K Laukamp

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

ObjectivesThis 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).MethodsTwenty-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.ResultsIn 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).ConclusionsMAR 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.

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

ObjectivesMagnetic 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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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

Dual-layer detector CT of the head: Initial experience in visualization of intracranial hemorrhage and hypodense brain lesions using virtual monoenergetic images

PURPOSE Retrospective comparison of diagnostic quality of virtual monoenergetic images (VMI) and conventional images (CI) reconstructed from dual-layer detector CT (DLCT) regarding intraparenchymal hemorrhage (IPH) and hypodense parenchymal lesions (HPL) of the brain. METHODS 58 patients underwent unenhanced DLCT of the head. CI and VMI ranging from 40 to 120 keV were reconstructed. Objective image quality was assessed using ROI-based measurements within IPH, HPL, grey matter, white matter and cerebrospinal fluid, from which contrast to noise ratio (CNR) was calculated. Two radiologists assessed IPH, HPL, artifacts and image noise on a 5-point Likert-scale. Statistical significance was determined using Wilcoxon rank sum test. RESULTS In comparison to conventional images, CNR of HPL to white matter was significantly increased in VMI at 120 keV (p ≤ 0.01), whereas at 40 keV, CNR to grey matter was enhanced (p ≤ 0.0001). Contrary, CNR of IPH to white matter was increased at 40 keV (p ≤ 0.01), while CNR to grey matter was improved at 120 keV (p ≤ 0.01). Subjective readings confirmed best delineation of IPH within grey matter at 120 keV. Both readers detected four additional hyperdense lesions within white and one within grey matter at 40 keV. CONCLUSIONS VMI obtained with DLCT can improve depiction of hypodense parenchymal lesions and intraparenchymal hemorrhage. The initial data show a great dependency on the type of pathology and on its location: hypodense lesions in white matter and hyperdense lesions in grey matter are better visualized in higher keV reconstructions, while hyperdense lesion in white matter and hypodense lesions in grey matter are better visualized at low keV values.

Improved depiction of atherosclerotic carotid artery stenosis in virtual monoenergetic reconstructions of venous phase dual-layer computed tomography in comparison to polyenergetic reconstructions

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.

Artifact reduction from dental implants using virtual monoenergetic reconstructions from novel spectral detector CT

OBJECTIVES Image quality in head and neck imaging is often severely hampered by artifacts arising from dental implants. This study evaluates metal artifact (MA) reduction using virtual monoenergetic images (VMI) compared to conventional CT images (CI) from spectral-detector computed tomography (SDCT). METHODS 38 consecutive patients with dental implants were included in this retrospective study. All examinations were performed using a SDCT (IQon, Philips, Best, The Netherlands). Images were reconstructed as conventional images (CI) and as VMI in a range of 40-200 keV (10 keV increment). Quantitative image analysis was performed ROI-based by measurement of attenuation (HU) and standard deviation in most pronounced hypo- and hyperdense artifact, fat and soft tissue with presence of artifacts. Qualitatively, extent of artifact reduction, assessment of soft palate and cheeks were rated on 5-point Likert-scales by two radiologists. Statistical data evaluation included ANOVA and Wilcoxon-test with correction for multiple comparisons; interrater-agreement was determined by intraclass-correlation coefficient (ICC). RESULTS The hypo- and hyperattenuating artifacts showed an increase and decrease of HU-values in VMIhigh (CI/VMI200 keV: -218.7/-174.4 HU, p = 0.1; and 309.8/119.2, p ≤ 0.05, respectively). Artifacts in the fat, as depicted by image noise did also decrease in VMIhigh (CI/VMI200 keV: 23.9/16.4, p ≤ 0.05). Qualitatively, hyperdense artifacts were decreased significantly in VMI ≥100 keV (e.g. CI/VMI200 keV: 2(1-3)/3(1-5), p ≤ 0.05). Artifact reduction resulted in improved assessment of the soft palate and cheeks (e.g. CI/VMI200 keV: 2(1-4)/3(1-5) and 2(1-5)/3(1-5), p ≤ 0.05). Overall interrater agreement was good (ICC = 0.77). CONCLUSIONS Virtual monoenergetic images from SDCT reduce metal artifacts from dental implants and improve diagnostic assessment of surrounding soft tissue.