Background: Cardiac CTA is an indispensable imaging tool for TAVR procedure planning. Post-processing of the dual-energy CT utilizing dual-source and kV-switching approach enables an increase in the density of iodine and allows the reduction of iodine dose. We hypothesized that the use of a dual-layer Spectral Detector CT (SDCT) can enhance the signal of intravascular iodine contrast material, reduce iodine contrast material volume, and facilitate pre-TAVR planning. Methods: We tested this in a preclinical porcine animal model, with results suggesting that spectral imaging may be superior to conventional imaging even at 21-35% of the full contrast medium dose with regard to reader confidence, higher SNR and CNR at the level of the aortic annulus and root. We subsequently followed this up by a prospective human validation study of 24 patients undergoing TAVR. Results: We demonstrate that SNR and CNR with SDCT were significantly higher (highest in lower energy virtual mono-energetic images (VMI) (monoE 40 keV) compared to conventional images, with the spectral images preferred for procedure planning by an experienced TAVR operator and imaging specialist. This was associated with a reduction in inter-observer variability in TAVR sizing measurements in low dose contrast studies (33% of the full contrast dose) resulting in a higher rate of agreement on the choice of valve prosthesis size. Conclusion: Low contrast dose spectral images achieved similar SNR and CNR compared to full contrast dose conventional images. Taken together, our results suggest that the use of SDCT imaging may facilitate the routine use of low contrast dose as part of pre-TAVR imaging.

Rationale and Objectives: Iodine quantification (IQ) and virtual noncontrast (VNC) images produced by dual-energy CT (DECT) can be used for various clinical applications. We investigate the performance of dual-layer DECT (DLDECT) in different phantom sizes and varying radiation doses and tube voltages, including a low-dose pediatric setting. Materials and Methods: Three phantom sizes (simulating a 10-year-old child, an average, and a large-sized adult) were scanned with iodine solution inserts with concentrations ranging 0-32 mg/ml, using the DLDECT. Each phantom size was scanned with CTDIvol 2-15 mGy at 120 and 140 kVp. The smallest phantom underwent additional scans with CTDIvol 0.9-1.8 mGy. All scans were repeated 3 times. Each iodine insert was analyzed using VNC and IQ images for accuracy and precision, by comparison to known values. Results: For scans from 2 to 15 mGy mean VNC attenuation and IQ error in the iodine inserts in the small, medium, and large phantoms was 1.2 HU +/- 3.2, -1.2 HU +/- 14.9, 2.6 HU +/- 23.6; and +0.1 mg/cc +/- 0.4, -0.9 mg/cc +/- 0.9, and -1.8 mg/cc +/- 1.8, respectively. In this dose range, there were no significant differences (p >= 0.05) in mean VNC attenuation or IQ accuracy in each phantom size, while IQ was significantly less precise in the small phantom at 2 mGy and 10 mGy (p < 0.05). Scans with CTDIvol 0.9-1.8 mGy in the small phantom showed a limited, but statistically significantly lower VNC attenuation precision and IQ accuracy (-0.5 HU +/- 5.3 and -0.3 mg/cc +/- 0.5, respectively) compared to higher dose scans in the same phantom size. Conclusion: Performance of iodine quantification and subtraction by VNC images in DLDECT is largely dose independent, with the primary factor being patient size. Low-dose pediatric scan protocols have a significant, but limited impact on IQ and VNC attenuation values.

Objective The objective of this study was to investigate the quantitative and qualitative effects of virtual monoenergetic images (VMIs) by spectral detector computed tomography (SDCT) on metal artifacts in routine examinations. Methods Fifty-nine patients with metal artifacts (caused by pacemakers, ports, screws, or prosthetic joints) affecting muscular tissue in the chest and/or abdomen were scanned using SDCT. Attenuation values around the metallic device were compared with contralateral unaffected values, for conventional images and 80 to 200 keV VMIs. In addition, general image quality and artifact intensity were rated by 2 readers. Results The VMIs significantly decreased metal artifact intensity in all patients (P < 0.05). In 39 patients (66.1%), the attenuation values of the artifact and the unaffected area on the optimal keV level were very similar (≤5 Hounsfield unit difference). Qualitative analysis showed that high VMIs significantly improved artifact intensity, with best scores at 140 keV. Conclusions High monoenergetic images of SDCT significantly reduce metal artifacts, with optimal assessment at 140 keV.

Introduction: Bone mineral density (BMD) analysis by Dual-Energy x-ray Absorptiometry (DXA) can have some false negatives due to overlapping structures in the projections. Spectral Detector CT (SDCT) can overcome these limitations by providing volumetric information. We investigated its performance for BMD assessment and compared it to DXA and phantomless volumetric bone mineral density (PLvBMD), the latter known to systematically underestimate BMD. DXA is the current standard for BMD assessment, while PLvBMD is an established alternative for opportunistic BMD analysis using CT. Similarly to PLvBMD, spectral data could allow BMD screening opportunistically, without additional phantom calibration. Methodology: Ten concentrations of dipotassium phosphate (K2HPO4) ranging from 0 to 600 mg/ml, in an acrylic phantom were scanned using SDCT in four different, clinically-relevant scan conditions. Images were processed to estimate the K2HPO4 concentrations. A model representing a human lumbar spine (European Spine Phantom) was scanned and used for calibration via linear regression analysis. After calibration, our method was retrospectively applied to abdominal SDCT scans of 20 patients for BMD assessment, who also had PLvBMD and DXA. Performance of PLvBMD, DXA and our SDCT method were compared by sensitivity, specificity, negative predictive value and positive predictive value for decreased BMD. Results: There was excellent correlation (R2 >0.99, p < 0.01) between true and measured K2HPO4 concentrations for all scan conditions. Overall mean measurement error ranged from −11.5 ± 4.7 mg/ml (−2.8 ± 6.0%) to −12.3 ± 6.3 mg/ml (−4.8 ± 3.0%) depending on scan conditions. Using DXA as a reference standard, sensitivity/specificity for detecting decreased BMD in the scanned patients were 100%/73% using SDCT, 100%/40% using PLvBMD provided T-scores, and 90–100%/40–53% using PLvBMD hydroxyapatite density classifications, respectively. Conclusions: Our results show excellent sensitivity and high specificity of SDCT for detecting decreased BMD, demonstrating clinical feasibility. Further validation in prospective clinical trials will be required.

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρe), mean excitation energy (Ix), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

OBJECTIVE:The aim of this study was to investigate the quantitative and qualitative effects of virtual monoenergetic images (VMI) by spectral detector computed tomography (SDCT) on calcium blooming in coronary computed tomography angiography. METHODS:Coronary computed tomography angiography using SDCT was performed on 42 patients with coronary artery calcifications. Stenosis grading by diameter and area of calcified plaques and free lumen using VMI from 70 to 140 keV was performed and compared with measurements by conventional images. In addition, interobserver reliability and subjective image quality were assessed by 2 experienced readers. RESULTS:A total of 61 coronary arteries were evaluated. Stenosis grading by diameter and area showed significant incremental decrease, from 48.86% to 22.82% and from 41.18% to 11.33%, respectively, with increasing VMI (P < 0.05). Interobserver reliability was excellent (intraclass correlation coefficient >0.99). Overall image quality was best at 80 keV. CONCLUSIONS:Calcium blooming significantly decreases at higher monoenergetic levels from SDCT, increasing luminal dimensions and decreasing stenotic grading, with best overall subjective image quality using 80-keV VMI.

Background: Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Resonance Imaging (MRI) Guided Adaptive Radiation Therapy (MR-ART). To address this, we propose a new artificial intelligence-based, auto-contouring method for abdominal MR-ART modeled after human brain cognition for manual contouring. Methods/Materials: Our algorithm is based on two types of information flow, i.e. top-down and bottom-up. Top down information is derived from simulation MR images. It grossly delineates the object based on its high-level information class by transferring the initial planning contours onto daily images. Bottom-up information is derived from pixel data by a supervised, self-adaptive, active learning based support vector machine. It uses low-level pixel features, such as intensity and location, to distinguish each target boundary from the background. The final result is obtained by fusing top-down and bottom-up outputs in a unified framework through artificial intelligence fusion. For evaluation, we used a dataset of four patients with locally advanced pancreatic cancer treated with MR-ART using a clinical system (MRIdian, Viewray, Oakwood Village, OH, USA). Each set included the simulation MRI and onboard T1 MRI corresponding to a randomly selected treatment session. Each MRI had 144 axial slices of 266 x 266 pixels. Using the Dice Similarity Index (DSI) and the Hausdorff Distance Index (HDI), we compared the manual and automated contours for the liver, left and right kidneys, and the spinal cord. Results: The average auto-segmentation time was two minutes per set. Visually, the automatic and manual contours were similar. Fused results achieved better accuracy than either the bottom-up or top-down method alone. The DSI values were above 0.86. The spinal canal contours yielded a low HDI value. Conclusion: With a DSI significantly higher than the usually reported 0.7, our novel algorithm yields a high segmentation accuracy. To our knowledge, this is the first fully automated contouring approach using T1 MRI images for adaptive radiotherapy.

BACKGROUND AND PURPOSE: Conventional CT often cannot distinguish hemorrhage from iodine extravasation following reperfusion therapy for acute ischemic stroke. We investigated the potential of spectral detector CT in differentiating these lesions. MATERIALS AND METHODS: Centrifuged blood with increasing hematocrit (5%-85%) was used to model hemorrhage. Pure blood, blood-iodine mixtures (75/25, 50/50, and 25/75 ratios), and iodine solutions (0-14 mg I/mL) were scanned in a phantom with attenuation ranging from 12 to 75 HU on conventional imaging. Conventional and virtual noncontrast attenuation was compared and investigated for correlation with calculation of relative virtual noncontrast attenuation. Values for all investigated categories were compared using the Mann-Whitney U test. Sensitivity and specificity of virtual noncontrast, relative virtual noncontrast, conventional CT attenuation, and iodine quantification for hemorrhage detection were determined with receiver operating characteristic analysis. RESULTS: Conventional image attenuation was not significantly different among all samples containing blood (P > .05), while virtual noncontrast attenuation showed a significant decrease with a decreasing blood component (P < .01) in all blood-iodine mixtures. Relative virtual noncontrast values were significantly different among all investigated categories (P < .01), with correct hemorrhagic component size estimation for all categories within a 95% confidence interval. Areas under the curve for hemorrhage detection were 0.97, 0.87, 0.29, and 0.16 for virtual noncontrast, relative virtual noncontrast, conventional CT attenuation, and iodine quantification, respectively. A 10-HU virtual noncontrast, 20-HU virtual noncontrast, 40% relative virtual noncontrast, and combined 10-HU virtual noncontrast and 40% relative virtual noncontrast attenuation threshold had a sensitivity/specificity for detecting hemorrhage of 100%/23%, 89%/95%, 100%/82%, and 100%/100%, respectively. CONCLUSIONS: Spectral detector CT can accurately differentiate blood from iodinated contrast in a phantom setting.

Objective This study aimed to evaluate image quality (IQ) of virtual monoenergetic images (VMIs) from novel spectral detector computed tomography angiography of the pulmonary arteries and to identify appropriate window settings for each kiloelectron volt level. Materials Forty consecutive patients were included in this institutional review board–approved, Health Insurance Portability and Accountability Act–compliant study. Signal- and contrast-to-noise ratios were calculated within the pulmonary trunk, and pulmonary/lobar/segmental arteries were calculated. The IQ and diagnostic certainty were rated by 2 radiologists on 5-point scales. In addition, they recorded appropriate window settings (center/width) that were linearly modeled against attenuation within the pulmonary trunk to generate generable results. Results Signal- and contrast-to-noise ratios, IQ, and diagnostic certainty are significantly increased in low–kiloelectron volt VMIs (≤60 keV). Interrater agreement was excellent (ĸ = 0.89). We developed 2 linear models (R 2 : 0.91–0.97 and R 2 : 0.43–0.91, respectively, P ≤ 0.01), that suggest appropriate window settings. Conclusions The VMIs from spectral detector computed tomography improve objective and subjective IQ in angiography of the pulmonary arteries, if window settings are adjusted; they can be automatically estimated using reported linear models.

To evaluate the image quality of routine diagnostic images generated from a novel detector-based spectral detector CT (SDCT) and compare it with CT images obtained from a conventional scanner with an energy-integrating detector (Brilliance iCT), Routine diagnostic (conventional/polyenergetic) images are non-material-specific images that resemble single-energy images obtained at the same radiation,

Early detection of residual tumour and local tumour progression (LTP) after radiofrequency (RF) ablation is crucial in the decision whether or not to re-ablate. In general, standard contrast-enhanced computed tomography (CT) is used to evaluate the technique effectiveness; however, it is difficult to differentiate post-treatment changes from residual tumour. Dual-energy CT (DECT) is a relatively new technique that enables more specific tissue characterisation of iodine-enhanced structures because of the isolation of iodine in the imaging data. Necrotic post-ablation zones can be depicted as avascular regions by DECT on greyscale- and colour-coded iodine images. Synthesised monochromatic images from dual-energy CT with spectral analysis can be used to select the optimal keV to achieve the highest contrast-to-noise ratio between tissues. This facilitates outlining the interface between the ablation zone and surrounding tissue. Post-processing of DECT data can lead to an improved characterisation and delineation of benign post-ablation changes from LTP. Radiologists need to be familiar with typical post-ablation image interpretations when using DECT techniques. Here, we review the spectrum of changes after RF ablation of liver, kidney, and lung lesions using single-source DECT imaging, with the emphasis on the additional information obtained and pitfalls encountered with this relatively new technique. Teaching Points •Technical success of RF ablation means complete destruction of the tumour. •Assessment of residual tumour on contrast-enhanced CT is hindered by post-ablative changes. •DECT improves material differentiation and may improve focal lesion characterisation. •Iodine maps delineate the treated area from the surrounding parenchyma well.