ResearchPad - imaging https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings]]> https://www.researchpad.co/article/elastic_article_15766 Genuine interareal cross-frequency coupling (CFC) can be identified from human resting state activity using magnetoencephalography, stereoelectroencephalography, and novel network approaches. CFC couples slow theta and alpha oscillations to faster oscillations across brain regions.

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<![CDATA[Spectral-power associations reflect amplitude modulation and within-frequency interactions on the sub-second timescale and cross-frequency interactions on the seconds timescale]]> https://www.researchpad.co/article/elastic_article_15765 We investigated the global structure of intrinsic cross-frequency dynamics by systematically examining power-based temporal associations among a broad range of oscillation frequencies both within and across EEG-based current sources (sites). We focused on power-based associations that could reveal unique timescale dependence independently of interacting frequencies. Large spectral-power fluctuations across all sites occurred at two characteristic timescales, sub-second and seconds, yielding distinct patterns of cross-frequency associations. On the fast sub-second timescale, within-site (local) associations were consistently between pairs of βγ frequencies differing by a constant Δf (particularly Δf ~ 10 Hz at posterior sites and Δf ~ 16 Hz at lateral sites) suggesting that higher-frequency oscillations are organized into Δf amplitude-modulated packets, whereas cross-site (long-distance) associations were all within-frequency (particularly in the >30 Hz and 6–12 Hz ranges, suggestive of feedforward and feedback interactions). On the slower seconds timescale, within-site (local) associations were characterized by a broad range of frequencies selectively associated with ~10 Hz at posterior sites and associations among higher (>20 Hz) frequencies at lateral sites, whereas cross-site (long-distance) associations were characterized by a broad range of frequencies at posterior sites selectively associated with ~10 Hz at other sites, associations among higher (>20 Hz) frequencies among lateral and anterior sites, and prevalent associations at ~10 Hz. Regardless of timescale, within-site (local) cross-frequency associations were weak at anterior sites indicative of frequency-specific operations. Overall, these results suggest that the fast sub-second-timescale coordination of spectral power is limited to local amplitude modulation and insulated within-frequency long-distance interactions (likely feedforward and feedback interactions), while characteristic patterns of cross-frequency interactions emerge on the slower seconds timescale. The results also suggest that the occipital α oscillations play a role in organizing higher-frequency oscillations into ~10 Hz amplitude-modulated packets to communicate with other regions. Functional implications of these timescale-dependent cross-frequency associations await future investigations.

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<![CDATA[Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review]]> https://www.researchpad.co/article/elastic_article_15761 To present the findings of 21 coronavirus disease 2019 (COVID-19) cases from two Chinese centers with CT and chest radiographic findings, as well as follow-up imaging in five cases.Materials and MethodsThis was a retrospective study in Shenzhen and Hong Kong. Patients with COVID-19 infection were included. A systematic review of the published literature on radiologic features of COVID-19 infection was conducted.ResultsThe predominant imaging pattern was of ground-glass opacification with occasional consolidation in the peripheries. Pleural effusions and lymphadenopathy were absent in all cases. Patients demonstrated evolution of the ground-glass opacities into consolidation and subsequent resolution of the airspace changes. Ground-glass and consolidative opacities visible on CT are sometimes undetectable on chest radiography, suggesting that CT is a more sensitive imaging modality for investigation. The systematic review identified four other studies confirming the findings of bilateral and peripheral ground glass with or without consolidation as the predominant finding at CT chest examinations.ConclusionPulmonary manifestation of COVID-19 infection is predominantly characterized by ground-glass opacification with occasional consolidation on CT. Radiographic findings in patients presenting in Shenzhen and Hong Kong are in keeping with four previous publications from other sites.© RSNA, 2020See editorial by Kay and Abbara in this issue. ]]> <![CDATA[Hemisphere opposite to vascular trunk deviation is earlier affected by glaucomatous damage in myopic high-tension glaucoma]]> https://www.researchpad.co/article/elastic_article_15760 To investigate whether the position of the central vascular trunk, as a surrogate of lamina cribrosa (LC) shift, is associated with the initial hemisphere of visual field defect in myopic high-tension glaucoma (HTG) eyes.MethodsThe deviation of the central vascular trunk was measured from the center of the Bruch’s membrane opening (BMO), which was delineated by OCT imaging. The angular deviation was measured with the horizontal nasal midline as 0° and the superior location as a positive value. The initial hemisphere developing visual field defect was defined as three connected abnormal points (having a P value with less than 0.5% probability of being normal) appearing in only one hemisphere in pattern deviation plots. If those points were observed in both hemispheres initially, the eye was classified as bi-hemispheric visual field defect.ResultsInitially, 36 eyes (44%) had superior visual field defects, 27 (33%) inferior visual field defects, and 18 (22%) bi-hemispheric visual field defects. After a mean follow-up of 5 years, the number of bi-hemispheric visual field defects had increased to 34 (42%). A logistic regression analysis revealed that inferior deviation of vascular trunk was the only factor associated with initial inferior visual field defect (P = 0.001), while initial bi-hemispheric visual field defects were associated with worse mean deviation at initial visits (P<0.001). A conditional inference tree analysis showed that both the angular deviation (P<0.001) and initial mean deviation (P = 0.025) determined the initial hemispheres developing visual field defect.ConclusionsAlthough both hemispheres were involved as glaucoma progression, the axons on the side counter to the vascular trunk deviation were damaged earlier in HTG. This finding implies the LC shift could add additional stress to axons exposed to high intraocular pressure. ]]> <![CDATA[Association between single nucleotide polymorphisms (SNPs) of IL1, IL12, IL28 and TLR4 and symptoms of congenital cytomegalovirus infection]]> https://www.researchpad.co/article/elastic_article_15749 Congenital cytomegalovirus (cCMV) infection is the most common intrauterine infection. A non-specific immune response is the first line of host defense mechanism against human cytomegalovirus (HCMV). There is limited data on associations between Single Nucleotide Polymorphisms (SNPs) in genes involving innate immunity and the risk and clinical manifestation of cCMV infection. The aim of the study was to investigate association between selected SNPs in genes encoding cytokines and cytokine receptors, and predisposition to cCMV infection including symptomatic course of disease and symptoms. A panel of eight SNPs: IL1B rs16944, IL12B rs3212227, IL28B rs12979860, CCL2 rs1024611, DC-SIGN rs735240, TLR2 rs5743708, TLR4 rs4986791, TLR9 rs352140 was analyzed in 233 infants (92 cCMV-infected and 141 healthy controls). Associations between genotyped SNPs and predisposition to cCMV infection and symptoms were analyzed. The association analysis was performed using SNPStats software. No statistically significant association was found between any genotyped SNPs and predisposition to cCMV infection and symptomatic course of disease. In relation to particular symptoms, polymorphism of IL12B rs3212227 was linked to decreased risk of prematurity (OR = 0.37;95%CI,0.14–0.98;p = 0.025), while polymorphism of IL1B rs16944 was linked to reduced risk of splenomegaly (OR = 0.36;95%CI,0.14–0.98; p = 0.034) in infants with cCMV infection. An increased risk of thrombocytopenia was associated with IL28B rs12979860 polymorphism (OR = 2.55;95%CI,1.03–6.32;p = 0.042), while hepatitis was associated with SNP of TLR4rs4986791 (OR = 7.80;95%CI,1.49–40,81; p = 0.024). This is the first study to demonstrate four new associations between SNPs in selected genes (IL1B, IL12B, IL28B, TLR4) and particular symptoms in cCMV disease. Further studies on the role of SNPs in the pathogenesis of cCMV infection and incorporation of selected SNPs in the clinical practice might be considered in the future.

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<![CDATA[AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT]]> https://www.researchpad.co/article/elastic_article_15707 COVID-19 and pneumonia of other etiology share similar CT characteristics, contributing to the challenges in differentiating them with high accuracy.PurposeTo establish and evaluate an artificial intelligence (AI) system in differentiating COVID-19 and other pneumonia on chest CT and assess radiologist performance without and with AI assistance.Methods521 patients with positive RT-PCR for COVID-19 and abnormal chest CT findings were retrospectively identified from ten hospitals from January 2020 to April 2020. 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia on chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by two-layer fully-connected neural network to pool slices together. Our final cohort of 1,186 patients (132,583 CT slices) was divided into training, validation and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance on separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance.ResultsOur final model achieved a test accuracy of 96% (95% CI: 90-98%), sensitivity 95% (95% CI: 83-100%) and specificity of 96% (95% CI: 88-99%) with Receiver Operating Characteristic (ROC) AUC of 0.95 and Precision-Recall (PR) AUC of 0.90. On independent testing, our model achieved an accuracy of 87% (95% CI: 82-90%), sensitivity of 89% (95% CI: 81-94%) and specificity of 86% (95% CI: 80-90%) with ROC AUC of 0.90 and PR AUC of 0.87. Assisted by the models’ probabilities, the radiologists achieved a higher average test accuracy (90% vs. 85%, Δ=5, p<0.001), sensitivity (88% vs. 79%, Δ=9, p<0.001) and specificity (91% vs. 88%, Δ=3, p=0.001).ConclusionAI assistance improved radiologists' performance in distinguishing COVID-19 from non-COVID-19 pneumonia on chest CT. ]]> <![CDATA[Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study]]> https://www.researchpad.co/article/elastic_article_15706 CT may play a central role in the diagnosis and management of COVID-19 pneumonia.PurposeTo perform a longitudinal study to analyze the serial CT findings over time in patients with COVID-19 pneumonia.Materials and MethodsDuring January 16 to February 17, 2020, 90 patients (male:female, 33:57; mean age, 45 years) with COVID-19 pneumonia were prospectively enrolled and followed up until they were discharged or died, or until the end of the study. A total of 366 CT scans were acquired and reviewed by 2 groups of radiologists for the patterns and distribution of lung abnormalities, total CT scores and number of zones involved. Those features were analyzed for temporal change.ResultsCT scores and number of zones involved progressed rapidly, peaked during illness days 6-11 (median: 5 and 5), and followed by persistence of high levels. The predominant pattern of abnormalities after symptom onset was ground-glass opacity (35/78 [45%] to 49/79 [62%] in different periods). The percentage of mixed pattern peaked (30/78 [38%]) on illness days 12-17, and became the second most predominant pattern thereafter. Pure ground-glass opacity was the most prevalent sub-type of ground-glass opacity after symptom onset (20/50 [40%] to 20/28 [71%]). The percentage of ground-glass opacity with irregular linear opacity peaked on illness days 6-11 (14/50 [28%)]) and became the second most prevalent subtype thereafter. The distribution of lesions was predominantly bilateral and subpleural. 66/70 (94%) patients discharged had residual disease on final CT scans (median CT scores and zones involved: 4 and 4), with ground-glass opacity (42/70 [60%]) and pure ground-glass opacity (31/42 [74%]) the most common pattern and subtype.ConclusionThe extent of lung abnormalities on CT peaked during illness days 6-11. The temporal changes of the diverse CT manifestations followed a specific pattern, which might indicate the progression and recovery of the illness. ]]> <![CDATA[Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT]]> https://www.researchpad.co/article/elastic_article_15705 Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT.PurposeTo develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances.Materials and MethodsIn this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity.ResultsThe collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97).ConclusionsA deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases. ]]> <![CDATA[Radiological Society of North America Expert Consensus Document on Reporting Chest CT Findings Related to COVID-19: Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA]]> https://www.researchpad.co/article/elastic_article_15701 Routine screening CT for the identification of coronavirus disease 19 (COVID-19) pneumonia is currently not recommended by most radiology societies. However, the number of CT examinations performed in persons under investigation for COVID-19 has increased. We also anticipate that some patients will have incidentally detected findings that could be attributable to COVID-19 pneumonia, requiring radiologists to decide whether or not to mention COVID-19 specifically as a differential diagnostic possibility. We aim to provide guidance to radiologists in reporting CT findings potentially attributable to COVID-19 pneumonia, including standardized language to reduce reporting variability when addressing the possibility of COVID-19. When typical or indeterminate features of COVID-19 pneumonia are present in endemic areas as an incidental finding, we recommend contacting the referring providers to discuss the likelihood of viral infection. These incidental findings do not necessarily need to be reported as COVID-19 pneumonia. In this setting, using the term viral pneumonia can be a reasonable and inclusive alternative. However, if one opts to use the term COVID-19 in the incidental setting, consider the provided standardized reporting language. In addition, practice patterns may vary, and this document is meant to serve as a guide. Consultation with clinical colleagues at each institution is suggested to establish a consensus reporting approach. The goal of this expert consensus is to help radiologists recognize findings of COVID-19 pneumonia and aid their communication with other health care providers, assisting management of patients during this pandemic.

Published under a CC BY 4.0 license.

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<![CDATA[Coronavirus-HKU1 Pneumonia and Differential Diagnosis with COVID-19]]> https://www.researchpad.co/article/elastic_article_15695 <![CDATA[Pulmonary Findings of COVID-19 Identified at Cardiac MRI]]> https://www.researchpad.co/article/elastic_article_15693 <![CDATA[Severe Acute Respiratory Disease in a Huanan Seafood Market Worker: Images of an Early Casualty]]> https://www.researchpad.co/article/elastic_article_15691 <![CDATA[The Many Faces of COVID-19: Spectrum of Imaging Manifestations]]> https://www.researchpad.co/article/elastic_article_15690 <![CDATA[Longitudinal Assessment of COVID-19 Using a Deep Learning–based Quantitative CT Pipeline: Illustration of Two Cases]]> https://www.researchpad.co/article/elastic_article_15687 <![CDATA[COVID-19 Complicated by Acute Pulmonary Embolism]]> https://www.researchpad.co/article/elastic_article_15686 <![CDATA[Severe COVID-19 Pneumonia: Assessing Inflammation Burden with Volume-rendered Chest CT]]> https://www.researchpad.co/article/elastic_article_15685 <![CDATA[Chest Imaging Appearance of COVID-19 Infection]]> https://www.researchpad.co/article/elastic_article_15684 Coronavirus disease 2019 (COVID-19) (previously known as novel coronavirus [2019-nCoV]), first reported in China, has now been declared a global health emergency by the World Health Organization. As confirmed cases are being reported in several countries from all over the world, it becomes important for all radiologists to be aware of the imaging spectrum of the disease and contribute to effective surveillance and response measures.

© RSNA, 2020

See editorial by Kay and Abbara in this issue.

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<![CDATA[Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia]]> https://www.researchpad.co/article/elastic_article_15682 Computed tomography (CT) of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease depicts the extent of lung involvement in COVID-19 pneumonia.PurposeThe aim of the study was to determine the value of quantification of the well-aerated lung obtained at baseline chest CT for determining prognosis in patients with COVID-19 pneumonia.Materials and MethodsPatients who underwent chest CT suspected for COVID-19 pneumonia at the emergency department admission between February 17 to March 10, 2020 were retrospectively analyzed. Patients with negative reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 in nasal-pharyngeal swabs, negative chest CT, and incomplete clinical data were excluded. CT was analyzed for quantification of well aerated lung visually (%V-WAL) and by open-source software (%S-WAL and absolute volume, VOL-WAL). Clinical parameters included demographics, comorbidities, symptoms and symptom duration, oxygen saturation and laboratory values. Logistic regression was used to evaluate relationship between clinical parameters and CT metrics versus patient outcome (ICU admission/death vs. no ICU admission/ death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance.ResultsThe study included 236 patients (females 59/123, 25%; median age, 68 years). A %V-WAL<73% (OR, 5.4; 95% CI, 2.7-10.8; P<0.001), %S-WAL<71% (OR, 3.8; 95% CI, 1.9-7.5; P<0.001), and VOL-WAL<2.9 L (OR, 2.6; 95% CI, 1.2-5.8; P<0.01) were predictors of ICU admission/death. In comparison with clinical model containing only clinical parameters (AUC, 0.83), all three quantitative models showed higher diagnostic performance (AUC 0.86 for all models). The models containing %V-WAL<73% and VOL-WAL<2.9L were superior in terms of performance as compared to the models containing only clinical parameters (P=0.04 for both models).ConclusionIn patients with confirmed COVID-19 pneumonia, visual or software quantification the extent of CT lung abnormality were predictors of ICU admission or death. ]]> <![CDATA[Spectrum of Chest CT Findings in a Familial Cluster of COVID-19 Infection]]> https://www.researchpad.co/article/elastic_article_15662 <![CDATA[Longitudinal CT Findings in COVID-19 Pneumonia: Case Presenting Organizing Pneumonia Pattern]]> https://www.researchpad.co/article/elastic_article_15661