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Abstract This study aimed to evaluate the impact of contrast media application on CT attenuation of the bone using a novel calcium-only imaging technique (VCa) from dual-layer spectral detector CT (DLCT), which enables CT-based bone mineral density measurement unimpeded by soft tissue components. For this, true non-contrast (TNC) and venous phase images (VP) of n = 97 patients were acquired. CT attenuation of the first lumbar vertebra (L1) was measured in TNC-VCa, VP-VCa, and in virtual non-contrast images (VNC). CT attenuation was significantly higher in VP-VCa than in TNC-VCa (p

AbstractObjectives To compare metal artifacts and evaluation of metal artifact reduction algorithms during probe positioning in computed tomography (CT)-guided microwave ablation (MWA), cryoablation (CRYO), and radiofrequency ablation (RFA).Materials and methods Using CT guidance, individual MWA, CRYO, and RFA ablation probes were placed into the livers of 15 pigs. CT imaging was then performed to determine the probe’s position within the test subject’s liver. Filtered back projection (B30f) and iterative reconstructions (I30-1) were both used with and without dedicated iterative metal artifact reduction (iMAR) to generate images from the initial data sets. Semi-automatic segmentation-based quantitative evaluation was conducted to estimate artifact percentage within the liver, while qualitative evaluation of metal artifact extent and overall image quality was performed by two observers using a 5-point Likert scale: 1-none, 2-mild, 3-moderate, 4-severe, 5-non-diagnostic.Results Among MWA, RFA, and CRYO, compared with non-iMAR in B30f reconstruction, the largest extent of artifact volume percentages were observed for CRYO (11.5–17.9%), followed by MWA (4.7–6.6%) and lastly in RFA (5.5–6.2%). iMAR significantly reduces metal artifacts for CRYO and MWA quantitatively (p = 0.0020; p = 0.0036, respectively) and qualitatively (p = 0.0001, p = 0.0005), but not for RFA. No significant reduction in metal artifact percentage was seen after applying iterative reconstructions (p > 0.05). Noise, contrast-to-noise-ratio, or overall image quality did not differ between probe types, irrespective of the application of iterative reconstruction and iMAR.Conclusion A dedicated metal artifact algorithm may decrease metal artifacts and improves image quality significantly for MWA and CRYO probes. Their application alongside with dedicated metal artifact algorithm should be considered during CT-guided positioning.

OBJECTIVES:To compare image quality and metal artifact reduction between virtual monochromatic spectral imaging (VMSI), linearly blended dual-energy (DE) and single-energy (SE) images, each with and without dedicated iterative metal artifact reduction (iMAR) for CT-guided biopsy. MATERIALS AND METHODS:A biopsy trocar was positioned in the liver of six pigs. DE (Sn140/100kVp) and SE (120kVp/200mAs) acquisitions were performed with equivalent dose. From dual-energy datasets DE Q30-3 images and VMSI between 40-180 keV in steps of 20 keV were generated. From SE datasets I30-3 images were reconstructed. All images were reconstructed with and without iMAR. Objective image quality was analyzed applying density measurements at standardized positions (e.g. trocar tip and liver parenchyma adjacent to the trocar tip) and semi-automated threshold based segmentation. Subjective image quality was performed using semi-quantitative scores. Analyses were performed by two observers. RESULTS:At the trocar tip quantitative image analysis revealed significant difference in CT numbers between reconstructions with iMAR compared to reconstructions without iMAR for VMSI at lower keV levels (80 and 100 keV; p = 0.03) and DE (p = 0.03). For liver parenchyma CT numbers were significantly higher in VMSI at high keV compared to low keV (p≤0.01). VMSI at high keV also showed higher CT numbers compared to DE and SE images, though not the level of statistical significance. The best signal-to-noise ratio for VMSI was at 80 keV and comparable to DE and SE. Noise was lowest at 80 keV and lower than in DE and SE. Subjective image quality was best with VMSI at 80 keV regardless of the application of iMAR. iMAR significantly improved image quality at levels of 140 keV and 160 keV. Interreader-agreement was good for quantitative and qualitative analysis. CONCLUSION:iMAR improved image quality in all settings. VMSI with iMAR provided metal artifact reduction and better image quality at 80 keV and thus could improve the accurate positioning in CT-guided needle biopsy. In comparison, DE imaging did not improve image quality compared to SE.

This study aimed to evaluate the impact of contrast media application on CT attenuation of the bone using a novel calcium-only imaging technique (VCa) from dual-layer spectral detector CT (DLCT), which enables CT-based bone mineral density measurement unimpeded by soft tissue components. For this, true non-contrast (TNC) and venous phase images (VP) of n = 97 patients were acquired. CT attenuation of the first lumbar vertebra (L1) was measured in TNC-VCa, VP-VCa, and in virtual non-contrast images (VNC). CT attenuation was significantly higher in VP-VCa than in TNC-VCa (p < 0.001), although regression analyses revealed a strong linear association between these measures (R 2  = 0.84). A statistical model for the prediction of TNC-VCa CT attenuation was established (TNC-VCa[HU] = - 6.81 + 0.87 × VP-VCa[HU]-0.55 × body weight[kg]) and yielded good agreement between observed and predicted values. Furthermore, a L1 CT attenuation threshold of 293 HU in VP-VCa showed a sensitivity of 90% and a specificity of 96% for detecting osteoporosis. The application of contrast media leads to an overestimation of L1 CT attenuation in VCa. However, CT attenuation values from VP-VCa can be used within CT-based opportunistic osteoporosis screening eighter by applying a separate threshold of 293 HU or by converting measured data to TNC-VCa CT attenuation with the given regression equation., (© 2024. The Author(s).)

Purpose: To evaluate the prediction of vertebral fractures in plasma cell dyscrasias using dual-layer CT (DLCT) with quantitative assessment of conventional CT image data (CI), calcium suppressed image data (CaSupp), and calculation of virtual calcium-only (VCa) image data., Material and Methods: Patients ( n = 81) with the diagnosis of a plasma cell dyscrasia and whole-body DLCT at the time of diagnosis and follow-up were retrospectively enrolled. CI, CaSupp25, and CaSupp100 were quantitatively analyzed using regions of interest in the lumbar vertebral bodies and fractured vertebral bodies on baseline or follow-up imaging. VCa were calculated by subtraction (CaSupp100-CaSupp25), delineating bone only. Logistic regression analyses were performed to assess the possibility of imminent spine fractures., Results: In 24 patients, new vertebral fractures were observed in the follow-up imaging. The possibility of new vertebral fractures was significant for baseline assessment of CT numbers in CI, CaSupp25, and VCa ( p = 0.01, respectively), with a higher risk for new fractures in the case of lower CT numbers in CI (Odds ratio = [0.969; 0.994]) and VCa (Odds ratio = [0.978; 0.995]) and in the case of higher CT numbers in CaSupp 25 (Odds ratio 1.015 [1.006; 1.026]). Direct model comparisons implied that CT numbers in CaSupp 25 and VCa might show better fracture prediction than those in CI (R 2 = 0.18 both vs. 0.15; AICc = 91.95, 91.79 vs. 93.62), suggesting cut-off values for CI at 103 HU (sensitivity: 54.2%; specificity: 82.5; AUC: 0.69), for VCa at 129 HU (sensitivity: 41.7%; specificity: 94.7; AUC: 0.72)., Conclusions: Quantitative assessment with CaSupp and calculation of VCa is feasible to predict the vertebral fracture risk in MM patients. DLCT may prove useful in detecting imminent fractures.

Background: This study provides a quantitative meta-analysis of pancreatic CT perfusion studies, investigating choice of study parameters, ability for quantitative discrimination of pancreatic diseases, and influence of acquisition and reconstruction parameters on reported results., Methods: Based on a PubMed search with key terms 'pancreas' or 'pancreatic,' 'dynamic' or 'perfusion,' and 'computed tomography' or 'CT,' 491 articles published between 1982 and 2020 were screened for inclusion in the study. Inclusion criteria were: reported original data, human subjects, five or more datasets, measurements of pancreas or pancreatic pathologies, and reported quantitative perfusion parameters. Study parameters and reported quantitative measurements were extracted, and heterogeneity of study parameters and trends over time are analyzed. Pooled data were tested with weighted ANOVA and ANCOVA models for differences in perfusion results between normal pancreas, pancreatitis, PDAC (pancreatic ductal adenocarcinoma), and non-PDAC (e.g., neuroendocrine tumors, insulinomas) and based on study parameters., Results: Reported acquisition parameters were heterogeneous, except for contrast agent amount and injection rate. Tube potential and slice thickness decreased, whereas tube current time product and scan coverage increased over time. Blood flow and blood volume showed significant differences between pathologies (both p < 0.001), unlike permeability (p = 0.11). Study parameters showed a significant effect on reported quantitative measurements (p < 0.05)., Conclusions: Significant differences in perfusion measurements between pathologies could be shown for pooled data despite observed heterogeneity in study parameters. Statistical analysis indicates most influential parameters for future optimization and standardization of acquisition protocols., Critical Relevance Statement: Quantitative CT perfusion enables differentiation of pancreatic pathologies despite the heterogeneity of study parameters in current clinical practice., (© 2023. The Author(s).)

For implementation, performance evaluation and timing optimization of CT perfusion first pass analysis (FPA) by correlation with maximum slope model (MSM) in pancreatic adenocarcinoma, dynamic CT perfusion acquisitions of 34 time-points were performed in 16 pancreatic adenocarcinoma patients. Regions of interest were marked in both parenchyma and carcinoma. FPA, a low radiation exposure CT perfusion technique, was implemented. Blood flow (BF) perfusion maps were calculated using FPA and MSM. Pearson's correlation between FPA and MSM was calculated at each evaluated time-point to determine optimum timing for FPA. Differences in BF between parenchyma and carcinoma were calculated. Average BF for MSM was 106.8 ± 41.5 ml/100 ml/min in parenchyma and 42.0 ± 24.8 ml/100 ml/min in carcinoma, respectively. For FPA, values ranged from 85.6 ± 37.5 ml/100 ml/min to 117.7 ± 44.5 ml/100 ml/min in parenchyma and from 27.3 ± 18.8 ml/100 ml/min to 39.5 ± 26.6 ml/100 ml/min in carcinoma, depending on acquisition timing. A significant difference (p value < 0.0001) between carcinoma and parenchyma was observed at all acquisition times based on FPA measurements. FPA shows high correlation with MSM (r > 0.90) and 94% reduction in the radiation dose compared to MSM. CT perfusion FPA, where the first scan is obtained after the arterial input function exceeds a threshold of 120 HU, followed by a second scan after 15.5-20.0 s, could be used as a potential imaging biomarker with low radiation exposure for diagnosing and evaluating pancreatic carcinoma in clinical practice, showing high correlation with MSM and the ability to differentiate between parenchyma and carcinoma., (© 2023. The Author(s).)

Introduction: By using bolus tracking with an appropriate acquisition delay dual-energy computed tomography (DECT) iodine maps might serve as a replacement of CT perfusion maps at reduced radiation exposure. This study aimed to evaluate the optimal acquisition delays of DECT for the replacement of parameter maps calculated with the Patlak model in pancreatic adenocarcinoma by corresponding iodine maps., Materials and Methods: Dual-source dynamic DECT acquisitions at 80 kV p /Sn140 kV p of 14 patients with pancreatic carcinoma were used to calculate CT perfusion maps of blood volume and permeability with the Patlak model. DECT iodine maps were generated from individual DECT acquisitions, matching acquisition times relative to prior bolus-triggered three-phase CT acquisitions for investigating different acquisition delays. Correlation between perfusion parameters and iodine concentrations was determined for acquisition delays between -6 s and 33 s., Results: Correlation between iodine concentrations and perfusion parameters ranged from -0.05 to 0.63 for blood volume and from -0.05 to 0.71 for permeability, depending on potential trigger delay. The correlation was significant for potential acquisition delays above 1.5 s for blood volume and above 9.0 s for permeability (both p < 0.05). Maximum correlation occurred at an acquisition delay of 15.0 s for blood volume (r = 0.63) and at 25.5 s for permeability (r = 0.71), with significantly lower iodine concentrations in carcinoma (15.0 s: 1.3 ± 0.5 mg/ml; 22.5 s: 1.4 ± 0.7 mg/ml) than in non-neoplastic pancreatic parenchyma (15.0 s: 2.3 ± 0.8 mg/ml; 22.5 s: 2.4 ± 0.6 mg/ml; p < 0.05)., Discussion: In the future, well-timed DECT iodine maps acquired with bolus tracking could provide an alternative to permeability and blood volume maps calculated with the Patlak model., Competing Interests: The authors declare the following conflict of interests: Stephan Skornitzke has ownership interests in investment funds containing stock of healthcare companies. Wolfram Stiller is a member of the CT Advisory Board of Philips Medical Systems. Otherwise, the authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (© 2023 The Authors.)

Objectives: To compare metal artifacts and evaluation of metal artifact reduction algorithms during probe positioning in computed tomography (CT)-guided microwave ablation (MWA), cryoablation (CRYO), and radiofrequency ablation (RFA)., Materials and Methods: Using CT guidance, individual MWA, CRYO, and RFA ablation probes were placed into the livers of 15 pigs. CT imaging was then performed to determine the probe's position within the test subject's liver. Filtered back projection (B30f) and iterative reconstructions (I30-1) were both used with and without dedicated iterative metal artifact reduction (iMAR) to generate images from the initial data sets. Semi-automatic segmentation-based quantitative evaluation was conducted to estimate artifact percentage within the liver, while qualitative evaluation of metal artifact extent and overall image quality was performed by two observers using a 5-point Likert scale: 1-none, 2-mild, 3-moderate, 4-severe, 5-non-diagnostic., Results: Among MWA, RFA, and CRYO, compared with non-iMAR in B30f reconstruction, the largest extent of artifact volume percentages were observed for CRYO (11.5-17.9%), followed by MWA (4.7-6.6%) and lastly in RFA (5.5-6.2%). iMAR significantly reduces metal artifacts for CRYO and MWA quantitatively ( p  = 0.0020; p  = 0.0036, respectively) and qualitatively ( p  = 0.0001, p  = 0.0005), but not for RFA. No significant reduction in metal artifact percentage was seen after applying iterative reconstructions ( p  > 0.05). Noise, contrast-to-noise-ratio, or overall image quality did not differ between probe types, irrespective of the application of iterative reconstruction and iMAR., Conclusion: A dedicated metal artifact algorithm may decrease metal artifacts and improves image quality significantly for MWA and CRYO probes. Their application alongside with dedicated metal artifact algorithm should be considered during CT-guided positioning.

Asynchronous calibration could allow opportunistic screening based on routine CT for early osteoporosis detection. In this phantom study, a bone mineral density (BMD) calibration phantom and multi-energy CT (MECT) phantom were imaged on eight different CT scanners with multiple tube voltages (80-150 kV p ) and image reconstruction settings (e.g. soft/hard kernel). Reference values for asynchronous BMD estimation were calculated from the BMD-phantom and validated with six calcium composite inserts of the MECT-phantom with known ground truth. Relative errors/changes in estimated BMD were calculated and investigated for influence of tube voltage, CT scanner and reconstruction setting. Reference values for 282 acquisitions were determined, resulting in an average relative error between calculated BMD and ground truth of - 9.2% ± 14.0% with a strong correlation (R 2  = 0.99; p < 0.0001). Tube voltage and CT scanner had a significant effect on calculated BMD (p < 0.0001), with relative differences in BMD of 3.8% ± 28.2% when adapting reference values for tube voltage, - 5.6% ± 9.2% for CT scanner and 0.2% ± 0.2% for reconstruction setting, respectively. Differences in BMD were small when using reference values from a different CT scanner of the same model (0.0% ± 1.4%). Asynchronous phantom-based calibration is feasible for opportunistic BMD assessment based on CT images with reference values adapted for tube voltage and CT scanner model., (© 2022. The Author(s).)

Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.

Objectives: To evaluate the prognostic value of fully automatic lung quantification based on spectral computed tomography (CT) and laboratory parameters for combined outcome prediction in COVID-19 pneumonia., Methods: CT images of 53 hospitalized COVID-19 patients including virtual monochromatic reconstructions at 40-140keV were analyzed using a fully automated software system. Quantitative CT (QCT) parameters including mean and percentiles of lung density, fibrosis index (FIBI-700, defined as the percentage of segmented lung voxels ≥-700 HU), quantification of ground-glass opacities and well-aerated lung areas were analyzed. QCT parameters were correlated to laboratory and patient outcome parameters (hospitalization, days on intensive care unit, invasive and non-invasive ventilation)., Results: Best correlations were found for laboratory parameters LDH (r = 0.54), CRP (r = 0.49), Procalcitonin (r = 0.37) and partial pressure of oxygen (r = 0.35) with the QCT parameter 75th percentile of lung density. LDH, Procalcitonin, 75th percentile of lung density and FIBI-700 were the strongest independent predictors of patients' outcome in terms of days of invasive ventilation. The combination of LDH and Procalcitonin with either 75th percentile of lung density or FIBI-700 achieved a r2 of 0.84 and 1.0 as well as an area under the receiver operating characteristic curve (AUC) of 0.99 and 1.0 for the prediction of the need of invasive ventilation., Conclusions: QCT parameters in combination with laboratory parameters could deliver a feasible prognostic tool for the prediction of invasive ventilation in patients with COVID-19 pneumonia., Competing Interests: Dr. Stephan Skornitzke has ownership interests in investment funds containing stock of healthcare companies. Dr. Thuy Duong Do was supported by a grant from the Medical Faculty of the University of Heidelberg. Prof. Dr. Mark O. Wielpütz receives funding from Boehringer Ingelheim and Vertex Pharma, outside the submitted work. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Objectives: To investigate the value of spectral-detector computed tomography (SDCT) parameters for the quantitative differentiation between atelectasis and pneumonia on contrast-enhanced chest CT., Material and Methods: Sixty-three patients, 22 clinically diagnosed with pneumonia and 41 with atelectasis, underwent contrast-enhanced SDCT scans during the venous phase. CT numbers (Hounsfield Units [HU]) were measured on conventional reconstructions (CON 120kVp ) and the iodine concentration (C iodine , [mg/ml]), and effective atomic number (Z eff ) on spectral reconstructions, using region-of-interest (ROI) analysis. Receiver operating characteristics (ROC) and contrast-to-noise ratios (CNRs) were calculated to assess each reconstruction's potential to differentiate between atelectasis and pneumonia., Results: On contrast-enhanced SDCT, the difference between atelectasis and pneumonia was significant on CON 120kVp , C iodine , and Z eff images (p < 0.001). On CON 120kVp images, a threshold of 81 HU achieved a sensitivity of 93 % and a specificity of 95 % for identifying pneumonia, while C iodine and Z eff images reached the same sensitivity but lower specificities of 85 % and 83 %. CON 120kVp images showed significantly higher CNRs between normal lung and atelectasis or pneumonia with 30.63 and 27.69 compared to C iodine images with 3.54 and 1.27 and Z eff images with 4.22 and 7.63 (p < 0.001). None of the parameters could differentiate atelectasis and pneumonia without contrast media., Conclusions: Contrast-enhanced SDCT can differentiate atelectasis and pneumonia based on the spectral parameters C iodine , and Z eff. However, they had no added value compared to CT number measurement on CON 120kVp images. Furthermore, contrast media is still needed for a differentiation based on quantitative SDCT parameters., Competing Interests: The authors declare the following conflict of interests: Stephan Skornitzke has ownership interests in investment funds containing stock of healthcare companies., (© 2021 The Authors.)

Purpose: To evaluate dual-energy CT (DE) and dedicated metal artifact reduction algorithms (iMAR) during CT-guided biopsy in comparison to single-energy CT (SE)., Methods: A trocar was placed in the liver of six pigs. CT acquisitions were performed with SE and dose equivalent DE at four dose levels(1.7-13.5mGy). Iterative reconstructions were performed with and without iMAR. ROIs were placed in four positions e.g. at the trocar tip(TROCAR) and liver parenchyma adjacent to the trocar tip(LIVER-1) by two independent observers for quantitative analysis using CT numbers, noise, SNR and CNR. Qualitative image analysis was performed regarding overall image quality and artifacts generated by iMAR., Results: There were no significant differences in CT numbers between DE and SE at TROCAR and LIVER-1 irrespective of iMAR. iMAR significantly reduced metal artifacts at LIVER-1 for all exposure settings for DE and SE(p = 0.02-0.04), but not at TROCAR. SNR, CNR and noise were comparable for DE and SE. SNR was best for high dose levels of 6.7/13.5mGy. Mean difference in the Blant-Altman analysis was -8.43 to 0.36. Cohen's kappa for qualitative interreader-agreement was 0.901., Conclusions: iMAR independently reduced metal artifacts more effectively and efficiently than CT acquisition in DE at any dose setting and its application is feasible during CT-guided liver biopsy., Competing Interests: The authors have declared that no competing interests exist.

Background/objectives: The aim of this study was to compare intravoxel incoherent motion (IVIM) diffusion weighted (DW) MRI and CT perfusion to assess tumor perfusion of pancreatic ductal adenocarcinoma (PDAC)., Methods: In this prospective study, DW-MRI and CT perfusion were conducted in nineteen patients with PDAC on the day before surgery. IVIM analysis of DW-MRI was performed and the parameters perfusion fraction f, pseudodiffusion coefficient D*, and diffusion coefficient D were extracted for tumors, upstream, and downstream parenchyma. With a deconvolution-based analysis, the CT perfusion parameters blood flow (BF) and blood volume (BV) were estimated for tumors, upstream, and downstream parenchyma. In ten patients, intratumoral microvessel density (MVD tumor ) and microvessel area (MVA tumor ) were analyzed microscopically in resection specimens. Correlation coefficients between IVIM parameters, CT perfusion parameters, and histological microvessel parameters in tumors were calculated. Receiver operating characteristic (ROC) analysis was performed for differentiation of tumors and upstream parenchyma., Results: f tumor significantly positively correlated with BF tumor (r = 0.668, p = 0.002) and BV tumor (r = 0.672, p = 0.002). There were significant positive correlations between f tumor and MVD tumor / MVA tumor (r ≥ 0.770, p ≤ 0.009) as well as between BF tumor and MVD tumor / MVA tumor (r ≥ 0.697, p ≤ 0.025). Correlation coefficients between f tumor and MVD tumor / MVA tumor were not significantly different from correlation coefficients between BF tumor and MVD tumor / MVA tumor (p ≥ 0.400). Moreover, f, BF, BV, and permeability values (PEM) showed excellent performance in distinguishing tumors from upstream parenchyma (area under the ROC curve ≥0.874)., Conclusions: The study shows that IVIM derived f tumor and CT perfusion derived BF tumor similarly reflect vascularity of PDAC and seem to be comparably applicable for the evaluation of tumor perfusion for tumor characterization and as potential quantitative imaging biomarker., Trial Registration: DRKS, DRKS00022227, Registered 26 June 2020, retrospectively registered. https://www.drks.de/drks&#95;web/navigate.do?navigationId=trial . HTML&TRIAL&#95;ID=DRKS00022227.

An evaluation of energy thresholding and acquisition mode for metal artifact reduction in Photon-counting detector CT (PCD-CT) compared to conventional energy-integrating detector CT (EID-CT) was performed. Images of a hip prosthesis phantom placed in a water bath were acquired on a scanner with PCD-CT and EID-CT (tube potentials: 100, 120 and 140 kV p ) and energy thresholds (above 55-75 keV) in Macro and Chess mode. Only high-energy threshold images (HTI) were used. Metal artifacts were quantified by a semi-automated segmentation algorithm, calculating artifact volumes, means and standard deviations of CT numbers. Images of a human cadaver with hip prosthesis were acquired on the PCD-CT in Macro mode as proof-of-concept. Images at 140 kV p showed less metal artifacts than 120 kV p or 100 kV p . HTI (70, 75 keV) had fewer artifacts than low energy thresholds (55, 60, 65 keV). Fewer artifacts were observed in the Macro-HTI (8.9-13.3%) for cortical bone compared to Chess-HTI (9.4-19.1%) and EID-CT (10.7-19.0%) whereas in bone marrow Chess-HTI (19.9-45.1%) showed less artifacts compared to Macro-HTI (21.9-38.3%) and EID-CT (36.4-54.9%). Noise for PCD-CT (56-81 HU) was higher than EID-CT (33-36 HU) irrespective of tube potential. High-energy thresholding could be used for metal artifact reduction in PCD-CT, but further investigation of acquisition modes depending on target structure is required.

Objectives: To systematically evaluate the influence of acquisition settings in conjunction with raw-data based iterative image reconstruction (IR) on lung densitometry based on multi-row detector computed tomography (CT) in an anthropomorphic chest phantom., Materials and Methods: Ten porcine heart-lung explants were mounted in an ex vivo chest phantom shell, six with highly and four with low attenuating chest wall. CT (Somatom Definition Flash, Siemens Healthineers) was performed at 120kVp and 80kVp, each combined with current-time products of 120, 60, 30, and 12mAs, and was reconstructed with filtered back projection (FBP) and IR (Safire, Siemens Healthineers). Mean lung density (LD), air density (AD) and noise were measured by semi-automated region-of interest (ROI) analysis, with 120kVp/120 mAs serving as the standard of reference., Results: Using IR, noise in lung parenchyma was reduced by ~ 31% at high attenuating chest wall and by ~ 22% at low attenuating chest wall compared to FBP, respectively (p<0.05). IR induced changes in the order of ±1 HU to mean absolute LD and AD compared to corresponding FBP reconstructions which were statistically significant (p<0.05)., Conclusions: Densitometry is influenced by acquisition parameters and reconstruction algorithms to a degree that may be clinically negligible. However, in longitudinal studies and clinical research identical protocols and potentially other measures for calibration may be required., Competing Interests: The authors have declared that no competing interest exist.

Objectives: To compare image quality and metal artifact reduction between virtual monochromatic spectral imaging (VMSI), linearly blended dual-energy (DE) and single-energy (SE) images, each with and without dedicated iterative metal artifact reduction (iMAR) for CT-guided biopsy., Materials and Methods: A biopsy trocar was positioned in the liver of six pigs. DE (Sn140/100kVp) and SE (120kVp/200mAs) acquisitions were performed with equivalent dose. From dual-energy datasets DE Q30-3 images and VMSI between 40-180 keV in steps of 20 keV were generated. From SE datasets I30-3 images were reconstructed. All images were reconstructed with and without iMAR. Objective image quality was analyzed applying density measurements at standardized positions (e.g. trocar tip and liver parenchyma adjacent to the trocar tip) and semi-automated threshold based segmentation. Subjective image quality was performed using semi-quantitative scores. Analyses were performed by two observers., Results: At the trocar tip quantitative image analysis revealed significant difference in CT numbers between reconstructions with iMAR compared to reconstructions without iMAR for VMSI at lower keV levels (80 and 100 keV; p = 0.03) and DE (p = 0.03). For liver parenchyma CT numbers were significantly higher in VMSI at high keV compared to low keV (p≤0.01). VMSI at high keV also showed higher CT numbers compared to DE and SE images, though not the level of statistical significance. The best signal-to-noise ratio for VMSI was at 80 keV and comparable to DE and SE. Noise was lowest at 80 keV and lower than in DE and SE. Subjective image quality was best with VMSI at 80 keV regardless of the application of iMAR. iMAR significantly improved image quality at levels of 140 keV and 160 keV. Interreader-agreement was good for quantitative and qualitative analysis., Conclusion: iMAR improved image quality in all settings. VMSI with iMAR provided metal artifact reduction and better image quality at 80 keV and thus could improve the accurate positioning in CT-guided needle biopsy. In comparison, DE imaging did not improve image quality compared to SE., Competing Interests: The authors have declared that no competing interests exist.

Background: In computed tomography (CT) quality assurance, alignment of image quality phantoms is crucial for quantitative and reproducible evaluation and may be improved by alignment correction. Our goal was to develop an alignment correction algorithm to facilitate geological sampling of sediment cores taken from a cold-water coral mount., Methods: An alignment correction algorithm was developed and tested with a CT acquisition at 120 kVp and 150 mAs of an image quality phantom. Random translation (maximum 15 mm) and rotation (maximum 2.86°) were applied and ground-truth was compared to parameters determined by alignment correction. Furthermore, mean densities were evaluated in four regions of interest (ROIs) placed in the phantom low-contrast section, comparing values before and after correction to ground truth. This process was repeated 1000 times. After validation, alignment correction was applied to CT acquisitions (140 kVp, 570 mAs) of sediment core sections up to 1 m in length, and sagittal reconstructions were calculated for sampling planning., Results: In the phantom, average absolute differences between applied and detected parameters after alignment correction were 0.01 ± 0.06 mm (mean ± standard deviation) along the x-axis, 0.11 ± 0.08 mm along the y-axis, 0.15 ± 0.07° around the x-axis, and 0.02 ± 0.02° around the y-axis, respectively. For ROI analysis, differences in densities were 63.12 ± 30.57, 31.38 ± 32.10, 18.27 ± 35.57, and 9.59 ± 26.37 HU before alignment correction and 1.22 ± 1.40, 0.76 ± 0.9, 0.45 ± 0.86, and 0.36 ± 0.48 HU after alignment correction, respectively. For sediment core segments, average absolute detected parameters were 3.93 ± 2.89 mm, 7.21 ± 2.37 mm, 0.37 ± 0.33°, and 0.21 ± 0.22°, respectively., Conclusions: The alignment correction algorithm was successfully evaluated in the phantom and allowed a correct alignment of sediment core segments, thus aiding in sampling planning. Application to other tasks, like image quality analysis, seems possible.

Mitral valve (MV) diseases are among the most common types of heart diseases, while heart diseases are the most common cause of death worldwide. MV repair surgery is connected to higher survival rates and fewer complications than the total replacement of the MV, but MV repair requires extensive patient-specific therapy planning. The simulation of MV repair with a patient-specific model could help to optimize surgery results and make MV repair available to more patients. However, current patient-specific simulations are difficult to transfer to clinical application because of time-constraints or prohibitive requirements on the resolution of the image data. As one possible solution to the problem of patient-specific MV modeling, we present a mass-spring MV model based on 3D transesophageal echocardiographic (TEE) images already routinely acquired for MV repair therapy planning. Our novel approach to the rest-length estimation of springs allows us to model the global support of the MV leaflets through the chordae tendinae without the need for high-resolution image data. The model is used to simulate MV annuloplasty for five patients undergoing MV repair, and the simulated results are compared to post-surgical TEE images. The comparison shows that our model is able to provide a qualitative estimate of annuloplasty surgery. In addition, the data suggests that the model might also be applied to simulating the implantation of artificial chordae., (Copyright © 2015 Elsevier Ltd. All rights reserved.)