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.

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.

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.

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.

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.

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.

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.

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.

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."

MRI Follow-up of Astrocytoma: Automated Coregistration and Color-Coding of FLAIR Sequences Improves Diagnostic Accuracy With Comparable Reading Time.

BACKGROUND: MRI follow-up is widely used for longitudinal assessment of astrocytoma, yet reading can be tedious and error-prone, in particular when changes are subtle. PURPOSE/HYPOTHESIS: To determine the effect of automated, color-coded coregistration (AC) of fluid attenuated inversion recovery (FLAIR) sequences on diagnostic accuracy, certainty, and reading time compared to conventional follow-up MRI assessment of astrocytoma patients. STUDY TYPE: Retrospective. POPULATION: In all, 41 patients with neuropathologically confirmed astrocytoma. FIELD STRENGTH/SEQUENCE: 1.0-3.0T/FLAIR ASSESSMENT: The presence or absence of tumor progression was determined based on FLAIR sequences, contrast-enhanced T1 sequences, and clinical data. Three radiologists assessed 47 MRI study pairs in a conventional reading (CR) and in a second reading supported by AC after 6 weeks. Readers determined the presence/absence of tumor progression and indicated diagnostic certainty on a 5-point Likert scale. Reading time was recorded by an independent assessor. STATISTICAL TESTS: The Wilcoxon test was used to assess reading time and diagnostic certainty. Differences in diagnostic accuracy, sensitivity, and specificity were analyzed with the McNemar mid-p test. RESULTS: Readers attained significantly higher overall sensitivity (0.86 vs. 0.75; P < 0.05) and diagnostic accuracy (0.84 vs. 0.73; P < 0.05) for detection of progressive nonenhancing tumor burden when using AC compared to CR. There was a strong trend towards higher specificity within the AC-augmented reading, yet without statistical significance (0.83 vs. 0.71; P = 0.08). Sensitivity for unequivocal disease progression was similarly high in both approaches (AC: 0.94, CR: 0.92), while for marginal disease progressions, it was significantly higher in AC (AC: 0.78, CR: 0.58; P < 0.05). Reading time including application loading time was comparable (AC: 38.1 ± 16.8 sec, CR: 36.0 ± 18.9 s; P = 0.25). DATA CONCLUSION: Compared to CR, AC improves comparison of FLAIR signal hyperintensity at MRI follow-up of astrocytoma patients, allowing for a significantly higher diagnostic accuracy, particularly for subtle disease progression at a comparable reading time. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY STAGE: 6 J. Magn. Reson. Imaging 2020;52:1197-1206.

Clinical application of free-breathing 3D whole heart late gadolinium enhancement cardiovascular magnetic resonance with high isotropic spatial resolution using Compressed SENSE.

BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) represents the gold standard for assessment of myocardial viability. The purpose of this study was to investigate the clinical potential of Compressed SENSE (factor 5) accelerated free-breathing three-dimensional (3D) whole heart LGE with high isotropic spatial resolution (1.4 mm3 acquired voxel size) compared to standard breath-hold LGE imaging. METHODS: This was a retrospective, single-center study of 70 consecutive patients (45.8 ± 18.1 years, 27 females; February-November 2019), who were referred for assessment of left ventricular myocardial viability and received free-breathing and breath-hold LGE sequences at 1.5 T in clinical routine. Two radiologists independently evaluated global and segmental LGE in terms of localization and transmural extent. Readers scored scans regarding image quality (IQ), artifacts, and diagnostic confidence (DC) using 5-point scales (1 non-diagnostic-5 excellent/none). Effects of heart rate and body mass index (BMI) on IQ, artifacts, and DC were evaluated with ordinal logistic regression analysis. RESULTS: Global LGE (n = 33) was identical for both techniques. Using free-breathing LGE (average scan time: 04:33 ± 01:17 min), readers detected more hyperenhanced lesions (28.2% vs. 23.5%, P < .05) compared to breath-hold LGE (05:15 ± 01:23 min, P = .0104), pronounced at subepicardial localization and for 1-50% of transmural extent. For free-breathing LGE, readers graded scans with good/excellent IQ in 80.0%, with low-impact/no artifacts in 78.6%, and with good/high DC in 82.1% of cases. Elevated BMI was associated with increased artifacts (P = .0012) and decreased IQ (P = .0237). Increased heart rate negatively influenced artifacts (P = .0013) and DC (P = .0479) whereas IQ (P = .3025) was unimpaired. CONCLUSIONS: In a clinical setting, free-breathing Compressed SENSE accelerated 3D high isotropic spatial resolution whole heart LGE provides good to excellent image quality in 80% of scans independent of heart rate while enabling improved depiction of small and particularly non-ischemic hyperenhanced lesions in a shorter scan time than standard breath-hold LGE.

Correction to: Imaging of the pulmonary vasculature in congenital heart disease without gadolinium contrast: Intraindividual comparison of a novel Compressed SENSE accelerated 3D modified REACT with 4D contrast-enhanced magnetic resonance angiography.

The original publication of this article [1], contained graphical errors in Figs. 1 and 2. This does not impact the display of the mean differences of the Bland-Altman plots. The updated figures (Fig. 1 and Fig. 2) are published in this correction article.

Imaging of the pulmonary vasculature in congenital heart disease without gadolinium contrast: Intraindividual comparison of a novel Compressed SENSE accelerated 3D modified REACT with 4D contrast-enhanced magnetic resonance angiography.

BACKGROUND: Patients with Congenital heart disease (CHD) require repetitive imaging of the pulmonary vasculature throughout their life. In this study, we compared a novel Compressed SENSE accelerated (factor 9) electrocardiogram (ECG)- and respiratory-triggered 3D modified Relaxation-Enhanced Angiography without Contrast and Triggering (modified REACT-non-contrast-enhanced magnetic resonance angiography (modified REACT-non-CE-MRA)) with standard non-ECG-triggered time-resolved 4D CE-MRA for imaging of the pulmonary arteries and veins in patients with CHD. METHODS: This retrospective analysis of 25 patients (June 2018-April 2019) with known or suspected CHD was independently conducted by two radiologists executing measurements on modified REACT-non-CE-MRA and 4D CE-MRA on seven dedicated points (inner edge): Main pulmonary artery (MPA), right and left pulmonary artery, right superior and inferior pulmonary vein, left superior (LSPV) and inferior pulmonary vein. Image quality for arteries and veins was evaluated on a four-point scale in consensus. RESULTS: Twenty-three of the 25 included patients presented a CHD. There was a high interobserver agreement for both methods of imaging at the pulmonary arteries (ICC ≥ 0.96); at the pulmonary veins, modified REACT-non-CE-MRA showed a slightly higher agreement, pronounced at LSPV (ICC 0.946 vs. 0.895). Measurements in 4D CE-MRA showed higher diameter values compared to modified REACT-non-CE-MRA, at the pulmonary arteries reaching significant difference (e.g. MPA: mean 0.408 mm, p = 0.002). Modified REACT-non-CE-MRA (average acquisition time 07:01 ± 02:44 min) showed significant better image quality than 4D CE-MRA at the pulmonary arteries (3.84 vs. 3.32, p < 0.001) and veins (3.32 vs. 2.72, p = 0.015). CONCLUSIONS: Compressed SENSE accelerated (factor 9) ECG- and respiratory-triggered 3D modified REACT-non-CE-MRA allows for reliable and fast imaging of the pulmonary arteries and veins with higher image quality and slightly higher interobserver agreement than 4D CE-MRA without contrast agent and associated disadvantages. Therefore, it represents a clinically suitable technique for patients requiring repetitive imaging of the pulmonary vasculature, e.g. patients with CHD.

Pulmonary Embolism Versus Mimics on Dual-energy Spectral Computed Tomography: An Algorithmic Approach.

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.

Virtual non-contrast for evaluation of liver parenchyma and vessels: results from 25 patients using multi-phase spectral-detector CT.

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.

Dual-Energy CT-derived Iodine Maps: Use in Assessing Pleural Carcinomatosis.

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 .

Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.

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.

Metal artifacts in patients with large dental implants and bridges: combination of metal artifact reduction algorithms and virtual monoenergetic images provides an approach to handle even strongest artifacts.

OBJECTIVES: 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). METHODS: 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. RESULTS: 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 < 0.001 and 274.8 ± 170.2/51.3 ± 150.8/36.6 ± 56.0, p < 0.001, respectively). Higher keV values in hyperdense artifacts allowed for additional artifact reduction; however, this trend was not significant. Artifacts in soft tissue were reduced significantly by MAR and VMIMAR. Visually, high-keV VMI, MAR, and VMIMAR reduced artifacts and improved diagnostic assessment of soft tissue. Overcorrection/new artifacts were reported that mostly did not hamper diagnostic assessment. Overall interrater agreement was excellent (ICC = 0.85). CONCLUSIONS: In the presence of strong artifacts due to large oral implants, MAR is a powerful mean for artifact reduction. For hyperdense artifacts, MAR should be supplemented by VMI ranging from 140 to 200 keV. This combination yields optimal artifact reduction and improves the diagnostic image assessment in imaging of the head and neck. KEY POINTS: • Large oral implants can cause strong artifacts. • MAR is a powerful tool for artifact reduction considering such strong artifacts. • Hyperdense artifact reduction is supplemented by VMI of 140-200 keV from SDCT.

Correction to: CT metal artifacts in patients with total hip replacements: for artifact reduction monoenergetic reconstructions and post-processing algorithms are both efficient but not similar.

The original version of this article, published on 03 May 2018, unfortunately contained a mistake. The following correction has therefore been made in the original.

CT metal artifacts in patients with total hip replacements: for artifact reduction monoenergetic reconstructions and post-processing algorithms are both efficient but not similar.

OBJECTIVES: 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). METHODS: 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. RESULTS: 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, p<0.001; MAR 16.5±14.9 mm, p<0.001; MA density: CI -412.1±204.5 HU, VMI -279.7±283.7 HU; p<0.01; MAR -116.74±105.6 HU, p<0.001). 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 (p<0.05), whereas VMI were superior for depiction of bone (p<0.05). Visual assessment confirmed that VMI and MAR improve artifact reduction and image quality (p<0.001). CONCLUSIONS: 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. KEY POINTS: • 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.