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Preimplantation cardiac computed tomography (CT) for assessment of the left atrial appendage (LAA) enables correct sizing of the device and the detection of contraindications, such as thrombi. In the arterial phase, distinction between false filling defects and true thrombi can be hampered by insufficient contrast medium distribution. A delayed scan can be used to further differentiate both conditions, but contrast in these acquisitions is relatively lower. In this study, we investigated whether virtual monoenergetic images (VMI) from dual-energy spectral detector CT (SDCT) can be used to enhance contrast and visualization in the delayed phase.Forty-nine patients receiving SDCT imaging of the LAA were retrospectively enrolled. The imaging protocol comprised dual-phase acquisitions with single-bolus contrast injection. Conventional images (CI) from both phases and 40-keV VMI from the delayed phase were reconstructed. Attenuation, signal-, and contrast-to-noise ratios (SNR/CNR) were calculated by placing regions-of-interest in the LAA, left atrium, and muscular portion of interventricular septum. Two radiologists subjectively evaluated conspicuity and homogeneity of contrast distribution within the LAA.Contrast of the LAA decreased significantly in the delayed phase but was significantly improved by VMI, showing comparable attenuation, SNR, and CNR to CI from the arterial phase (attenuation/SNR/CNR, CI arterial phase: 266.0 ± 117.0 HU/14.2 ± 7.2/6.6 ± 3.9; CI-delayed phase: 107.6 ± 35.0 HU/5.9 ± 3.0/1.0 ± 1.0; VMI delayed phase: 260.3 ± 108.6 HU/18.2 ± 10.6/4.8 ± 3.4). The subjective reading confirmed the objective findings showing improved conspicuity and homogeneity in the delayed phase.The investigated single-bolus dual-phase acquisition protocol provided improved visualization of the LAA. Homogeneity of contrast media was higher in the delayed phase, while VMI maintained high contrast.

To investigate the differences in small cell lung cancer (SCLC) diagnostic imaging utilization relative to National Comprehensive Cancer Network (NCCN) guidelines.We retrospectively reviewed SCLC records at our institution between January 1, 2003 and August 1, 2019 (n = 529). Patients were grouped by extensive-stage versus limited-stage and diagnosis date. Clinical, CT, MRI, and nuclear imaging data was collected. Imaging utilization was compared using Student's t-test or Kruskal-Wallis-test/Wilcoxon-Rank-Sums test. Survival was compared using Log-rank-test and Kaplan-Meier-curves.SCLC patients had a median survival of 290 days. Extensive-stage patients with SCLC demonstrated an increase in emergency imaging utilization when diagnosed in 2011-2019 compared to 2003-2010 (CT abdomen/pelvis p 0.001, CTA chest for pulmonary embolism p 0.01, CT head p 0.003). Limited-stage patients with SCLC demonstrated an increase in inpatient imaging utilization (CT abdomen/pelvis p 0.04) and decreased total/outpatient imaging utilization (CT chest-abdomen-pelvis p 0.05, CT head p 0.003) when diagnosed in 2011-2019 compared to 2003-2010. All patients with SCLC had decreased average number of bone-scan studies when diagnosed in 2011-2019 compared to 2003-2010 (Extensive-stage p 0.006, Limited-stage p 0.0006).Imaging utilization trends in the management of patients with SCLC at our institution differed between 2003 and 2010 and 2011-2019 reflecting the changes in the NCCN guidelines.

BACKGROUND: In stroke magnetic resonance imaging (MRI), contrast-enhanced magnetic resonance angiography (CE-MRA) is the clinical standard to depict extracranial arteries but native MRA techniques are of increased interest to facilitate clinical practice. The purpose of this study was to assess the detection of extracranial internal carotid artery (ICA) stenosis and plaques as well as the image quality of cervical carotid arteries between a novel flow-independent relaxation-enhanced angiography without contrast and triggering (REACT) sequence and CE-MRA in acute ischemic stroke (AIS). METHODS: In this retrospective, single-center study, 105 consecutive patients (65.27±18.74 years, 63 males) were included, who received a standard stroke protocol at 3T in clinical routine including Compressed SENSE (CS) accelerated (factor 4) 3D isotropic REACT (fixed scan time: 02:46 min) and CS accelerated (factor 6) 3D isotropic CE-MRA. Three radiologists independently assessed scans for the presence of extracranial ICA stenosis and plaques (including hyper-/hypointense signal) with concomitant diagnostic confidence using 3-point scales (3= excellent). Vessel quality, artifacts, and image noise of extracranial carotid arteries were subjectively scored on 5-point scales (5= excellent/none). Wilcoxon tests were used for statistical comparison. RESULTS: Considering CE-MRA as the standard of reference, REACT provided a sensitivity of 89.8% and specificity of 95.2% for any and of 93.5% and 95.8% for clinically relevant (≥50%) extracranial ICA stenosis and yielded a to CE-MRA comparable diagnostic confidence [mean ± standard deviation (SD), median (interquartile range): 2.8±0.5, 3 (3–3) vs. 2.7±0.5, 3 (2–3), P=0.03]. Using REACT, readers detected more plaques overall (n=57.3 vs. 47.7, P0.05] were comparable between the sequences with REACT showing a lower image noise [4.43±0.67 vs. 4.25±0.71, 5 (4–5) vs. 4 (4–5), P

Purpose To evaluate vessel assessment in virtual monoenergetic images (VMI40keV) and virtual-non-contrast images (VNC) derived from venous phase spectral detector computed tomography (SDCT) acquisitions in comparison to arterial phase and true non-contrast (TNC) images. Methods Triphasic abdominal SDCT was performed in 25 patients including TNC, arterial and venous phase. VMI40keV and VNC were reconstructed from the venous phase and compared to conventional arterial-phase images (CIart), TNC and conventional venous-phase images (CIven). Vessel contrast and virtual contrast removal were analyzed with region-of-interest-based measurements and in a qualitative assessment. Results Quantitative analysis revealed no significant attenuation differences between TNC and VNC in arterial vessels (p-range 0.07–0.47) except for the renal artery (p = 0.011). For venous vessels, significant differences between TNC and VNC were found for all veins (p p = 0.26), yet these differences remained within a 10 HU range in most patients. No significant attenuation differences were found between CIart/VMI40keV in arterial vessels (p-range 0.06–0.86). Contrast-to-noise ratio provided by VMI40keV and CIart was equivalent for all arterial vessels assessed (p-range 0.14–0.91). Qualitatively, VMI40keV showed similar enhancement of abdominal and pelvic arteries as CIart and VNC were rated comparable to TNC. Conclusion Our study suggests that VNC and VMI40keV derived from single venous-phase SDCT offer comparable assessment of major abdominal vessels as provided by routine triphasic examinations, if no dynamic contrast information is required.

OBJECTIVE The aim of this study was to investigate if Hounsfield unit (HU) values from virtual noncontrast (VNC) images derived from portal venous phase spectral-detector computed tomography can help to differentiate adrenal adenomas and metastases. METHODS Spectral-detector computed tomography datasets of 33 patients with presence of adrenal lesions and standard of reference for lesion origin by follow-up/prior examinations or dedicated magnetic resonance imaging were included. Conventional and VNC images were reconstructed from the same scan. Region of interest-based image analysis was performed in adrenal lesions and contralateral healthy adrenal tissue. RESULTS The 33 lesions consisted of 23 adenomas and 10 metastases. Hounsfield unit values of all lesions in VNC images were significantly lower compared with conventional images (18.2 ± 12.6 HU vs 59.6 ± 21.7 HU, P < 0.001). Hounsfield unit values in adenomas were significantly lower in VNC images (11.3 ± 6.5 HU vs 34.1 ± 9.1 HU, P < 0.001). CONCLUSIONS Virtual noncontrast HU values differed significantly between adrenal adenomas and metastases and can therefore be used for improved characterization of incidental adrenal lesions and definition of adrenal adenomas.

Pulmonary embolism is a commonly encountered diagnosis that is traditionally identified on conventional computed tomography angiography. Dual-energy computed tomography (DECT) is a new technology that may aid the initial identification and differential diagnosis of pulmonary embolism. In this review, we present an algorithmic approach for assessing pulmonary embolism on DECT, including acute versus chronic pulmonary embolism, relationship to conventional computed tomography angiography, surrogate for likelihood of hemodynamic significance, and alternative diagnoses for DECT perfusion defects.

Conventional computed tomography (CT) plays an important role in detection of lung nodules. However, further characterization is usually limited requiring additional imaging and invasive work up. Spectral Detector CT (SDCT) is an upcoming novel modality that not only allows morphological evaluation but also provides insight into prediction of malignant behavior of lung nodules. Additional quantification capabilities available from the same scan make it a more comprehensive imaging option in oncology patients. This is a first case report demonstrating the potential of single SDCT to provide necessary information for lung cancer diagnosis and preoperative planning, comparable to standard of care imaging.

Background Evaluation of small cystic lesions of the pancreas remains a challenging task, as due to their size appearance can be rather hypodense than clearly fluid-filled. Purpose To evaluate whether additional information provided by novel dual-layer spectral-detector computed tomography (SDCT) imaging can improve assessment of these lesions. Material and Methods For this retrospective study, we reviewed reports of 1192 contrast-enhanced portal-venous phase SDCT scans of the abdomen conducted between May 2017 and January 2019. On basis of the radiological report 25 small (≤1.5 cm) cystic pancreatic lesions in 22 patients were identified, in which additional short-term follow-up imaging was recommended to confirm/clarify cystic nature. Conventional images (CI) and spectral images (SI) including virtual-monoenergetic images at 40 keV (VMI), iodine-density and iodine-overlay images were reconstructed. Two readers indicated lesion conspicuity and confidence for presence of cystic nature on three-point scales. First, solely CI were evaluated, while in a second reading after a four-week interval, the combination of CI and corresponding SI were reviewed. Quantitatively, ROI-based mean attenuation was measured in CI and VMI. Results In the subjective reading, SI significantly improved lesion conspicuity (CI 2 [1–2], SI 3 [2–3], P Conclusion Compared to CI alone, combination with SI significantly improves visualization and confidence in evaluation of small equivocal cystic pancreatic lesions.

Amiodarone accumulates in the liver, where it increases x-ray attenuation due to its iodine content. We evaluated liver attenuation in patients treated and not treated with amiodarone using true-non-contrast (TNC) and virtual-non-contrast (VNC) images acquired with spectral-detector-CT (SDCT). 142 patients, of which 21 have been treated with amiodarone, receiving SDCT-examinations (unenhanced-chest CT [TNC], CT-angiography of chest and abdomen [CTA-Chest, CTA-Abdomen]) were included. TNC, CTA-Chest, CTA-Abdomen, and corresponding VNC-images (VNC-Chest, VNC-Abdomen) were reconstructed. Liver-attenuation-index (LAI) was calculated as difference between liver- and spleen-attenuation. Liver-attenuation and LAI derived from TNC-images of patients receiving amiodarone were higher. Contrary to TNC, liver-attenuation and LAI were not higher in amiodarone patients in VNC-Chest and in VNC-Abdomen. To verify these initial results, a phantom scan was performed and an additional patient cohort included, both confirming that VNC is viable of accurately subtracting iodine of hepatic amiodarone-deposits. This might help to monitor liver-attenuation more accurately and thereby detect liver steatosis as a sign of liver damage earlier as well as to verify amiodarone accumulation in the liver.

To evaluate artifact reduction by virtual monoenergetic images (VMI) and metal artifact reduction algorithms (MAR) as well as the combination of both approaches (VMIMAR) compared to conventional CT images (CI) as standard of reference. In this retrospective study, 35 patients were included who underwent spectral-detector CT (SDCT) with additional MAR-reconstructions due to artifacts from coils or clips. CI, VMI, MAR and VMIMAR (range: 100–200 keV, 10 keV-increment) were reconstructed. Region-of-interest based objective analysis was performed by assessing mean and standard deviation of attenuation (HU) in hypo- and hyperdense artifacts from coils and clips. Visually, extent of artifact reduction and diagnostic assessment were rated. Compared to CI, VMI ≥ 100 keV, MAR and VMIMAR between 100–200 keV increased attenuation in hypoattenuating artifacts (CI/VMI200keV/MAR/VMIMAR200keV, HU: −77.6 ± 81.1/−65.1 ± 103.2/−36.9 ± 27.7/−21.1 ± 26.7) and decreased attenuation in hyperattenuating artifacts (HU: 47.4 ± 32.3/42.1 ± 50.2/29.5 ± 18.9/20.8 ± 25.8). However, differences were only significant for MAR in hypodense and VMIMAR in hypo- and hyperdense artifacts (p ≥140/100keV, MAR and VMIMAR≥100keV. Diagnostic assessment of surrounding brain tissue was significantly improved in VMI≥100keV, MAR and VMIMAR≥100keV. The combination of VMI and MAR facilitates a significant reduction of artifacts adjacent to intracranial coils and clips. Hence, if available, these techniques should be combined for optimal reduction of artifacts following intracranial aneurysm treatment.

OBJECTIVE. The purpose of this article is to outline the utility of iodine density maps for evaluating cardiothoracic disease and abnormalities. Multiple studies have shown that the variety of images generated from dual-energy spectral detector CT (SDCT) improve identification of cardiothoracic conditions. CONCLUSION. Understanding the technique of SDCT and being familiar with the features of different cardiothoracic conditions on iodine density map images help the radiologist make a better diagnosis.

Purpose In chest imaging, sternal wires can cause metal artifacts that hamper depiction of surrounding soft tissue and bone. This study investigated if these artifacts may be reduced by means of virtual monoenergetic images (VMI) obtained from a novel detector-based spectral CT scanner (SDCT) in comparison to conventional CT images (CI). Materials and methods 30 patients with clinically indicated SDCT scans of the chest exhibiting artifacts due to sternal wires were included in this IRB-approved study. CI and VMI (40–200 keV, 10 keV increment) were reconstructed. Quantitative image analysis was conducted by ROI-based measurement of attenuation (HU) and standard deviation within the most pronounced hypo- and hyperdense artifacts. Visually, artifact reduction and diagnostic assessment of surrounding soft tissue as well as sternal bone were independently rated by two radiologists on 5-point Likert-scales. Results In comparison to CI, high-keV VMI showed an effective reduction of hypo- and hyperattenuating parasternal artifacts, as corrected HU-values approximated their true expected values (hypodense: CI −66.2 ± 70.8; VMI200keV 2.4 ± 29.2; hyperdense: CI 156.7 ± 70.8HU; VMI200keV 76.9 ± 45.4, both p Conclusions High-keV VMI yielded a significant reduction of artifacts from sternal wires and improved assessment of surrounding structures.

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.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.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 (p0.05). 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.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.

Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation. The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus. Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume. Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

In recent years, the use of immune checkpoint inhibitor (ICI) therapy has rapidly grown, with increasing U.S. Food and Drug Administration approvals of a variety of agents used as first- and second-line treatments of various malignancies. ICIs act through a unique mechanism of action when compared with those of conventional chemotherapeutic agents. ICIs target the cell surface receptors cytotoxic T-lymphocyte antigen-4, programmed cell death protein 1, or programmed cell death ligand 1, which result in immune system-mediated destruction of tumor cells. Immune-related adverse events are an increasingly recognized set of complications of ICI therapy that may affect any organ system. ICI therapy-related pneumonitis is an uncommon but important complication of ICI therapy, with potential for significant morbidity and mortality. As the clinical manifestation is often nonspecific, CT plays an important role in diagnosis and triage. Several distinct radiographic patterns of pneumonitis have been observed: (a) organizing pneumonia, (b) nonspecific interstitial pneumonia, (c) hypersensitivity pneumonitis, (d) acute interstitial pneumonia-acute respiratory distress syndrome, (e) bronchiolitis, and (f) radiation recall pneumonitis. Published guidelines outline the treatment of ICI therapy-related pneumonitis based on the severity of symptoms. Treatment is often effective, although recurrence is possible. This article reviews the mechanism of ICIs and ICI therapy complications, with subsequent management techniques and illustrations of the various radiologic patterns of ICI-therapy related pneumonitis.©RSNA, 2019.

Objective This study aimed to identify the energy level of virtual monoenergetic images (VMI) that closest represents conventional images (CI) in order to demonstrate that these images provide improved image quality in terms of noise and Signal-to-noise ratio (SD/SNR) while attenuation values (HU) remain unaltered as compared to CI. Methods 60 and 30 patients with contrast-enhanced (CE) and non-enhanced (NCE) spectral detector CT (SDCT) of the abdomen were included in this retrospective, IRB-approved study. CI and VMI of 66–74 keV as well as quantitative iodine maps were reconstructed (Q-IodMap). Two regions of interest were placed in each: pulmonary trunk, abdominal aorta, portal vein, liver, pancreas, renal cortex left/right, psoas muscle, (filled) bladder and subcutaneous fat. For each reconstruction, HU and SD were averaged. ΔHU and SNR (SNR = HU/SD) were calculated. Q-IodMap were considered as confounder for ΔHU. In addition, two radiologists compared VMI of 72 keV and CI in a forced-choice approach regarding image quality. Results In NCE studies, no significant differences for any region was found. In CE studies, VMI72keV images showed lowest ΔHU (HUliver CI/VMI72keV: 104 ± 18/103 ± 17, p ≥ 0.05). Iodine containing voxels as indicated by Q-IodMap resulted in an over- and underestimation of attenuation in lower and higher VMI energies, respectively. Image noise was lower in VMI images (e.g. muscle: CI/ VMI72keV: 15.3 ± 3.3/12.3 ± 2.9 HU, p ≤ 0.05). Hence, SNR was significantly higher in VMI72keV compared to CI (e.g. liver 3.8 ± 0.6 vs 3.0 ± 0.8, p ≤ 0.05). In visual analysis, VMI72keV were preferred over CI at all times. Conclusions VMI72keV show improved SD/SNR characteristics while the attenuation remains unaltered as compared to CI.

In multiple sclerosis (MS), the heterogeneous and numerous appearances of lesions may impair diagnostic accuracy. This study investigates if a combined automated co-registration and lesion color-coding method (AC) improves assessment of MS follow-up MRI compared with conventional reading (CR). We retrospectively assessed 70 follow-up MRI of 53 patients. Heterogeneous datasets of diverse scanners and institutions were used. Two readers determined presence of (a) progression, (b) regression, (c) mixed change, or (d) stable disease between the two examinations using corresponding FLAIR sequences in CR and AC-assisted reading. Consensus reference reading was provided by two blinded radiologists. Kappa statistics tested interrater agreement, McNemar’s test dichotomous variables, and Wilcoxon’s test continuous variables (statistical significance p ≤ 0.05). The cohort comprised 41 female and 12 male patients with a mean age of 40 (± 14) years. Average rating time was reduced from 78 (± 36) to 44 (±22) s with the AC approach (p

Purpose: Meningioma is the most common primary brain tumor in adults. In recent years, several non-NF2 mutations, i.e. AKT1, SMO, TRAF7, and KLF4 mutations, specific for meningioma have been identified. This study aims to analyze the clinical impact and imaging characteristics of the KLF4K409Q mutation in meningioma. Methods: Clinical, neuropathological, and imaging data of 170 patients who underwent meningioma resection between 2013 and 2018 were retrospectively collected and tumors were analyzed for the presence of the KLF4K409Q mutation. We collected imaging characteristics, performed semiautomatic volumetric analysis of tumor size and peritumoral edema (PTBE), and calculated the edema index (EI, i.e. ratio of PTBE to tumor volume). Receiver operating characteristic (ROC) curve analysis was performed to identify cut-off EI values to predict the mutational status of KLF4.Results: Eighteen (10.6%) of the meningiomas carried the KLF4K409Qmutation; these were significantly associated with a secretory subtype (pKLF4K409Q mutation. In receiver operating characteristic (ROC) curve analysis, EI predicted the KLF4K409Q mutation with an AUC of 0.728 (p=0.0016). Conclusion: The KLF4K409Q mutation is associated with a distinct small tumor subtype, prone to substantial PTBE. EI is a reliable parameter to predict the KLF4K409Q mutation in meningioma, thus providing a tool for improvement of pre- and perioperative medical management.

BACKGROUND AND PURPOSE: Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging. MATERIALS AND METHODS: Sixty-nine patients with melanoma with a total of 135 brain metastases at initial diagnosis and available multiparametric MR imaging datasets (T1-/T2-weighted, T1-weighted gadolinium contrast-enhanced, FLAIR) were included. A previously established deep learning model architecture (3D convolutional neural network; DeepMedic) simultaneously operating on the aforementioned MR images was trained on a cohort of 55 patients with 103 metastases using 5-fold cross-validation. The efficacy of the deep learning model was evaluated using an independent test set consisting of 14 patients with 32 metastases. Manual segmentations of metastases in a voxelwise manner (T1-weighted gadolinium contrast-enhanced imaging) performed by 2 radiologists in consensus served as the ground truth. RESULTS: After training, the deep learning model detected 28 of 32 brain metastases (mean volume, 1.0 [SD, 2.4] cm(3)) in the test cohort correctly (sensitivity of 88%), while false-positive findings of 0.71 per scan were observed. Compared with the ground truth, automated segmentations achieved a median Dice similarity coefficient of 0.75. CONCLUSIONS: Deep learning–based automated detection and segmentation of brain metastases in malignant melanoma yields high detection and segmentation accuracy with false-positive findings of

Aneurysmal subarachnoid hemorrhage (aSAH) is associated with high morbidity. The objective was to evaluate, whether specific morphological aneurysm characteristics could serve as predictive values for aSAH severity, disease-related complications and clinical outcome.A total of 453 aSAH patients (mean age: 54.9 ± 13.8 years, mean aneurysm size: 7.5 ± 3.6 mm) treated at a single center were retrospectively included. A morphometric analysis was performed based on angiographic image sets, determining aneurysm location, aneurysm size, neck width, aneurysm size ratios, aneurysm morphology and vessel size. The following outcome measures were defined: World Federation of Neurosurgical Societies (WFNS) grade 4 and 5, Fisher grade 4, vasospasm, cerebral infarction and unfavorable functional outcome.Regarding morphology parameters, aneurysm neck width was an independent predictor for Fisher 4 hemorrhage (OR: 1.1, 95%CI: 1.0-1.3, p = 0.048), while dome width (OR: 0.92, 95%CI: 0.86-0.97, p = 0.005) and internal carotid artery location (OR: 2.1, 95%CI: 1.1-4.2, p = 0.028) predicted vasospasm. None of the analyzed morphological characteristics prognosticated functional outcome. Patient age (OR: 0.95, 95%CI: 0.93-0.96, p 0.001), WFNS score (OR: 4.8, 95%CI: 2.9-8.0, p 0.001), Fisher score (OR: 2.3, 95%CI: 1.4-3.7, p 0.001) and cerebral infarction (OR: 4.5, 95%CI: 2.7-7.8, p 0.001) were independently associated with unfavorable outcome.The findings indicate a correlation between aneurysm morphology, Fisher grade and vasospasm. Further studies will be required to reveal an independent association of aneurysm morphology with cerebral infarction and functional outcome.

Background: After injection into a brachial vein, high contrast media concentration in axillary and subclavian veins can cause artifacts that impair diagnostic utility. This study assessed artifact reduction by artifact-reduction-algorithms (ARA) and virtual-monoenergetic-images (VMI), as well as their combination (VMIARA) compared to conventional CT-images (CI). Methods: Forty-six spectral-detector-CT (SDCT) examinations of patients that received ARA-reconstructions due to perivenous-artifacts were included in this retrospective study. CI, ARA, VMI, and VMIARA (range: 70-200 keV) were reconstructed. Objective analysis was performed with ROI-based assessment of mean and standard deviation of attenuation (HU) in hypo- and hyperdense artifacts and impaired muscle and arteries as well as artifact-free reference-tissue. Extent of artifact reduction, assessment of surrounding soft tissue and vessels, and appearance of new artifacts were rated visually by two radiologists. Results: Hypo- and hyperdense artifacts showed significant improvement as evidenced by decreasing attenuation differences between artifact impaired and artifact-free reference tissue in ARA, VMI ≥80 keV, and VMIARA between 70-200 keV (e.g., CI/ARA/VMI100keV/VMIARA100keV: hypodense artifacts, (-)264.8±150.9/(-)87.1±78.9/(-)48.6±64.6/9.9±63.9 HU; P

Recent advances in dual-energy imaging techniques, dual-energy subtraction radiography (DESR) and dual-energy CT (DECT), offer new and useful additional information to conventional imaging, thus improving assessment of cardiothoracic abnormalities. DESR facilitates detection and characterization of pulmonary nodules. Other advantages of DESR include better depiction of pleural, lung parenchymal, airway and chest wall abnormalities, detection of foreign bodies and indwelling devices, improved visualization of cardiac and coronary artery calcifications helping in risk stratification of coronary artery disease, and diagnosing conditions like constrictive pericarditis and valvular stenosis. Commercially available DECT approaches are classified into emission based (dual rotation/spin, dual source, rapid kilovoltage switching and split beam) and detector-based (dual layer) systems. DECT provide several specialized image reconstructions. Virtual non-contrast images (VNC) allow for radiation dose reduction by obviating need for true non contrast images, low energy virtual mono-energetic images (VMI) boost contrast enhancement and help in salvaging otherwise non-diagnostic vascular studies, high energy VMI reduce beam hardening artifacts from metallic hardware or dense contrast material, and iodine density images allow quantitative and qualitative assessment of enhancement/iodine distribution. The large amount of data generated by DECT can affect interpreting physician efficiency but also limit clinical adoption of the technology. Optimization of the existing workflow and streamlining the integration between post-processing software and picture archiving and communication system (PACS) is therefore warranted.

Objectives To evaluate the reduction of artifacts from cardiac implantable electronic devices (CIEDs) by virtual monoenergetic images (VMI), metal artifact reduction (MAR) algorithms, and their combination (VMIMAR) derived from spectral detector CT (SDCT) of the chest compared to conventional CT images (CI). Methods In this retrospective study, we included 34 patients (mean age 74.6 ± 8.6 years), who underwent a SDCT of the chest and had a CIED in place. CI, MAR, VMI, and VMIMAR (10 keV increment, range: 100–200 keV) were reconstructed. Mean and standard deviation of attenuation (HU) among hypo- and hyperdense artifacts adjacent to CIED generator and leads were determined using ROIs. Two radiologists qualitatively evaluated artifact reduction and diagnostic assessment of adjacent tissue. Results Compared to CI, MAR and VMIMAR ≥ 100 keV significantly increased attenuation in hypodense and significantly decreased attenuation in hyperdense artifacts at CIED generator and leads (p < 0.05). VMI ≥ 100 keV alone only significantly decreased hyperdense artifacts at the generator (p < 0.05). Qualitatively, VMI ≥ 100 keV, MAR, and VMIMAR ≥ 100 keV provided significant reduction of hyper- and hypodense artifacts resulting from the generator and improved diagnostic assessment of surrounding structures (p < 0.05). Diagnostic assessment of structures adjoining to the leads was only improved by MAR and VMIMAR 100 keV (p < 0.05), whereas keV values ≥ 140 with and without MAR significantly worsened diagnostic assessment (p < 0.05). Conclusions The combination of VMI and MAR as well as MAR as a standalone approach provides effective reduction of artifacts from CIEDs. Still, higher keV values should be applied with caution due to a loss of soft tissue and vessel contrast along the leads. Key Points • The combination of VMI and MAR as well as MAR as a standalone approach enables effective reduction of artifacts from CIEDs. • Higher keV values of both VMI and VMIMARat CIED leads should be applied with caution since diagnostic assessment can be hampered by a loss of soft tissue and vessel contrast. • Recommended keV values for CIED generators are between 140 and 200 keV and for leads around 100 keV.

Objective: Meningioma grading is relevant to therapy decisions in complete or partial resection, observation, and radiotherapy because higher grades are associated with tumor growth and recurrence. The differentiation of low and intermediate grades is particularly challenging. This study attempts to[for full text, please go to the a.m. URL], 71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), 9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie

Purpose To evaluate the reduction of artifacts caused by total hip replacements (THR) in dual-layer DECT (DLCT) provided by the combination of virtual monoenergetic images (VMI) and orthopedic metal artifact reduction (MAR). Materials and Methods A total of 24 consecutive patients carrying THR, who received DLCT, were included. Four different images were reconstructed from the same CT dataset: a) conventional images (CI), b) conventional images with orthopedic metal artifact reduction (CIMAR) c) VMI and d) VMI combined with orthopedic metal artifact reduction (VMIMAR). VMI and VMIMAR were reconstructed at 140 keV, 160 keV, 180 keV and 200 keV. Attenuation (HU) and noise (SD) were measured in order to evaluate reduction of hypodense and hyperdense artifacts, evaluate reduction of image noise as well as to calculate contrast-to-noise ratios (CNR). Image quality was additionally rated with regard to: a) extent of artifact reduction and assessment of b) pelvic organs, c) bone and d) muscle adjacent to the metal implants. Statistical analysis was performed using Wilcoxon test. Results VMIMAR at high keV, 140, 160, 180 and 200 keV, led to the greatest reduction of hypodense artifacts in comparison to plain VMI or CIMAR (p Conclusion The combination of VMI and MAR yields strongest reduction of hypo- and hyperdense artifacts caused by total hip replacements in staging DLCT in comparison to each technique by itself.

BACKGROUND Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time-consuming and impeded by intra- and interrater variabilities. PURPOSE To investigate the performance of a deep-learning model (DLM) to automatically detect and segment primary central nervous system lymphoma (PCNSL) on clinical MRI. STUDY TYPE Retrospective. POPULATION Sixty-nine scans (at initial and/or follow-up imaging) from 43 patients with PCNSL referred for clinical MRI tumor assessment. FIELD STRENGTH/SEQUENCE T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE), and FLAIR at 1.0, 1.5, and 3.0T from different vendors and study centers. ASSESSMENT Fully automated voxelwise segmentation of tumor components was performed using a 3D convolutional neural network (DeepMedic) trained on gliomas (n = 220). DLM segmentations were compared to manual segmentations performed in a 3D voxelwise manner by two readers (radiologist and neurosurgeon; consensus reading) from T1 CE and FLAIR, which served as the reference standard. STATISTICAL TESTS Dice similarity coefficient (DSC) for comparison of spatial overlap with the reference standard, Pearson's correlation coefficient (r) to assess the relationship between volumetric measurements of segmentations, and Wilcoxon rank-sum test for comparison of DSCs obtained in initial and follow-up imaging. RESULTS The DLM detected 66 of 69 PCNSL, representing a sensitivity of 95.7%. Compared to the reference standard, DLM achieved good spatial overlap for total tumor volume (TTV, union of tumor volume in T1 CE and FLAIR; average size 77.16 ± 62.4 cm3 , median DSC: 0.76) and tumor core (contrast enhancing tumor in T1 CE; average size: 11.67 ± 13.88 cm3 , median DSC: 0.73). High volumetric correlation between automated and manual segmentations was observed (TTV: r = 0.88, P

Objectives Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Methods We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. Results The DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE. Conclusions The DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity. Key Points • Deep learning allows for accurate meningioma detection and segmentation • Deep learning helps clinicians to assess patients with meningiomas • Meningioma monitoring and treatment planning can be improved Electronic supplementary material The online version of this article (10.1007/s00330-018-5595-8) contains supplementary material, which is available to authorized users.

This study compares metal artifact (MA) reduction in imaging of total hip replacements (THR) using virtual monoenergetic images (VMI), for MA-reduction-specialized reconstructions (MAR) and conventional CT images (CI) from detector-based dual-energy computed tomography (SDCT).Twenty-seven SDCT-datasets of patients carrying THR were included. CI, MAR and VMI with different energy-levels (60-200 keV) were reconstructed from the same scans. MA width was measured. Attenuation (HU), noise (SD) and contrast-to-noise ratio (CNR) were determined in: extinction artifact, adjacent bone, muscle and bladder. Two radiologists assessed MA-reduction and image quality visually.In comparison to CI, VMI (200 keV) and MAR showed a strong artifact reduction (MA width: CI 29.9±6.8 mm, VMI 17.6±13.6 mm, p0.001; MAR 16.5±14.9 mm, p0.001; MA density: CI -412.1±204.5 HU, VMI -279.7±283.7 HU; p0.01; MAR -116.74±105.6 HU, p0.001). In strong artifacts reduction was superior by MAR. In moderate artifacts VMI was more effective. MAR showed best noise reduction and CNR in bladder and muscle (p0.05), whereas VMI were superior for depiction of bone (p0.05). Visual assessment confirmed that VMI and MAR improve artifact reduction and image quality (p0.001).MAR and VMI (200 keV) yielded significant MA reduction. Each showed distinct advantages both regarding effectiveness of artifact reduction, MAR regarding assessment of soft tissue and VMI regarding assessment of bone.• Spectral-detector computed tomography improves assessment of total hip replacements and surrounding tissue. • Virtual monoenergetic images and MAR reduce metal artifacts and enhance image quality. • Evaluation of bone, muscle and pelvic organs can be improved by SDCT.

Objectives To compare virtual monoenergetic images (VMI) reconstructed from venous phase Dual-Layer CT (DLCT) with polyenergetic images (PI) of DLCT-Angiography (DLCT-A) regarding vessel contrast and image quality especially in sight to atherosclerotic carotid artery stenosis. Methods & materials 25 DLCT-A and 55 venous phase DLCT were analyzed in this retrospective study. For objective analysis PI and VMI (40–120 keV) were assessed comparing attenuation, standard deviation, signal-/contrast- to noise ratios (SNR, CNR) in the common carotid artery (CCA), vertebral artery, sternocleidomastoid muscle and air. For subjective analysis, vessel contrast, delineation of the superficial temporal artery, depiction of calcified plaque as well as vessel patency within the atherosclerotic stenosis of the internal carotid artery were rated and the extent of the calcified plaque and remaining vessel lumen were measured in venous phase DLCT. Results In venous phase DLCT, attenuation, SNR and CNR in the CCA increased with lower keV. Attenuation, SNR and CNR at 40 keV in the CCA were comparable to PI of DLCT-A (all: p > 0.05). Subjective image contrast, assessment of vessel patency within a stenosis as well as delineation of the superficial temporal artery were rated superior at 40–60 keV in comparison to PI of venous phase DLCT (all: p ≤ 0.05). Slightly more blooming of the atherosclerotic plaque was found in VMI at 40–60 keV. There was no difference of NASCET-criteria of carotid stenosis between VMI at different keV-levels and PI (p = 1.0). Conclusion VMI at 40 keV reconstructed from venous phase DLCT yield an image quality equal to CT-Angiography, especially regarding vessel contrast. Perception and assessment of the carotid artery within an atherosclerotic stenosis are not impaired at low keV.

OBJECTIVE Timely aneurysm occlusion and neurointensive care treatment are key principles in the management of aneurysmal subarachnoid hemorrhage (aSAH) to prevent secondary brain injury. Patients with early (EHA) and delayed hospital admission (DHA) were compared in terms of clinical presentation, treatment strategies, aSAH-related complications, and outcome. METHODS In this retrospective study, consecutive aSAH patients were treated at a single neurovascular center between 2009 and 2019. Propensity score matching was performed to account for divergent baseline characteristics. RESULTS Among 509 included patients, 55 were admitted more than 48 hours after ictus (DHA group). DHA patients were significantly younger (52 ± 11 vs 56 ± 14 years, p = 0.03) and had lower World Federation of Neurosurgical Societies scores (p < 0.01) than EHA patients. In 54.5% of the cases, DHA patients presented with neurological deterioration or aggravated symptoms. Propensity score matching revealed a higher vasospastic infarction rate in the DHA group (41.5%) than in the EHA group (22.6%) (p = 0.04). A similar portion of patients in both groups achieved favorable outcome at midterm follow-up (77.3% vs 73.6%, p = 0.87). DHA patients (62.3%) received conventional coiling more often than EHA patients (41.5%) (p = 0.03). CONCLUSIONS DHA patients are at an increased risk of cerebral infarction. Nevertheless, state-of-the-art neurointensive care treatment can result in a good clinical outcome.

Background In abdominal imaging, contrast-enhanced computed tomography (CT) examinations are most commonly applied; however, unenhanced examinations are still needed for several clinical questions but require additional scanning and radiation exposure. Purpose To evaluate accuracy of virtual non-contrast (VNC) from arterial and venous phase spectral-detector CT (SDCT) scans compared to true-unenhanced (TNC) images for the evaluation of liver parenchyma and vessels. Material and Methods A total of 25 patients undergoing triphasic SDCT examinations were included. VNC was reconstructed from arterial and venous phases and compared to TNC images. Quantitative image analysis was performed by region of interest (ROI)-based assessment of mean and SD of attenuation (HU) in each liver segment, spleen, portal vein, common hepatic artery, and abdominal aorta. Subjectively, iodine subtraction and diagnostic assessment were rated on 5-point Likert scales. Results Attenuation and image noise measured in the liver from VNC were not significantly different from TNC (TNC: 54.6 ± 10.8 HU, VNC arterial phase: 55.7 ± 10.8 HU; VNC venous phase: 58.3 ± 10.0 HU; P > 0.05). VNC also showed accurate results regarding attenuation and image noise for spleen, portal vein, and abdominal aorta. Only iodine subtraction in the common hepatic artery in the arterial phase was insufficient which was confirmed by the subjective reading. Apart from that, subjective reading showed accurate iodine subtraction and comparable diagnostic assessment. Conclusion VNC from the arterial and venous phases were very similar to TNC yielding mostly negligible differences in attenuation, image noise, and diagnostic utility. Inadequate iodine subtraction occurred in hepatic arteries in the arterial phase.

Objective: To evaluate accuracy of virtual-non-contrast images (VNC) compared to true-unenhanced-images (TNC) for evaluation of liver attenuation acquired using spectral-detector CT (SDCT). Methods: 149 patients who underwent multiphase transcatheter-aortic-valve-replacement (TAVR) SDCT-examinations [unenhanced-chest (TNC), CT-angiography chest (CTA-chest, early arterial-phase) and abdomen (CTA-abdomen, additional early arterial-phase after a second injection of contrast media)] were retrospectively included. VNC of CTA-chest (VNC-chest) and CTA-abdomen (VNC-abdomen) were reconstructed and compared to TNC. Region of interest-based measurement of mean attenuation (Hounsfield unit, HU) was applied in the following regions: liver, spleen, abdominal aorta and paraspinal muscle. Results: VNC accuracy was high in the liver, spleen, abdominal aorta and muscle for abdomen-scanning. For the liver, average attenuation was 59.0 ± 9.1 HU for TNC and 72.6 ± 9.5 HU for CTA-abdomen. Liver attenuation in VNC-abdomen (59.1 ± 6.4 HU) was not significantly different from attenuation in TNC (p > 0.05). In contrast, VNC was less accurate for chest-scanning: Due to the protocol, in CTA-chest no contrast media was present in the liver parenchyma as indicated by the same attenuation in TNC (59.0 ± 9.1 HU) and CTA-chest (58.8 ± 8.9 HU, p > 0.05). Liver attenuation in VNC-chest (56.2 ± 6.4 HU, p < 0.05) was, however, significantly lower than in TNC and CTA-chest implying an artificial reduction of attenuation. Conclusion: VNC performed well in a large cohort of TAVR-examinations yielding equivalent mean attenuations to TNC; however, application of this technique might be limited when no or very little contrast media is present in parenchyma, more precisely in an early arterial-phase of the liver. Advances in knowledge: This study showed that VNC can be reliably applied in cardiac protocols when certain limitations are considered

Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation.The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus.Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume.Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

Although the risk of aneurysm remnants after microsurgical clipping is generally low, complete aneurysm occlusion is not always guaranteed. We performed a morphometric analysis of intracranial aneurysms to identify predictors for aneurysm remnants and to propose a novel risk score.This is a retrospective, single-center analysis of consecutive patients with ruptured and unruptured aneurysms who underwent microsurgical clipping and postoperative digital subtraction angiography between 2010 and 2018. Based on preoperative rotational angiography, distinct morphologic aneurysm characteristics were determined and correlated with postoperative angiographic results. Factors predictive in the univariate and multivariate analyses were determined to establish a risk score for postoperative remnants after aneurysm clipping.Among 140 patients with 166 clipped aneurysms, aneurysm remnants were present in 19.9%. In the multivariate analysis, ruptured aneurysm status (odds ratio [OR], 7.8; 95% confidence interval [CI], 1.7-36; P0.01) and increased aspect ratio (OR, 1.9; 95% CI, 1.0-4.0; P = 0.07) were associated with postoperative aneurysm remnants. Anterior communicating artery location (P = 0.02), internal carotid artery location (P = 0.06), increased aneurysm inclination angle (P0.01), and irregular aneurysm shape (P = 0.07) were further predictors for aneurysm remnants in the univariate analysis. These factors were weighted and included into a risk sum score for postoperative aneurysm remnants (range, 0-8 points), which performed with good accuracy (area under the curve = 0.807).After external validation of the proposed risk score, it could help identify cases requiring angiographic control after aneurysm surgery.

Background In CT imaging, a high concentration of iodinated contrast media in axillary and subclavian veins after brachial application can cause perivenous artifacts impairing diagnostic assessment of local vascular structures and soft tissue. Purpose To investigate reduction of perivenous hypo- and hyperattenuating artifacts of the axillary and subclavian veins using virtual monoenergetic images (VMI) in comparison to conventional CT images (CI), acquired on spectral-detector CT. Material and Methods 50 spectral-detector CT datasets of patients with perivenous artifacts from contrast media were included in this retrospective, institutional review board-approved study. CT images and virtual monoenergetic images (range 40–200 keV, 10-keV increments) were reconstructed from the same scans. Quantitative analysis was performed by region of interest-based assessment of mean attenuation (HU) and standard deviation in most pronounced hypo- and hyperdense artifacts and artifact-impaired arteries as well as muscle. Visually, artifact reduction, assessment of vessels, and surrounding soft tissue were rated on 5-point Likert-scales by two radiologists. Results In comparison to CT images, virtual monoenergetic images of ≥90 keV showed a significant reduction of hypo- and hyperattenuating artifacts (hypodense: CI -220.0±171.2 HU; VMI130keV -13.4±49.1 HU; hyperdense: CI 274.6±184.4 HU; VMI130keV 24.2±84.9 HU; P130keV 5[4–5], hyperdense: CI 2[1–4]; VMI130keV 5[5–5], PConclusion Higher keV virtual monoenergetic images yielded significant reduction of contrast media artifacts and led to improved assessment of vessels and surrounding soft tissue. Recommended keV values for best diagnostic assessment are in the range of 100–160 keV.

Computed tomographic angiography (CTA) based planning for transcatheter aortic valve replacement (TAVR) is essential for reduction of periprocedural complications. Spectral CT based imaging provides several advantages, including better contrast/signal to noise ratio and increased soft tissue contrast, permitting better delineation of contrast filled structures at lower doses of iodinated contrast media. The aim of this prospective study was to assess the initial feasibility of a low dose iodinated contrast protocol, utilizing monoenergetic 40 keV reconstruction, using a dual-layer CT scanner (DLCT) for CTA in patients undergoing TAVR planning.116 consecutive TAVR patients underwent a gated chest and a non-gated CTA of the abdomen and pelvis. 40 keV virtual monoenergetic images (VMI) were reconstructed and compared with conventional polychromatic images (CI). The proximal aorta and access vessels were scored for image quality by independent experienced cardiovascular imagers.Proximal aortic image quality as assessed by signal to noise (SNR) and contrast to noise ratio (CNR), were significantly better with 40 keV VMI relative to CI (SNR 14.65 ± 7.37 vs 44.16 ± 22.39, p 0.001; CNR 15.84 ± 9.93 vs 59.8 ± 40.83, p 0.001). Aortic root dimensions were comparable between the two approaches with a bias towards higher measurements at 40 keV (Bland Altman). SNR and CNR in all access vessel segments at 40 keV were substantially better (p 0.001 for all peripheral access vessel segments) with comparable image quality.40 keV VMI with low dose contrast dose spectral imaging is feasible for comprehensive preprocedural evaluation of access vessels and measurements of aortic root dimensions in patients undergoing TAVR.

To compare objective and subjective image quality between low keV virtual monoenergetic images (VMI) of the excretory phase and conventional venous phase images derived from spectral dual-energy CT (DECT) in the assessment of urothelial carcinoma.26 consecutive patients with histologically confirmed urothelial carcinoma who received clinically indicated venous- and excretory phase abdominal CT scans were included retrospectively. Attenuation, image noise as well as signal- and contrast-to-noise-ratio (SNR, CNR) in venous and excretory phase CT and excretory phase VMI from 40 to 70 keV were obtained from ROI-based measurements in the following regions: urothelial carcinoma, liver, pancreas, renal cortex, subcutaneous fat, renal vein/artery, portal vein, urinary bladder wall, lymph nodes, prostate/uterus. Subjective vessel contrast and delineation of primary tumor manifestations and distant metastases were rated on 5-point Likert scales.In comparison to venous phase CT, attenuation and SNR in excretory phase VMI40keV were higher (p 0.001), except for liver parenchyma, where they were comparable (p = 0.07 and p = 0.17, respectively). Regarding image noise, no significant difference was found between venous phase CT and excretory phase VMI40keV (p-range: 0.08-1.00), except for liver, portal vein and renal artery, where it was lower in VMI40keV (p 0.05). CNR of urothelial carcinoma to circumjacent bladder wall was significantly higher in excretory phase VMI40keV compared to venous phase CT. Subjective vessel contrast and delineation of primary tumor and distant metastases received equivalent or higher Likert scores in excretory phase VMI40keV than in venous phase CT.This feasibility study indicates that in the assessment of urothelial carcinoma, virtual monoenergetic excretory phase images at 40 keV acquired with spectral DECT could be feasible to maintain subjective and objective image quality as provided by conventional venous phase images. Still, equivalence with regards to metastatic lesion detection requires further investigation before employing this technique in a potential signal-scan, single-bolus approach.

Purpose To evaluate the use of spectral CT for differentiation between noncalcified benign pleural lesions and pleural carcinomatosis. Materials and Methods In this retrospective study, patients who underwent contrast agent-enhanced late venous phase spectral CT of the chest between June 1, 2016, and July 1, 2018 with histopathologic and/or imaging confirmation of noncalcified pleural lesions were evaluated. Conventional images, iodine overlay (IO) images, and virtual monoenergetic images at 40 keV (hereafter, VMI40keV) were reconstructed from contrast-enhanced spectral chest CT. Four blinded radiologists determined lesion presence and indicated lesion conspicuity and diagnostic certainty. Hounsfield unit attenuation from conventional images and iodine concentration (IC) (in milligrams per milliliter) from IO images were determined. Area under the receiver operating characteristics curve determined thresholds for quantitative lesion differentiation and cutoff values were validated in an independent data set. Results Eighty-four patients were included (mean age, 66.2 years; 54 men and 30 women; 44 patients with cancer with confirmed pleural carcinomatosis and 40 patients with benign pleural lesions). The area under the receiver operating characteristics curve for IC was greater than that of conventional Hounsfield units (0.96 vs 0.91; P ≤ .05, respectively). The optimal IC threshold was 1.3 mg/mL, with comparable sensitivity and specificity when applied to the test data set. The sensitivities to depict pleural carcinomatosis with spectral reconstructions versus conventional CT were 96% (199 of 208) and 83% (172 of 208), respectively, with specificities of 84% (161 of 192) and 63% (120 of 192), respectively (P ≤ .001 each). Conclusion Compared with conventional images, spectral CT with iodine maps improved both quantitative and qualitative determination of pleural carcinomatosis versus noncalcified benign pleural lesions. © RSNA, 2019 See also the editorial by K. S. Lee and H. Y. Lee .

This study compares reduction of strong metal artifacts from large dental implants/bridges using spectral detector CT-derived virtual monoenergetic images (VMI), metal artifact reduction algorithms/reconstructions (MAR), and a combination of both methods (VMIMAR) to conventional CT images (CI). Forty-one spectral detector CT (SDCT) datasets of patients that obtained additional MAR reconstructions due to strongest artifacts from large oral implants were included. CI, VMI, MAR, and VMIMAR ranging from 70 to 200 keV (10 keV increment) were reconstructed. Objective image analyses were performed ROI-based by measurement of attenuation (HU) and standard deviation in most pronounced hypo-/hyperdense artifacts as well as artifact impaired soft tissue (mouth floor/soft palate). Extent of artifact reduction, diagnostic assessment of soft tissue, and appearance of new artifacts were rated visually by two radiologists. The hypo-/hyperattenuating artifacts showed an increase and decrease of HU values in MAR and VMIMAR (CI/MAR/VMIMAR-200keV: − 369.8 ± 239.6/− 37.3 ± 109.6/− 46.2 ± 71.0 HU, p

Objectives The well-known boost of iodine associated-attenuation in low-keV virtual monoenergetic images (VMI_low) is frequently used to improve visualization of lesions and structures taking up contrast media. This study aimed to evaluate this concept in reverse. Hence to investigate if increased attenuation within the liver allows for improved visualization of little or not-enhancing lesions. Methods A 3D-printed phantom mimicking the shape of a human liver exhibiting a lesion in its center was designed and printed. Both, parenchyma- and lesion-mimic were filled with different solutions exhibiting 80/100/120HU and 0/15/40/60HU, respectively. Further, a total of 74 contrast-enhanced studies performed on a spectral detector CT scanner (SDCT) were included in this retrospective study. Patients had MRI or follow-up proven cysts and/or hypodense metastases. VMI of 40–200 keV as well as conventional images (CI) were reconstructed. ROI were placed in lesion and parenchyma(-mimics) on CI and transferred to VMI. Signal- and contrast-to-noise ratio were calculated (S-/CNR). Further, two radiologists independently evaluated image quality. Data was statistically assessed using ANOVA or Wilcoxon-test. Results In phantoms, S/CNR was significantly higher in VMI_low. The cyst-mimic in highly attenuating parenchyma-mimic on CI yielded a CNR of 6.4 ± 0.8; using VMI_40 keV, mildly hypodense lesion-mimic in poorly attenuating parenchyma-mimic exhibited a similar CNR (5.8 ± 0.9; p ≤ 0.05). The same tendency was observed in patients (cyst in CI/metastasis in VMI_40 keV: 4.4 ± 1.2/3.9 ± 1.8; p ≤ 0.05). Qualitative analysis indicated a benefit of VMI_40 keV (p ≤ 0.05). Conclusions VMI_low from SDCT allow for an improved visualization of hypodense focal liver lesions exploiting the concept of contrast blooming in reverse.