Metal artifact reduction in patients with total hip replacements: evaluation of clinical photon counting CT using virtual monoenergetic images.

OBJECTIVES: To investigate photon-counting CT (PCCT)-derived virtual monoenergetic images (VMI) for artifact reduction in patients with unilateral total hip replacements (THR). METHODS: Forty-two patients with THR and portal-venous phase PCCT of the abdomen and pelvis were retrospectively included. For the quantitative analysis, region of interest (ROI)-based measurements of hypodense and hyperdense artifacts, as well as of artifact-impaired bone and the urinary bladder, were conducted, and corrected attenuation and image noise were calculated as the difference of attenuation and noise between artifact-impaired and normal tissue. Two radiologists qualitatively evaluated artifact extent, bone assessment, organ assessment, and iliac vessel assessment using 5-point Likert scales. RESULTS: VMI110keV yielded a significant reduction of hypo- and hyperdense artifacts compared to conventional polyenergetic images (CI) and the corrected attenuation closest to 0, indicating best possible artifact reduction (hypodense artifacts: CI: 237.8 ± 71.4 HU, VMI110keV: 8.5 ± 122.5 HU; p < 0.05; hyperdense artifacts: CI: 240.6 ± 40.8 HU vs. VMI110keV: 13.0 ± 110.4 HU; p < 0.05). VMI110keV concordantly provided best artifact reduction in the bone and bladder as well as the lowest corrected image noise. In the qualitative assessment, VMI110keV received the best ratings for artifact extent (CI: 2 (1-3), VMI110keV: 3 (2-4); p < 0.05) and bone assessment (CI: 3 (1-4), VMI110keV: 4 (2-5); p < 0.05), whereas organ and iliac vessel assessments were rated highest in CI and VMI70keV. CONCLUSIONS: PCCT-derived VMI effectively reduce artifacts from THR and thereby improve assessability of circumjacent bone tissue. VMI110keV yielded optimal artifact reduction without overcorrection, yet organ and vessel assessments at that energy level and higher were impaired by loss of contrast. CLINICAL RELEVANCE STATEMENT: PCCT-enabled artifact reduction is a feasible method for improving assessability of the pelvis in patients with total hip replacements at clinical routine imaging. KEY POINTS: • Photon-counting CT-derived virtual monoenergetic images at 110 keV yielded best reduction of hyper- and hypodense artifacts, whereas higher energy levels resulted in artifact overcorrection. • The qualitative artifact extent was reduced best in virtual monoenergetic images at 110 keV, facilitating an improved assessment of the circumjacent bone. • Despite significant artifact reduction, assessment of pelvic organs as well as vessels did not profit from energy levels higher than 70 keV, due to the decline in image contrast.

Virtual non-contrast reconstructions improve differentiation between vascular enhancement and calcifications in stereotactic planning CT scans of cystic intracranial tumors.

PURPOSE: To assess the diagnostic value of spectral-detector CT (SDCT) derived virtual non-contrast images (VNC) for differentiation between vascular enhancement and wall calcifications of cystic intracranial tumors in contrast-enhanced stereotactic planning examinations. METHOD: 48 patients with cystic intracranial tumors who underwent stereotactic SDCT examinations between 02/2017 and 02/2020 were retrospectively included. In each patient, two separate hyperattenuating structures along the cyst wall were defined as either enhancement or calcification, respectively, using reference MRI examinations. Quantitative analysis was performed ROI-based in conventional images (CI) and VNC. In the subjective analysis, two radiologists diagnosed the predefined peri-cystic structures in binary decisions as either enhancement or calcification using CI and the combination of CI and VNC, and rated diagnostic confidence, image noise and removal of iodine in VNC. Moreover, a potential diagnostic benefit of VNC was indicated. RESULTS: Attenuation in CI was higher as compared to VNC across all assessed ROI (all p < 0.01). In VNC, CNR between calcification and white matter was significantly higher as compared to CNR between vascular enhancement and white matter (2.6 vs 1.3, p < 0.01), while there was no significant difference in CI. In the qualitative assessment, diagnostic accuracy was significantly higher using both VNC and CI compared to using CI alone. Raters reported less image noise in VNC as compared to CI. An additional diagnostic benefit of VNC was indicated in 84.4 % of all cases. CONCLUSIONS: SDCT-derived VNC images facilitate differentiation between peri-cystic contrast enhancement in blood vessels and calcifications in stereotactic planning scans of cystic intracranial tumors.

Differentiation of Intracerebral Tumor Entities with Quantitative Contrast Attenuation and Iodine Mapping in Dual-Layer Computed Tomography.

Purpose: To investigate if quantitative contrast enhancement and iodine mapping of common brain tumor (BT) entities may correctly differentiate between tumor etiologies in standardized stereotactic CT protocols. Material and Methods: A retrospective monocentric study of 139 consecutive standardized dual-layer dual-energy CT (dlDECT) scans conducted prior to the stereotactic needle biopsy of untreated primary brain tumor lesions. Attenuation of contrast-enhancing BT was derived from polyenergetic images as well as spectral iodine density maps (IDM) and their contrast-to-noise-ratios (CNR) were determined using ROI measures in contrast-enhancing BT and healthy contralateral white matter. The measures were correlated to histopathology regarding tumor entity, isocitrate dehydrogenase (IDH) and MGMT mutation status. Results: The cohort included 52 female and 76 male patients, mean age of 59.4 (±17.1) years. Brain lymphomas showed the highest attenuation (IDM CNR 3.28 ± 1,23), significantly higher than glioblastoma (2.37 ± 1.55, p < 0.005) and metastases (1.95 ± 1.14, p < 0.02), while the differences between glioblastomas and metastases were not significant. These strongly enhancing lesions differed from oligodendroglioma and astrocytoma (Grade II and III) that showed IDM CNR in the range of 1.22−1.27 (±0.45−0.82). Conventional attenuation measurements in DLCT data performed equally or slightly superior to iodine density measurements. Conclusion: Quantitative attenuation and iodine density measurements of contrast-enhancing brain tumors are feasible imaging biomarkers for the discrimination of cerebral tumor lesions but not specifically for single tumor entities. CNR based on simple HU measurements performed equally or slightly superior to iodine quantification.

Imaging of the extracranial internal carotid artery in acute ischemic stroke: assessment of stenosis, plaques, and image quality using relaxation-enhanced angiography without contrast and triggering (REACT).

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, P<0.001) and plaques of hyperintense signal (n=12.3 vs. 5.7, P=0.02) with higher diagnostic confidence [2.8±0.5, 3 (3-3) vs. 2.6±0.7, 3 (2-3), P<0.001] than CE-MRA. After analyzing a total of 1,260 segments, the vessel quality of all segments combined [4.61±0.66 vs. 4.58±0.68, 5 (4-5) vs. 5 (4-5), P=0.0299] and artifacts [4.51±0.70 vs. 4.44±0.73, 5 (4-5) vs. 5 (4-5), P>0.05] 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<0.001]. Conclusions: Without the use of gadolinium-based contrast agents or triggering, REACT provides a high sensitivity and specificity for extracranial ICA stenosis and a potential improved depiction of adjacent plaques while yielding to CE-MRA comparable vessel quality in a large patient cohort with AIS.

Imaging of the Left Atrial Appendage Before Occluder Device Placement: Evaluation of Virtual Monoenergetic Images in a Single-Bolus Dual-Phase Protocol.

PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Virtual Non-Contrast versus True Non-Contrast Computed Tomography: Initial Experiences with a Photon Counting Scanner Approved for Clinical Use.

The present study evaluates the diagnostic reliability of virtual non-contrast (VNC) images acquired with the first photon counting CT scanner that is approved for clinical use by comparing quantitative image properties of VNC and true non-contrast (TNC) images. Seventy-two patients were retrospectively enrolled in this study. VNC images reconstructed from the arterial (VNCa) and the portalvenous (VNCv) phase were compared to TNC images. In addition, consistency between VNCa and VNCv images was evaluated. Regions of interest (ROI) were drawn in the following areas: liver, spleen, kidney, aorta, muscle, fat and bone. Comparison of VNCa and VNCv images revealed a mean offset of less than 4 HU in all tissues. The greatest difference between TNC and VNC images was found in spongious bone (VNCv 86.13 HU ± 28.44, p < 0.001). Excluding measurements in spongious bone, differences between TNC and VNCv of 10 HU or less were found in 40% (VNCa 36%) and differences of 15 HU or less were found in 72% (VNCa 68%) of all measurements. The underlying algorithm for the subtraction of iodine works in principle but requires adjustments. Until then, special caution should be exercised when using VNC images in routine clinical practice.

Generally applicable window settings of low-keV virtual monoenergetic reconstructions in dual-layer CT-angiography of the head and neck.

BACKGROUND: Increased vessel contrast in low-keV virtual monoenergetic images (VMI) in spectral detector CT angiography of the head and neck requires adaption of window settings. Aim of this study was to define generally applicable window settings of low-keV VMI. METHODS: Two radiologists determined ideal subjective window settings for VMI40-70 keV in 54 patients. To obtain generally applicable window settings, center and width values were modeled against the attenuation of the internal carotid artery (HUICA). This modeling was performed with and without respect to keV. Subsequently, image quality of VMI40-70 keV was assessed using the model-based determined window settings. RESULTS: With decreasing keV values, HUICA increased significantly in comparison to conventional images (CI) (P<0.05 for 40-60 keV). No significant differences between modelled and individually recorded window settings were found confirming validity of the obtained models (P values: 0.2-1.0). However, modelling with respect to keV was marginally less precise. CONCLUSIONS: Window settings of low-keV VMI can be semi-automatically determined in dependency of the ICA attenuation in spectral detector CTA of the head and neck. The reported models are a promising tool to leverage the improved image quality of these images in clinical routine.

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. PURPOSE: To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. STUDY TYPE: Retrospective. POPULATION: Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. ASSESSMENT: A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1 CE served as the reference standard. STATISTICAL TESTS: Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value <0.05 was considered statistically significant. RESULTS: In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 ± 4.24 cm3 ), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 ± 0.04 cm3 ) than detected BMs (0.96 ± 2.4 cm3 ). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. DATA CONCLUSION: Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. Level of Evidence 3 Technical Efficacy Stage 2.

Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage.

PURPOSE: To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH). METHODS: Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm3, 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared. RESULTS: In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1: 91.2%, reader 2: 86.5%, and reader 3: 86.5%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6%, reader 2: 97.6%,and reader 3: 96.0%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM). CONCLUSION: Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.

Abdominal vessel depiction on virtual triphasic spectral detector CT: initial clinical experience.

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 < 0.001) except the inferior vena cava (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.

Reduction of CT artifacts from cardiac implantable electronic devices using a combination of virtual monoenergetic images and post-processing algorithms.

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 VMIMAR at 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.

Trends in imaging utilization for small cell lung cancer: a decision tree analysis of the NCCN guidelines.

PURPOSE: To investigate the differences in small cell lung cancer (SCLC) diagnostic imaging utilization relative to National Comprehensive Cancer Network (NCCN) guidelines. METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Impact of the weekend effect on outcome after microsurgical clipping of ruptured intracranial aneurysms.

BACKGROUND: The "weekend effect" describes the assumption that weekend and/or on-call duty admission of emergency patients is associated with increased morbidity and mortality rates. For aneurysmal subarachnoid hemorrhage, we investigated, whether presentation out of regular working hours and microsurgical clipping at nighttime correlates with worse patient outcome. METHODS: This is a retrospective review of consecutive patients that underwent microsurgical clipping of an acutely ruptured aneurysm at our institution between 2010 and 2019. Patients admitted during (1) regular working hours (Monday-Friday, 08:00-17:59) and (2) on-call duty and microsurgical clipping performed during (a) daytime (Monday-Sunday, 08:00-17:59) and (b) nighttime were compared regarding the following outcome parameters: operation time, treatment-related complications, vasospasm, functional outcome, and angiographic results. RESULTS: Among 157 enrolled patients, 104 patients (66.2%) were admitted during on-call duty and 48 operations (30.6%) were performed at nighttime. Admission out of regular hours did not affect cerebral infarction (p = 0.545), mortality (p = 0.343), functional outcome (p = 0.178), and aneurysm occlusion (p = 0.689). Microsurgical clipping at nighttime carried higher odds of unfavorable outcome at discharge (OR: 2.3, 95%CI: 1.0-5.1, p = 0.039); however, there were no significant differences regarding the remaining outcome parameters. After multivariable adjustment, clipping at nighttime did not remain as independent prognosticator of short-term outcome (OR: 2.1, 95%CI: 0.7-6.2, p = 0.169). CONCLUSIONS: Admission out of regular working hours and clipping at nighttime were not independently associated with poor outcome. The adherence to standardized treatment protocols might mitigate the "weekend effect."

CT artifacts after contrast media injection in chest imaging: evaluation of post-processing algorithms, virtual monoenergetic images and their combination for artifact reduction.

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<0.001); hyperdense artifacts, 164.2±51.1/82.1±73.2/7.9±34.7/(-)17.3±50.7 HU; P<0.001). Artifacts impairing surrounding muscle and arteries were also reduced by all three approaches. In visual assessment, ARA, VMI ≥100 keV, and VMIARA between 70-200 keV also showed significant artifact reduction and improved assessment; however, for assessment of arteries improvement was not significant using ARA alone. New artifacts were reported, particularly at higher keV-values. CONCLUSIONS: In presence of perivenous-artifacts, ARA, VMI and their combination allow for significant artifact reduction; however, their combination and VMI as a standalone approach yielded best results and should therefore be used, if available.

Woven Endobridge Embolization Versus Microsurgical Clipping for Unruptured Anterior Circulation Aneurysms: A Propensity Score Analysis.

BACKGROUND: Intrasaccular flow-disruption represents a new paradigm in endovascular treatment of wide-necked bifurcation aneurysms. OBJECTIVE: To retrospectively compare Woven Endobridge (WEB) embolization with microsurgical clipping for unruptured anterior circulation aneurysms using propensity score adjustment. METHODS: A total of 63 patients treated with WEB and 103 patients treated with clipping were compared based on the intention-to-treat principle. The primary outcome measures were immediate technical treatment success, major adverse events, and 6-mo complete aneurysm occlusion. RESULTS: The technical success rates were 83% for WEB and 100% for clipping. Procedure-related complications occurred more often in the clipping group (13%) than the WEB group (6%, adjusted P < .01). However, the rates of major adverse events were comparable in both groups (WEB: 3%, clip: 4%, adjusted P = .53). At the 6-mo follow-up, favorable functional outcomes were achieved in 98% of the WEB embolization group and 99% of the clipping group (adjusted P = .19). Six-month complete aneurysm occlusion was obtained in 75% of the WEB group and 94% of the clipping group (adjusted P < .01). CONCLUSION: Microsurgical clipping was associated with higher technical success and complete occlusion rates, whereas WEB had a lower complication rate. Favorable functional outcomes were achieved in ≥98% of both groups. The decision to use a specific treatment modality should be made on an individual basis and in accordance with the patient's preferences.

Evaluation of equivocal small cystic pancreatic lesions with spectral-detector computed tomography.

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 < 0.001) and confidence regarding presence of cystic nature (CI 2 [1-2], SI 3 [3-3], P < 0.001). Inter-observer agreement depicted by intraclass correlation coefficient improved considerably from 0.51 with only CI to 0.85 when the combination with SI was used. Further, VMI displayed significantly higher signal-to-noise (CI 1.2 ± 0.8, VMI 3.2 ± 1.8, P < 0.001) and contrast-to-noise ratios (CI 2.6 ± 0.8, VMI 4.7 ± 1.9). CONCLUSION: Compared to CI alone, combination with SI significantly improves visualization and confidence in evaluation of small equivocal cystic pancreatic lesions.

Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

Virtual Noncontrast Images From Portal Venous Phase Spectral-Detector CT Acquisitions for Adrenal Lesion Characterization.

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.

Relaxation-Enhanced Angiography Without Contrast and Triggering (REACT) for Fast Imaging of Extracranial Arteries in Acute Ischemic Stroke at 3 T.

PURPOSE: To evaluate a novel flow-independent 3D isotropic REACT sequence compared with CE-MRA for the imaging of extracranial arteries in acute ischemic stroke (AIS). METHODS: This was a retrospective study of 35 patients who underwent a stroke protocol at 3 T including REACT (fixed scan time: 2:46 min) and CE-MRA of the extracranial arteries. Three radiologists evaluated scans regarding vessel delineation, signal, and contrast and assessed overall image noise and artifacts using 5-point scales (5: excellent delineation/no artifacts). Apparent signal- and contrast-to-noise ratios (aSNR/aCNR) were measured for the common carotid artery (CCA), internal carotid artery (ICA, C1 segment), and vertebral artery (V2 segment). Two radiologists graded the degree of proximal ICA stenosis. RESULTS: Compared to REACT, CE-MRA showed better delineation for the CCA and ICA (C1 and C2 segments) (median 5, range 2-5 vs. 4, range 3-5; P < 0.05). For the ICA (C1 and C2 segments), REACT provided a higher signal (5, range 3-5; P < 0.05/4.5, range 3-5; P > 0.05 vs. 4, range 2-5) and contrast (5, range 3-5 vs. 4, range 2-5; P > 0.05) than CE-MRA. The remaining segments of the blood-supplying vessels showed equal medians. There was no significant difference regarding artifacts, whereas REACT provided significantly lower image noise (4, range 3-5 vs. 4 range 2-5; P < 0.05) with a higher aSNR (P < 0.05) and aCNR (P < 0.05) for all vessels combined. For clinically relevant (≥50%) ICA stenosis, REACT achieved a detection sensitivity of 93.75% and a specificity of 100%. CONCLUSION: Given its fast acquisition, comparable image quality to CE-MRA and high sensitivity and specificity for the detection of ICA stenosis, REACT was proven to be a clinically applicable method to assess extracranial arteries in AIS.

Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning.

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 < 0.0001; core: r = 0.86, P < 0.0001). Performance of automated segmentations was comparable between pretreatment and follow-up scans without significant differences (TTV: P = 0.242, core: P = 0.177). DATA CONCLUSION: In clinical MRI scans, a DLM initially trained on gliomas provides segmentation of PCNSL comparable to manual segmentation, despite its complex and multifaceted appearance. Segmentation performance was high in both initial and follow-up scans, suggesting its potential for application in longitudinal tumor imaging. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.