to as the filter, algorithm or computationally intensive algorithms are used to modify the frequency content of the image data prior to back projection during image reconstruction in computed tomography (CT). Reconstruction kernels are one of the most critical parameters affecting image quality; adjust the spatial resolution and affect the image quality by sharpening or softening the image (5). Different kernels exist for evaluating different anatomical structures, i.e., soft tissue and bones. Application of different kernels may affect the attenuation measurements in SECT, especially for tissues with extremely low or high attenuation values taking, for example, water as the reference with 0 HU (6-9). A smooth kernel generates images with relatively low noise, but relatively low spatial resolution. A sharp kernel generates images with higher spatial resolution, but increased image noise. S...

Background/Aim: Multiparametric dual energy comptuted tomography (CT) imaging allows for multidimensional tissue characterization beyond the measurement of Hounsfield units. The purpose of this study was to evaluate multiple imaging parameters for different abdominal organs in dual energy CT (DECT) and analyze the effects of the contrast agent on these different parameters and provide normal values for characterization of parenchymatous organs. Patients and Methods: This retrospective analysis included a total of 484 standardized DECT scans of the abdomen. Hounsfield Units (HU), rho (electron density relative to water), Z eff (effective atomic number) and FF (fat fraction) were evaluated for liver, spleen, kidney, muscle, fat-tissue. Independent generalized estimation equation models were fitted. Results: In DECT imaging there is only little difference in mean HU mixed for parenchymatous abdominal organs. Analysis including Z eff , rho and FF allows for better discrimination while a large overlap remains for liver, spleen and muscle. Including multidimensional analysis and the effects of contrast medium further enhances tissue characterization. Small differences remain for liver and spleen. Conclusion: Organ characterization using multiparametric dual energy CT analysis is possible. An increased number of parameters obtained from DECT improves organ characterization. To our knowledge this is the first attempt to provide normal values for characterization of parenchymatous organs.The differentiation of tissue and organs on single-energy computed-tomography (CT) is performed according to differences in X-ray attenuation, which in the case of CT is measured as Hounsfield units (HU) relative to the attenuation of pure water (1, 2). The CT number is influenced by the effective atomic number (Z eff ) and the electron density of each material (ρ e ). As the effective atomic number is dependent to spectral properties, a change in X-ray energy leads to a change in the resulting Hounsfield units for a tissue accordingly (3-6).The basic idea of dual-energy CT (DECT) is to apply two different X-ray energies and hence take advantage of the different spectral properties. This method has become established over recent years and offers the possibility of new and better tissue characterizations (7-15). By acquiring two CT datasets either simultaneously or one immediately after another, DECT enables the concurrent acquisition of two attenuation maps at low-and high-energy spectrums (10,(15)(16)(17). Although different tissues may show quite similar attenuation (in terms of CT numbers) at a certain energy level (7, 8), they may show large differences in attenuation at other energy levels because of their individual electron binding energies (9,10,12). The two main reasons for this effect are Compton-scattering and photoelectric-absorption (5, 6, 11-13, 15, 18). Compton scatter is nearly independent of the photon-energy, depending primarily on the electron density of the material. It occurs predominately at high energies. However,...

Background/Aim: The effective atomic number (Z eff ) and electron density relative to water (ρ e or Rho) of elements can be derived in dual-energy computed tomography (DECT). The aim of this phantom study was to investigate the effect of different photon energies, radiation doses, and reconstruction kernels on Z eff and Rho measured in DECT. Materials and Methods: An anthropomorphic head phantom including five probes of known composition was scanned under three tube-voltage combinations in DECT: Sn140/100 kV, 140/80 kV and Sn140/80 kV with incremented radiation doses. Raw data were reconstructed with four reconstruction kernels (I30, I40, I50, and I70). Rho and Z eff were measured for each probe for all possible combinations of scan and reconstruction parameters. Results: DECT-based Rho and Z eff closely approached the reference values with a mean and maximum error of 1.7% and 6.8%, respectively. Rho was lower for 140/80 kV compared with Sn140/100 kV and Sn140/80 kV with differences being 0.009. Z eff differed among all tube voltages with the most prominent difference being 0.28 between 140/80 kV and Sn140/100 kV. Z eff was lower in I70 compared with those of I30 and I40 with a difference of 0.07. Varying radiation dose yielded a variation of 0.0002 in Rho and 0.03 in Z, both considered negligible in practice. Conclusion: DECT comprises a feasible method for the extraction of material-specific information. Slight variations should be taken into account when different radiation doses, photon energies, and kernels are applied; however, they are considered small and in practice not crucial for an effective tissue differentiation.