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The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commercially available and replace conventional filtered back projection. Since then, this technique has caused a true hype in the field of radiology. Within a few years, all major CT vendors introduced iterative reconstruction algorithms for clinical routine, which evolved rapidly into increasingly advanced reconstruction algorithms. The complexity of algorithms ranges from hybrid-, model-based to fully iterative algorithms. As a result, the number of scientific publications on this topic has skyrocketed over the last decade. But what exactly has this technology brought us so far? And what can we expect from future hardware as well as software developments, such as photon-counting CT and artificial intelligence? This paper will try answer those questions by taking a concise look at the overall evolution of CT image reconstruction and its clinical implementations. Subsequently, we will give a prospect towards future developments in this domain.Key Points• Advanced CT reconstruction methods are indispensable in the current clinical setting.• IR is essential for photon-counting CT, phase-contrast CT, and dark-field CT.• Artificial intelligence will potentially further increase the performance of reconstruction methods.

ObjectivesEvaluation of imaging performance across dual-energy CT (DECT) platforms, including dual-layer CT (DLCT), rapid-kVp-switching CT (KVSCT) and dual-source CT (DSCT).MethodsA semi-anthropomorphic abdomen phantom was imaged on these DECT systems. Scans were repeated three times for CTDIvol levels of 10 mGy, 20 mGy, 30 mGy and different fat-simulating extension rings. Over the available range of virtual-monoenergetic images (VMI), noise as well as quantitative accuracy of hounsfield units (HU) and iodine concentrations were evaluated.ResultsFor all VMI levels, HU values could be determined with high accuracy compared to theoretical values. For KVSCT and DSCT, a noise increase was observed towards lower VMI levels. A patient-size dependent increase in the uncertainty of quantitative iodine concentrations is observed for all platforms. For a medium patient size the iodine concentration root-mean-square deviation at 20 mGy is 0.17 mg/ml (DLCT), 0.30 mg/ml (KVSCT) and 0.77mg/ml (DSCT).ConclusionNoticeable performance differences are observed between investigated DECT systems. Iodine concentrations and VMI HUs are accurately determined across all DECT systems. KVSCT and DLCT deliver slightly more accurate iodine concentration values than DSCT for investigated scenarios. In DLCT, low-noise and high-image contrast at low VMI levels may help to increase diagnostic information in abdominal CT.Key Points• Current dual-energy CT platforms provide accurate, reliable quantitative information.• Dual-energy CT cross-platform evaluation revealed noticeable performance differences between different systems.• Dual-layer CT offers constant noise levels over the complete energy range.

Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The harmonization tool ComBat is a promising solution but is limited by inability to harmonize multimodal distributions, unknown imaging parameters, and multiple imaging parameters. In this study, we propose two methods for addressing these limitations. We propose a sequential method that allows for harmonization of radiomic features by multiple imaging parameters (Nested ComBat). We also employ a Gaussian Mixture Model (GMM)-based method (GMM ComBat) where scans are split into groupings based on the shape of the distribution used for harmonization as a batch effect and subsequent harmonization by a known imaging parameter. These two methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography datasets. We found that Nested ComBat exhibited similar performance to standard ComBat in reducing the percentage of features with statistically significant differences in distribution attributable to imaging parameters. GMM ComBat improved harmonization performance over standard ComBat (− 11%, − 10% for Lung3/CAPTK, Lung3/PyRadiomics harmonizing by kernel resolution). Features harmonized with a variant of the Nested method and the GMM split method demonstrated similar c-statistics and Kaplan–Meier curves when used in survival analyses.

Objective Evaluation of image characteristics at ultra-low radiation dose levels of a first-generation dual-source photon-counting computed tomography (PCCT) compared to a dual-source dual-energy CT (DECT) scanner. Methods A multi-energy CT phantom was imaged with and without an extension ring on both scanners over a range of radiation dose levels (CTDI vol 0.4–15.0 mGy). Scans were performed in different modes of acquisition for PCCT with 120 kVp and DECT with 70/Sn150 kVp and 100/Sn150 kVp. Various tissue inserts were used to characterize the precision and repeatability of Hounsfield units (HUs) on virtual mono-energetic images between 40 and 190 keV. Image noise was additionally investigated at an ultra-low radiation dose to illustrate PCCT’s ability to remove electronic background noise. Results Our results demonstrate the high precision of HU measurements for a wide range of inserts and radiation exposure levels with PCCT. We report high performance for both scanners across a wide range of radiation exposure levels, with PCCT outperforming at low exposures compared to DECT. PCCT scans at the lowest radiation exposures illustrate significant reduction in electronic background noise, with a mean percent reduction of 74% ( p value ~ 10 −8 ) compared to DECT 70/Sn150 kVp and 60% ( p value ~ 10 −6 ) compared to DECT 100/Sn150 kVp. Conclusions This paper reports the first experiences with a clinical dual-source PCCT. PCCT provides reliable HUs without disruption from electronic background noise for a wide range of dose values. Diagnostic benefits are not only for quantification at an ultra-low dose but also for imaging of obese patients. Key Points PCCT scanners provide precise and reliable Hounsfield units at ultra-low dose levels. The influence of electronic background noise can be removed at ultra-low-dose acquisitions with PCCT. Both spectral platforms have high performance along a wide range of radiation exposure levels, with PCCT outperforming at low radiation exposures.

The recent progress in photon-counting detector technology using high-Z semiconductor sensors provides new possibilities for spectral x-ray imaging. The benefits of the approach to extract spectral information directly from measurements in the projection domain are very advantageous for material science studies with x-rays as polychromatic artifacts like beam-hardening are handled properly. Since related methods require accurate knowledge of all energy-dependent system parameters, we utilize an adapted semi-empirical model, which relies on a simple calibration procedure. The method enables a projection-based decomposition of photon-counting raw-data into basis material projections. The objective of this paper is to investigate the method's performance applied to x-ray micro-CT with special focus on applications in material science and non-destructive testing. Projection-based dual-energy micro-CT is shown to be of good quantitative accuracy regarding material properties such as electron densities and effective atomic numbers. Furthermore, we show that the proposed approach strongly reduces beam-hardening artifacts and improves image contrast at constant measurement time.

Radiomic approaches in precision medicine are promising, but variation associated with image acquisition factors can result in severe biases and low generalizability. Multicenter datasets used in these studies are often heterogeneous in multiple imaging parameters and/or have missing information, resulting in multimodal radiomic feature distributions. ComBat is a promising harmonization tool, but it only harmonizes by single/known variables and assumes standardized input data are normally distributed. We propose a procedure that sequentially harmonizes for multiple batch effects in an optimized order, called OPNested ComBat. Furthermore, we propose to address bimodality by employing a Gaussian Mixture Model (GMM) grouping considered as either a batch variable (OPNested + GMM) or as a protected clinical covariate (OPNested − GMM). Methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography (CT) datasets. We found that OPNested ComBat improved harmonization performance over standard ComBat. OPNested + GMM ComBat exhibited the best harmonization performance but the lowest predictive performance, while OPNested − GMM ComBat showed poorer harmonization performance, but the highest predictive performance. Our findings emphasize that improved harmonization performance is no guarantee of improved predictive performance, and that these methods show promise for superior standardization of datasets heterogeneous in multiple or unknown imaging parameters and greater generalizability.

ObjectiveTo investigate the in vivo applicability of non-contrast-enhanced hydroxyapatite (HA)-specific bone mineral density (BMD) measurements based on dual-layer CT (DLCT).MethodsA spine phantom containing three artificial vertebral bodies with known HA densities was measured to obtain spectral data using DLCT and quantitative CT (QCT), simulating different patient positions and grades of obesity. BMD was calculated from virtual monoenergetic images at 50 and 200 keV. HA-specific BMD values of 174 vertebrae in 33 patients (66 ± 18 years; 33% women) were determined in non-contrast routine DLCT and compared with corresponding QCT-based BMD values.ResultsExamining the phantom, HA-specific BMD measurements were on a par with QCT measurements. In vivo measurements revealed strong correlations between DLCT and QCT (r = 0.987 [95% confidence interval, 0.963–1.000]; p < 0.001) and substantial agreement in a Bland–Altman plot.ConclusionDLCT-based HA-specific BMD measurements were comparable with QCT measurements in in vivo analyses. This suggests that opportunistic DLCT-based BMD measurements are an alternative to QCT, without requiring phantoms and specific protocols.Key Points• DLCT-based hydroxyapatite-specific BMD measurements show a substantial agreement with QCT-based BMD measurements in vivo.• DLCT-based hydroxyapatite-specific measurements are on a par with QCT in spine phantom measurements.• Opportunistic DLCT-based BMD measurements may be a feasible alternative for QCT, without requiring dedicated examination protocols or a phantom.

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that possess accurate densities and exhibit visually realistic image textures. These qualities are crucial for evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized calcium-doped filament to increase the Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility, and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in visual texture and contrast. Micro-CT analysis revealed minimal variations between prints, with an overall deviation of ± 0.8% in filament line spacing and ± 0.022 mm in line width. Measured differences between patient and phantom were less than 12 HU for soft tissue and 15 HU for bone marrow, and 514 HU for cortical bone. The calcium-doped filament accurately represented bony tissue structures across different X-ray energies in spectral CT (RMSE ranging from ± 3 to ± 28 HU, compared to 400 mg/ml hydroxyapatite). In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

ObjectivesAfter endovascular aortic repair (EVAR), discrimination of endoleaks and intra-aneurysmatic calcifications within the aneurysm often requires multiphase computed tomography (CT). Spectral photon-counting CT (SPCCT) in combination with a two-contrast agent injection protocol may provide reliable detection of endoleaks with a single CT acquisition.MethodsTo evaluate the feasibility of SPCCT, the stent-lined compartment of an abdominal aortic aneurysm phantom was filled with a mixture of iodine and gadolinium mimicking enhanced blood. To represent endoleaks of different flow rates, the adjacent compartments contained either one of the contrast agents or calcium chloride to mimic intra-aneurysmatic calcifications. After data acquisition with a SPCCT prototype scanner with multi-energy bins, material decomposition was performed to generate iodine, gadolinium and calcium maps.ResultsIn a conventional CT slice, Hounsfield units (HU) of the compartments were similar ranging from 147 to 168 HU. Material-specific maps differentiate the distributions within the compartments filled with iodine, gadolinium or calcium.ConclusionSPCCT may replace multiphase CT to detect endoleaks without sacrificing diagnostic accuracy. It is a unique feature of our method to capture endoleak dynamics and allow reliable distinction from intra-aneurysmatic calcifications in a single scan, thereby enabling a significant reduction of radiation exposure.Key Points• SPCCT might enable advanced endoleak detection.• Material maps derived from SPCCT can differentiate iodine, gadolinium and calcium.• SPCCT may potentially reduce radiation burden for EVAR patients under post-interventional surveillance.

Modern non-invasive evaluation of Coronary Artery Disease (CAD) requires non-contrast low dose Computed Tomography (CT) imaging for determination of Calcium Scoring (CACS) and contrast-enhanced imaging for evaluation of vascular stenosis. Several methods for calculation of CACS from contrast-enhanced images have been proposed before. The main principle for that is generation of virtual non-contrast images by iodine subtraction from a contrast-enhanced spectral CT dataset. However, those techniques have some limitations: Dual-Source CT imaging can lead to increased radiation exposure, and switching of the tube voltage (rapid kVp switching) can be associated with slower rotation speed of the gantry and is thus prone to motion artefacts that are especially critical in cardiac imaging. Both techniques cannot simultaneously acquire spectral data. A novel technique to overcome these difficulties is spectral imaging with a dual-layer detector. After absorption of the lower energetic photons in the first layer, the second layer detects a hardened spectrum of the emitted radiation resulting in registration of two different energy spectra at the same time. The objective of the present investigation was to evaluate the accuracy of virtual non-contrast CACS computed from spectral data in comparison to standard non-contrast imaging. We consecutively investigated 20 patients referred to Coronary Computed Tomography Angiography (CCTA) with suspicion of CAD using a Dual-Layer spectral CT system (IQon; Philips Healthcare, The Netherlands). CACS was calculated from both, real- and virtual non-contrast images by certified software for medical use. Correlation analyses for real- and virtual non-contrast images and agreement evaluation with Bland-Altman-Plots were performed. Mean patient age was 57.7 ± 14 years (n = 20). 13 patients (65%) were male. Inter-quartile-range of clinical CACS was 0-448, the mean was 334. Correlation of CACS from real- and virtual non-contrast images was very high (0.94); p < 0.0001. The slope was 2.3 indicating that values from virtual non-contrast images are approximately half of the results obtained from real non-contrast data. Visual analysis of Bland-Altman-Plot shows good accordance of both methods when results from virtual non-contrast data are multiplied by the slope of the logistic regression model (2.3). The acquired power of this results is 0.99. Determination of Calcium Score from contrast enhanced CCTA using spectral imaging with a dual-layer detector is feasible and shows good agreement with the conventional technique when a proportionality factor is applied. The observed difference between both methods is due to an underestimation of plaque volume, and-to an even greater extend -an underestimation of plaque density with the virtual non-contrast approach. Our data suggest that radiation exposure can be reduced through omitting additional native scans for patients referred to CCTA when using a dual-layer spectral system without the usual limitations of dual energy analysis.