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To describe the feasibility of a fresh frozen human cadaver model for research and training of endovascular image guided procedures in the aorta and lower extremity. The cadaver model was constructed in fresh frozen human cadaver torsos and lower extremities. Endovascular access was acquired by inserting a sheath in the femoral artery. The arterial segment of the specimen was restricted by ligation of collateral arteries and, in the torsos, clamping of the contralateral femoral artery and balloon occlusion of the supratruncal aorta. Tap water was administered through the sheath to create sufficient intraluminal pressure to manipulate devices and acquire digital subtraction angiography (DSA). Endovascular cannulation tasks of the visceral arteries (torso) or the peripheral arteries (lower extremities) were performed to assess the vascular patency of the model. Feasibility of this model is based on our institute's experiences throughout the use of six fresh frozen human cadaver torsos and 22 lower extremities. Endovascular simulation in the aortic and peripheral vasculature was achieved using this human cadaver model. Acquisition of DSA images was feasible in both the torsos and the lower extremities. Approximately 84 of the 90 target vessels (93.3%) were patent, the remaining six vessels showed signs of calcified steno-occlusive disease. Fresh frozen human cadavers provide a feasible simulation model for aortic and peripheral endovascular interventions, and can potentially reduce the need for animal experimentation. This model is suitable for the evaluation of new endovascular devices and techniques or to master endovascular skills.

Objective To determine the accuracy of iodine quantification with dual energy computed tomography (DECT) in two high-end CT systems with different spectral imaging techniques. Methods Five tubes with different iodine concentrations (0, 5, 10, 15, 20 mg/ml) were analysed in an anthropomorphic thoracic phantom. Adding two phantom rings simulated increased patient size. For third-generation dual source CT (DSCT), tube voltage combinations of 150Sn and 70, 80, 90, 100 kVp were analysed. For dual layer CT (DLCT), 120 and 140 kVp were used. Scans were repeated three times. Median normalized values and interquartile ranges (IQRs) were calculated for all kVp settings and phantom sizes. Results Correlation between measured and known iodine concentrations was excellent for both systems ( R  = 0.999–1.000, p  < 0.0001). For DSCT, median measurement errors ranged from −0.5% (IQR −2.0, 2.0%) at 150Sn/70 kVp and −2.3% (IQR −4.0, −0.1%) at 150Sn/80 kVp to −4.0% (IQR −6.0, −2.8%) at 150Sn/90 kVp. For DLCT, median measurement errors ranged from −3.3% (IQR −4.9, −1.5%) at 140 kVp to −4.6% (IQR −6.0, −3.6%) at 120 kVp. Larger phantom sizes increased variability of iodine measurements ( p  < 0.05). Conclusion Iodine concentration can be accurately quantified with state-of-the-art DECT systems from two vendors. The lowest absolute errors were found for DSCT using the 150Sn/70 kVp or 150Sn/80 kVp combinations, which was slightly more accurate than 140 kVp in DLCT. Key Points • High - end CT scanners allow accurate iodine quantification using different DECT techniques . • Lowest measurement error was found in scans with largest photon energy separation . • Dual - source CT quantified iodine slightly more accurately than dual layer CT .

ObjectivesTo evaluate the added value of deep learning (DL) analysis of the left ventricular myocardium (LVM) in resting coronary CT angiography (CCTA) over determination of coronary degree of stenosis (DS), for identification of patients with functionally significant coronary artery stenosis.MethodsPatients who underwent CCTA prior to an invasive fractional flow reserve (FFR) measurement were retrospectively selected. Highest DS from CCTA was used to classify patients as having non-significant (≤ 24% DS), intermediate (25–69% DS), or significant stenosis (≥ 70% DS). Patients with intermediate stenosis were referred for fully automatic DL analysis of the LVM. The DL algorithm characterized the LVM, and likely encoded information regarding shape, texture, contrast enhancement, and more. Based on these encodings, features were extracted and patients classified as having a non-significant or significant stenosis. Diagnostic performance of the combined method was evaluated and compared to DS evaluation only. Functionally significant stenosis was defined as FFR ≤ 0.8 or presence of angiographic high-grade stenosis (≥ 90% DS).ResultsThe final study population consisted of 126 patients (77% male, 59 ± 9 years). Eighty-one patients (64%) had a functionally significant stenosis. The proposed method resulted in improved discrimination (AUC = 0.76) compared to classification based on DS only (AUC = 0.68). Sensitivity and specificity were 92.6% and 31.1% for DS only (≥ 50% indicating functionally significant stenosis), and 84.6% and 48.4% for the proposed method.ConclusionThe combination of DS with DL analysis of the LVM in intermediate-degree coronary stenosis may result in improved diagnostic performance for identification of patients with functionally significant coronary artery stenosis.Key Points• Assessment of degree of coronary stenosis on CCTA has consistently high sensitivity and negative predictive value, but has limited specificity for identifying the functional significance of a stenosis.• Deep learning algorithms are able to learn complex patterns and relationships directly from the images without prior specification of which image features represent presence of disease, and thereby may be more sensitive to subtle changes in the LVM caused by functionally significant stenosis.• Addition of deep learning analysis of the left ventricular myocardium to the evaluation of degree of coronary artery stenosis improves diagnostic performance and increases specificity of resting CCTA. This could potentially decrease the number of patients undergoing invasive coronary angiography.

Invasive fractional flow reserve (FFR) adoption remains low mainly due to procedural and operator related factors as well as costs. Alternatively, quantitative flow ratio (QFR) achieves a high accuracy mainly outside the intermediate zone without the need for hyperaemia and wire-use. We aimed to determine the diagnostic performance of QFR and to evaluate a QFR–FFR hybrid strategy in which FFR is measured only in the intermediate zone. This retrospective study included 289 consecutive patients who underwent invasive coronary angiography and FFR. QFR was calculated for all vessels in which FFR was measured. The QFR–FFR hybrid approach was modelled using the intermediate zone of 0.77–0.87 in which FFR-measurements are recommended. The sensitivity, specificity, and accuracy on a per vessel-based analysis were 84.6%, 86.3% and 85.6% for QFR and 88.0%, 92.9% and 90.3% for the QFR–FFR hybrid approach. The diagnostic accuracy of QFR–FFR hybrid strategy with invasive FFR measurement was 93.4% and resulted in a 56.7% reduction in the need for FFR. QFR has a good correlation and agreement with invasive FFR. A hybrid QFR–FFR approach could extend the use of QFR and reduces the proportion of invasive FFR-measurements needed while improving accuracy.

ObjectiveTo quantify metal artifact reduction using 130 keV virtual monochromatic imaging (VMI) with and without orthopedic metal artifact reduction (O-MAR) in total hip arthroplasty.MethodsConventional polychromatic images and 130 keV VMI of a phantom with pellets representing bone with unilateral or bilateral prostheses were reconstructed with and without O-MAR on a dual-layer CT. Pellets were categorized as unaffected, mildly affected and severely affected.ResultsWhen 130 keV VMI with O-MAR was compared to conventional imaging with O-MAR, a relative metal artifact reduction in CT values, contrast-to-noise (CNR), signal-to-noise (SNR) and noise in mildly affected pellets (67%, 74%, 48%, 68%, respectively; p < 0.05) was observed but no significant relative metal artifact reduction in severely affected pellets. Comparison between 130 keV VMI without O-MAR and conventional imaging with O-MAR showed relative metal artifact reduction in CT values, CNR, SNR and noise in mildly affected pellets (92%, 72%, 38%, 51%, respectively; p < 0.05) but negative relative metal artifact reduction in CT values and noise in severely affected pellets (− 331% and -223%, respectively; p < 0.05), indicating aggravation of metal artifacts.ConclusionOverall, VMI of 130 keV with O-MAR provided the strongest metal artifact reduction.

Conventional Computed Tomography (CCT) measures the total x-ray attenuation for a whole x-ray spectrum, which can be used for medical diagnosis of anatomically visible pathologies. Spectral Computed Tomography (SCT) is an extension of CCT and enables the separation of two distinct processes of x-ray attenuation in tissues. The combined pair of attenuation contributions is material-specific, enabling rapid quantification of in vivo tissue composition. The combined, detailed knowledge about anatomy and pathological tissue composition allows for disease phenotyping, with great potential for improved medical diagnosis. In this paper we introduce X-Ray Attenuation Decomposition (XAD), to intuitively display and quantify the material contents of biological tissues and integrate it into the conventional, anatomical visualization of CT images. XAD gives an interactive display of material contents superimposed on anatomical information, making it easy to identify both the anatomical location as well as the concentrations of specific materials.

The amount of coronary artery calcium determined in CT scans is a well established predictor of cardiovascular events. However, high interscan variability of coronary calcium quantification may lead to incorrect cardiovascular risk assignment. Partial volume effect contributes to high interscan variability. Hence, we propose a method for coronary calcium quantification employing partial volume correction. Two phantoms containing artificial coronary artery calcifications and 293 subject chest CT scans were used. The first and second phantom contained nine calcifications and the second phantom contained three artificial arteries with three calcifications of different volumes, shapes and densities. The first phantom was scanned five times with and without extension rings. The second phantom was scanned three times without and with simulated cardiac motion (10 and 30 mm/s). Chest CT scans were acquired without ECG-synchronization and reconstructed using sharp and soft kernels. Coronary calcifications were annotated employing the clinically used intensity value thresholding (130 HU). Thereafter, a threshold separating each calcification from its background was determined using an Expectation-Maximization algorithm. Finally, for each lesion the partial content of calcification in each voxel was determined depending on its intensity and the determined threshold. Clinical calcium scoring resulted in overestimation of calcium volume for medium and high density calcifications in the first phantom, and overestimation of calcium volume for high density and underestimation for low density calcifications in the second phantom. With induced motion these effects were further emphasized. The proposed quantification resulted in better accuracy and substantially lower over- and underestimation of calcium volume even in presence of motion. In chest CT, the agreement between calcium scores from the two reconstructions improved when proposed method was used. Compared with clinical calcium scoring, proposed quantification provides a better estimate of the true calcium volume in phantoms and better agreement in calcium scores between different subject scan reconstructions.

Objective Analysis of textural features of pulmonary nodules in chest CT, also known as radiomics, has several potential clinical applications, such as diagnosis, prognostication, and treatment response monitoring. For clinical use, it is essential that these features provide robust measurements. Studies with phantoms and simulated lower dose levels have demonstrated that radiomic features can vary with different radiation dose levels. This study presents an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels. Methods Nineteen patients with a total of thirty-five pulmonary nodules underwent four chest CT scans at different radiation dose levels (60, 33, 24, and 15 mAs) in a single session. The nodules were manually delineated. To assess the robustness of features, we calculated the intra-class correlation coefficient (ICC). To visualize the effect of milliampere-second variation on groups of features, a linear model was fitted to each feature. We calculated bias and calculated the R 2 value as a measure of goodness of fit. Results A small minority of 15/100 (15%) radiomic features were considered stable (ICC > 0.9). Bias increased and R 2  decreased at lower dose, but shape features seemed to be more robust to milliampere-second variations than other feature classes. Conclusion A large majority of pulmonary nodule radiomic features were not inherently robust to radiation dose level variations. For a subset of features, it was possible to correct this variability by a simple linear model. However, the correction became increasingly less accurate at lower radiation dose levels. Clinical relevance statement Radiomic features provide a quantitative description of a tumor based on medical imaging such as computed tomography (CT). These features are potentially useful in several clinical tasks such as diagnosis, prognosis prediction, treatment effect monitoring, and treatment effect estimation. Key Points • The vast majority of commonly used radiomic features are strongly influenced by variations in radiation dose level. • A small minority of radiomic features, notably the shape feature class, are robust against dose-level variations according to ICC calculations. • A large subset of radiomic features can be corrected by a linear model taking into account only the radiation dose level.

We investigated the feasibility and extent to which iodine concentration can be reduced in computed tomography angiography imaging of the aorta and coronary arteries using low tube voltage and virtual monochromatic imaging of 3 major dual-energy CT (DECT) vendors. A circulation phantom was imaged with dual source CT (DSCT), gemstone spectral imaging (GSI) and dual-layer spectral detector CT (SDCT). For each scanner, a reference scan was acquired at 120 kVp using routine iodine concentration (300 mg I/ml). Subsequently, scans were acquired at lowest possible tube potential (70, 80, 80 kVp, respectively), and DECT-mode (80/150Sn, 80/140 and 120 kVp, respectively) in arterial phase after administration of iodine (300, 240, 180, 120, 60, 30 mg I/ml). Objective image quality was evaluated using attenuation, CNR and dose corrected CNR (DCCNR) measured in the aorta and left main coronary artery. Average DCCNR at reference was 227.0, 39.7 and 60.2 for DSCT, GSI and SDCT. Maximum iodine concentration reduction without loss of DCCNR was feasible down to 180 mg I/ml (40% reduced) for DSCT (DCCNR 467.1) and GSI (DCCNR 46.1) using conventional CT low kVp, and 120 mg I/ml (60% reduced) for SDCT (DCCNR 171.5) using DECT mode. Low kVp scanning and DECT allows for 40–60% iodine reduction without loss in image quality compared to reference. Optimal scan protocol and to which extent varies per vendor. Further patient studies are needed to extend and translate our findings to clinical practice.

Objectives The aim of this study was to evaluate the feasibility and accuracy of dual-layer spectral detector CT (SDCT) for the quantification of clinically encountered gadolinium concentrations. Methods The cardiac chamber of an anthropomorphic thoracic phantom was equipped with 14 tubular inserts containing different gadolinium concentrations, ranging from 0 to 26.3 mg/mL (0.0, 0.1, 0.2, 0.4, 0.5, 1.0, 2.0, 3.0, 4.0, 5.1, 10.6, 15.7, 20.7 and 26.3 mg/mL). Images were acquired using a novel 64-detector row SDCT system at 120 and 140 kVp. Acquisitions were repeated five times to assess reproducibility. Regions of interest (ROIs) were drawn on three slices per insert. A spectral plot was extracted for every ROI and mean attenuation profiles were fitted to known attenuation profiles of water and pure gadolinium using in-house-developed software to calculate gadolinium concentrations. Results At both 120 and 140 kVp, excellent correlations between scan repetitions and true and measured gadolinium concentrations were found ( R  > 0.99, P  < 0.001; ICCs > 0.99, CI 0.99–1.00). Relative mean measurement errors stayed below 10% down to 2.0 mg/mL true gadolinium concentration at 120 kVp and below 5% down to 1.0 mg/mL true gadolinium concentration at 140 kVp. Conclusion SDCT allows for accurate quantification of gadolinium at both 120 and 140 kVp. Lowest measurement errors were found for 140 kVp acquisitions. Key Points • Gadolinium quantification may be useful in patients with contraindication to iodine. • Dual-layer spectral detector CT allows for overall accurate quantification of gadolinium. • Interscan variability of gadolinium quantification using SDCT material decomposition is excellent.