Utility of Dual-Energy Computed Tomography in Clinical Conundra

Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems.

Dual energy CT reconstruction using the constrained one step spectral image reconstruction algorithm

BACKGROUND

The constrained one-step spectral CT Image Reconstruction method (cOSSCIR) has been developed to estimate basis material maps directly from spectral CT data using a model of the polyenergetic x-ray transmissions and incorporating convex constraints into the inversion problem. This 'one-step' approach has been shown to stabilize the inversion in the case of photon-counting CT, and may provide similar benefits to dual-kV systems that utilize integrating detectors. Since the approach does not require the same rays be acquired for every spectral measurement, cOSSCIR can apply to dual energy protocols and systems used clinically, such as fast and slow kV switching systems and dual source scanning.

PURPOSE

The purpose of this study is to investigate the use of cOSSCIR applied to dual-kV data, using both registered and unregistered spectral acquisitions, specifically slow and fast kV switching imaging protocols. For this application, cOSSCIR is investigated using inverse crime simulations and dual-kV experiments. This study is the first demonstration of cOSSCIR on the dual-kV reconstruction problem.

METHODS

An integrating detector model was developed for the purpose of reconstructing dual-kV data, and an inverse crime study was used to validate the detector model within the cOSSCIR framework using a simulated pelvic phantom. Experiments were also used to evaluate cOSSCIR on the dual energy problem. Dual-kV data was obtained from a physical phantom containing analogs of adipose, bone, and liver tissues, with the aim of recovering the material coefficients in the bone and adipose basis material maps. cOSSCIR was applied to acquisitions where all rays performed both spectral measurements (registered) and fast and slow kV switching acquisitions (unregistered). cOSSCIR was also compared to two image-domain decomposition approaches, where image-domain methods are the conventional approach for decomposing unregistered spectral data.

RESULTS

Simulations demonstrate the application of cOSSCIR to the dual-kV inversion problem by successfully recovering the material basis maps on ideal data, while further showing that unregistered data presents a more challenging inversion problem. In our experimental reconstructions, the recovered basis material coefficient errors were found to be less than 6.5% in the bone, adipose, and liver regions for both registered and unregistered protocols. Similarly, the errors were less than 4% in the 50 keV virtual mono-energetic images, and the recovered material decomposition vectors nearly overlap their corresponding ground-truth vectors. Additionally, a preliminary two material decomposition study of iodine quantification recovered an average concentration of 9.2 mg/mL from a 10 mg/mL experimental iodine analog.

CONCLUSIONS

Using our integrating detector and spectral models, cOSCCIR is capable of accurately recovering material basis maps from dual-kV data for both registered and unregistered data. The material decomposition quantification compare favorably to the image domain approaches, and our results were not affected by the imaging protocol. Our results also suggest the extension of cOSSCIR to iodine quantification using two material decomposition.

Prediction of proton stopping power ratios using dual-energy CT basis material decomposition

BACKGROUND

Proton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single-energy computed tomography (SECT). Various studies have shown that multi-energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age-related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection-based MECT systems for SPR prediction.

PURPOSE

To present and evaluate a novel proton SPR prediction method based on MD images from dual-energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy.

METHODS

A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non-tissue materials, by minimizing the root-mean-square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD-SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data-fitting. The goodness of fit of the MD-SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head- and an abdomen-sized phantom setup, were acquired with a clinical projection-based fast-kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non-tissue materials in the phantoms, as well as used for input to the MD-SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image-based consecutive scan DECT for the same phantom setups.

RESULTS

The predicted SPRs of the tissue surrogates were similar for MD-SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non-adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between -0.7% and -1.8%. The SPRs of the non-tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD-SPR images, compared to 6.8% for DirectSPR.

CONCLUSIONS

The MD-SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non-tissue materials.

The Role of Dual-Energy CT in Solid Organ Injury

The liver, spleen, and kidneys are the commonest injured solid organs in blunt and penetrating trauma. The American Association for the Surgery of Trauma (AAST) Organ Injury Scale (OIS) is the most widely accepted system for categorizing traumatic injuries. Grading systems allow clear communication of findings between clinical teams and assign a measurable severity of injury, which directly correlates with morbidity and mortality. The 2018 revised AAST OIS emphasizes reliance on CT for accurate grading; in particular regarding vascular injuries. Dual-Energy CT (DECT) has emerged as a promising tool with multiple clinical applications already demonstrated. In this review article, we summarize the basic principles of CT attenuation to refresh the minds of our readers and we scrutinize DECT's technology as opposed to conventional Single-Energy CT (SECT). This is followed by outlining the benefits of various DECT postprocessing techniques, which authors of this article refer to as the 3Ms (Mapping of Iodine, Material decomposition, and Monoenergetic virtual imaging), in aiding radiologists to confidently assign an OIS as well as problem solve complex injury patterns. In addition, a thorough discussion of changes to the revised AAST OIS focusing on definitions of key terms used in reporting injuries is described.

Dual-Energy Computed Tomography: Technological Considerations

Compared to conventional single-energy CT (SECT), dual-energy CT (DECT) provides additional information to better characterize imaged tissues. Approaches to DECT acquisition vary by vendor and include source-based and detector-based systems, each with its own advantages and disadvantages. Despite the different approaches to DECT acquisition, the most utilized DECT images include routine SECT equivalent, virtual monoenergetic, material density (eg, iodine map), and virtual non-contrast images. These images are generated either through reconstructions in the projection or image domains. Designing and implementing an optimal DECT workflow into routine clinical practice depends on radiologist and technologist input with special considerations including appropriate patient and protocol selection and workflow automation. In addition to better tissue characterization, DECT provides numerous advantages over SECT such as the characterization of incidental findings and dose reduction in radiation and iodinated contrast.

Physiological iodine uptake of the spine's bone marrow in dual-energy computed tomography - using artificial intelligence to define reference values based on 678 CT examinations of 189 individuals

Purpose

The bone marrow's iodine uptake in dual-energy CT (DECT) is elevated in malignant disease. We aimed to investigate the physiological range of bone marrow iodine uptake after intravenous contrast application, and examine its dependence on vBMD, iodine blood pool, patient age, and sex.

Method

Retrospective analysis of oncological patients without evidence of metastatic disease. DECT examinations were performed on a spectral detector CT scanner in portal venous contrast phase. The thoracic and lumbar spine were segmented by a pre-trained neural network, obtaining volumetric iodine concentration data [mg/ml]. vBMD was assessed using a phantomless, CE-certified software [mg/cm3]. The iodine blood pool was estimated by ROI-based measurements in the great abdominal vessels. A multivariate regression model was fit with the dependent variable "median bone marrow iodine uptake". Standardized regression coefficients (β) were calculated to assess the impact of each covariate.

Results

678 consecutive DECT exams of 189 individuals (93 female, age 61.4 ± 16.0 years) were evaluated. AI-based segmentation provided volumetric data of 97.9% of the included vertebrae (n=11,286). The 95^th percentile of bone marrow iodine uptake, as a surrogate for the upper margin of the physiological distribution, ranged between 4.7-6.4 mg/ml. vBMD (p <0.001, mean β=0.50) and portal vein iodine blood pool (p <0.001, mean β=0.43) mediated the strongest impact. Based thereon, adjusted reference values were calculated.

Conclusion

The bone marrow iodine uptake demonstrates a distinct profile depending on vBMD, iodine blood pool, patient age, and sex. This study is the first to provide the adjusted reference values.

Dual-layer spectral CT improves the image quality of cerebral unenhanced CT scan in children

PURPOSE

To evaluate the image quality and determine the optimal energies of virtual monoenergetic imaging (VMI) in unenhanced pediatric cerebral scans by dual-layer spectral detector computed tomography (DLCT).

METHODS

Fifty-three consecutive unenhanced cerebral scans by a DLCT scanner in children (age ≤ 12 years) were retrospectively analyzed. Conventional images (CI) and VMIs were reconstructed. The gray matter (GM) and white matter (WM) noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), posterior fossa, and subcalvarial artifac tindex (PFAI, SAI) were calculated. Two radiologists independently determined the image quality using a 5-point Likert-type scale based on GM - WM differentiation (GWMA), subcalvarialspace (SAA), beam hardening artifacts in the posterior fossa (PFAA), and the overall diagnostic quality. The student t-test and Wilcoxon test were used to determining the statistical significance.

RESULTS

Compared with CI, superior noise were observed in VMI at low keV levels and were lowest at 100 keV (P < 0.001); the SNR and CNR were significantly higher at the 45 keV to 75 keV levels (all Ps of <0.005). The best GWMA were noticed at the 50 keV level compared to other keV levels (all P < 0.05). The optimal SAA and PFAA were found at 100 keV, respectively. The assessment of overall diagnostic quality was the best at 50 keV (P < 0.013 to < 0.001).

CONCLUSIONS

The VMI scan significantly improved the quality of pediatric cerebral images compared with those from CI. The optimal energy level for the brainparenchyma was 50 keV while those for subcalvarial space and posterior fossa were 100 keV.

[Accuracy of Virtual Non-contrast Image Reconstruction Using Material Decomposition for Fast kV-switching Dual-energy CT]

PURPOSE

Dual-energy computed tomography (DECT) system can generate virtual non-contrast (VNC) images. Although several reconstruction algorithms are defined, there are not many researches using deep learning image reconstruction (DLIR) algorithm. In this study, we evaluated the accuracy of the VNC image reconstruction under various conditions using DLIR algorithm.

METHODS

At first, each iodine insert with variable concentrations (2.0, 5.0, 10.0, 15.0 mg/ml) or diameters (2.0, 5.0, 10.0, 28.5 mm), or mixed insert including blood-mimicking material with iodine (iodine concentrations: 2.0, 4.0 mg/ml) was put in the center of the multi-energy CT phantom (Gammex, USA). This phantom was placed in the isocenter of DECT, and it scanned and reconstructed the VNC images. In addition, the VNC images were reconstructed with various display field of view (DFOV) sizes (240, 350 mm) or reconstruction algorithms (filtered back projection, advanced statistical iterative reconstruction, deep learning image reconstruction) for each iodine diameter. Attenuation values of these images (CT_VNC) were measured and assessed by placing a circular region of interest (ROI) on each insert.

RESULTS

CT_VNC form iodine inserts increased with iodine concentration became lower, whereas CT_VNC form blood plus iodine inserts were stable regardless of low iodine concentration. As iodine diameter became smaller, CT_VNC increased remarkably. CT_VNC remained steady even though reconstruction parameters were varied.

CONCLUSION

In our study, the VNC image reconstruction using DLIR algorithm was affected by various conditions such as iodine concentration and size. In particular, its accuracy was reduced by the size of target.

Gemstone Spectral CT Virtual Noncontrast Images and Iodine Maps for the Characterization of Thyroid Lesions and Distinguishing Thyroid Papillary Carcinoma from Nodular Goiter

BACKGROUND

Gemstone spectral contrast-enhanced CT with virtual noncontrast (VNC) images and iodine maps can potentially reduce the number of required CT scans for thyroid lesions. However, data regarding the clinical utility of VNC images and iodine maps in characterizing thyroid lesions and distinguishing thyroid papillary carcinoma from nodular goiter are still limited.

PURPOSE

To determine whether VNC images and iodine density could reliably aid in characterizing thyroid lesions and distinguishing thyroid papillary carcinoma from nodular goiter compared with true noncontrast (TNC) images.

METHODS

This retrospective study included patients with thyroid papillary carcinoma or nodular goiter who underwent TNC and contrast-enhanced gemstone spectral CT scans. The consistency of qualitative parameters, including intralesional calcification, necrosis, lesion boundary, thyroid edge interruption, and lymph node metastasis, between TNC and VNC images, was analyzed using the kappa statistic. TNC attenuation, VNC attenuation, absolute attenuation between TNC and VNC, and iodine density were compared between thyroid papillary carcinoma and nodular goiter by using Student's t-test. The diagnostic performance for distinguishing papillary carcinoma from nodular goiter was evaluated by using the area under the receiver operating characteristic curve (AUC) value, sensitivity, and specificity.

RESULTS

VNC and TNC imaging showed comparable performance in delineating calcification, necrosis, lesion boundary, thyroid edge interruption, and lymph node metastasis (all k > 0.75). Papillary carcinoma showed significantly lower absolute attenuation between VNC and TNC than nodular goiter (7.86 ± 6.74 vs. 13.43 ± 10.53, P=0.026), which was similarly observed for iodine density (31.45 ± 8.51 vs. 37.27 ± 10.34, P=0.016). The iodine density showed higher diagnostic performance (AUC = 0.727), accuracy (0.773 vs. 0.667), sensitivity (0.750 vs. 0.708), and specificity (0.786 vs. 0.643) than the absolute attenuation between TNC and VNC images (AUC = 0.683).

CONCLUSIONS

VNC imaging, a promising substitute for TNC imaging, has comparable diagnostic efficacy for reliably characterizing thyroid lesions. Iodine density could be valuable for distinguishing thyroid papillary carcinoma from nodular goiter.

Diagnostic performance of dual-energy computed tomography for HCC after transarterial chemoembolization: Utility of virtual unenhanced and low keV virtual monochromatic images

The purpose of this study is to evaluate the usefulness of virtual unenhanced (VUE) and low keV virtual monochromatic images (VMI) for diagnosing viable hepatocellular carcinomas (HCC) after transarterial chemoembolization (TACE). This retrospective study included 53 patients with suspected viable HCC after TACE who underwent multiphasic liver computed tomography including true unenhanced (TUE) phase and conventional (CV) enhanced phases on a dual-energy scanner. VUE images, 40 keV and 55 keV VMIs of enhanced phases were reconstructed using dual-energy computed tomography data. For every patient, six combination image sets (TUE-CV; TUE-55; TUE-40; VUE-CV; VUE-55; VUE-40) were evaluated by two readers and compared with the reference standard.There was no statistically significant difference (P > .05) in sensitivity or specificity among all image combinations. In most combinations, interobserver agreements were almost perfect. The diagnostic odds ratio showed a higher trend in combinations with conventional images. Currently, with regards to diagnostic performance, liver computed tomography including TUE and CV enhanced phases is recommended for tumor surveillance after TACE because VUE and VMIs do not have a distinct advantage compared to conventional images.

Application of dual-layer spectral detector computed tomography to evaluate the expression of Ki-67 in colorectal cancer

BACKGROUND

Compared with traditional computed tomography (CT), dual-layer spectral detector CT (SDCT) shows significant improvement in imaging soft tissues of the digestive tract. This work aimed to explore the application of SDCT to evaluate the expression of the molecular marker Ki-67 in colorectal cancer.

METHODS

We retrospectively analyzed the imaging data of the SDCT (IQon Spectral CT; Philips Healthcare) of 45 patients with colorectal cancer in our centre. We used Spearman's test for the imaging parameters (reconstruction of 40, 70, and 100 keV virtual monoenergetic images [VMIs] and the slope of the Hounsfield unit attenuation plot [VMI Slope] based on venous phase CT images, the arterial phase iodine concentration [AP-IC] and venous phase iodine concentration [VP-IC], and the effective atomic number [Z effect]) and correlation analysis for the Ki-67 index. Multivariate logistic regression was used to eliminate confounding factors. We evaluated the expression level of Ki-67 and drew the receiver operating characteristic curve.

RESULTS

The 40-keV VMI, VMI Slope, and AP-IC were found to better reflect the Ki-67 index in patients with colorectal cancer with statistical significance. The 40-keV VMI (r = -0.612, p < 0.001) and VMI Slope (r = -0.523, p < 0.001) were negatively correlated with the Ki-67 index, and AP-IC (r = 0.378, p = 0.010) was positively correlated with the Ki-67 index. The other indexes (p > 0.05) were not statistically significant. The SDCT parameters demonstrated good performance, with area under curves of 0.785 for 40-keV VMI and 0.752 for AP-IC.

CONCLUSION

The SDCT parameters 40-keV VMI and AP-IC can be used for preliminary evaluation of the Ki-67 index in colorectal cancer.

Potential of dual-layer spectral CT for the differentiation between hemorrhage and iodinated contrast medium in the brain after endovascular treatment of ischemic stroke patients

BACKGROUND

One possible complication after mechanical thrombectomy is hemorrhage. In conventional CT it is often difficult to differ between extravasation of iodinated contrast medium and blood. This differentiation, however, is essential for treatments with anticoagulants and antiplatelets.

PURPOSE

To evaluate dual-layer spectral Computed Tomography (DLSCT) for the differentiation between intracranial hemorrhage and iodinated contrast medium in ischemic stroke patients after mechanical thrombectomy.

MATERIALS AND METHODS

First, in vitro experiments were performed. Then, head CT images of 47 patients after mechanical thrombectomy were analyzed. Virtual non-contrast (VNC) images and iodine density maps (IDM) were calculated and evaluated. Region of interests (ROIs) analyses were performed. Sensitivity and specificity as well as ROC curves were calculated.

RESULTS

IDM and VNC images enabled clear differentiation between blood and iodine and reliable quantification of different iodine concentrations in vitro. A total of 23 hyperdense areas were detected in 13 patients, classified as hemorrhage (n = 7), iodinated contrast medium (n = 4) and a mixture of both (n = 12). Sensitivity and specificity for the detection of blood was 100%.

CONCLUSION

DLSCT enables differentiation between intracranial hemorrhage and iodinated contrast medium in patients after mechanical thrombectomy and might improve diagnostic imaging in post-interventional stroke patients.

CT in coronavirus disease 2019 (COVID-19): a systematic review of chest CT findings in 4410 adult patients

OBJECTIVE

The objective of this systematic review was to evaluate the key imaging manifestations of COVID-19 on chest CT in adult patients by providing a comprehensive review of the published literature.

METHODS

We performed a systematic literature search from the PubMed, Google Scholar, Embase, and WHO databases for studies mentioning the chest CT imaging findings of adult COVID-19 patients.

RESULTS

A total of 45 studies comprising 4410 patients were included. Ground glass opacities (GGO), in isolation (50.2%) or coexisting with consolidations (44.2%), were the most common lesions. Distribution of GGOs was most commonly bilateral, peripheral/subpleural, and posterior with predilection for lower lobes. Common ancillary findings included pulmonary vascular enlargement (64%), intralobular septal thickening (60%), adjacent pleural thickening (41.7%), air bronchograms (41.2%), subpleural lines, crazy paving, bronchus distortion, bronchiectasis, and interlobular septal thickening. CT in early follow-up period generally showed an increase in size, number, and density of GGOs, with progression into mixed areas of GGOs plus consolidations and crazy paving, peaking at 10-11 days, before gradually resolving or persisting as patchy fibrosis. While younger adults more commonly had GGOs, extensive/multilobar involvement with consolidations was prevalent in the older population and those with severe disease.

CONCLUSION

This review describes the imaging features for diagnosis, stratification, and follow-up of COVID-19 patients. The most common CT manifestations are bilateral, peripheral/subpleural, posterior GGOs with or without consolidations with a lower lobe predominance. It is pertinent to be familiar with the various imaging findings to positively impact the management of these patients.

KEY POINTS

• Ground glass opacities (GGOs), whether isolated or coexisting with consolidations, in bilateral and subpleural distribution, are the most prevalent chest CT findings in adult COVID-19 patients. • Follow-up CT shows a progression of GGOs into a mixed pattern, reaching a peak at 10-11 days, before gradually resolving or persisting as patchy fibrosis. • Younger people tend to have more GGOs. Older or sicker people tend to have more extensive involvement with consolidations.

Challenges for managing the cumulative effective dose for patients

Notwithstanding that 100 mSv is not a threshold for radiation effects, cumulative effective dose (CED) for patients of ≥100 mSv derived from recurrent imaging procedures with ionising radiation has been recently the topic of several publications. The International Commission on Radiological Protection has alerted on the problems to use effective dose for risk estimation in individual patients but has accepted to use this quantity for comparison the relative radiation risks between different imaging modalities. A new International Commission on Radiological Protection document on the use of effective dose (including medicine), is in preparation. Recently published data on the number of patients with CED ≥100 mSv ranged from 0.6 to 3.4% in CT and around 4% in interventional radiology. The challenges to manage the existing situation are summarised. The main aspects identified are: 1) New technology with dose reduction techniques. 2) Refinements in the application of the justification and optimisation for these groups of patients. 3) Patient dose management systems with alerts on the cumulative high doses. 4) Education on the proper use of cumulative effective dose for referrers and practitioners including information for patients. 5) Future research programmes in radiation biology and epidemiology may profit the patient dose data from the groups with high cumulative dose values.

Diagnostic approaches in COVID-19: clinical updates

INTRODUCTION

COVID-19 is a recent emerging pandemic whose prognosis is still unclear. Diagnostic tools are the main players that not only indicate a possible infection but can further restrict the transmission and can determine the extent to which disease progression would occur.

AREAS COVERED

In this paper, we have performed a narrative and critical review on different technology-based diagnostic strategies such as molecular approaches including real-time reverse transcriptase PCR, serological testing through enzyme-linked immunosorbent assay, laboratory and point of care devices, radiology-based detection through computed tomography and chest X-ray, and viral cell cultures on Vero E6 cell lines are discussed in detail to address COVID-19. This review further provides an overview of emergency use authorized immunodiagnostic and molecular diagnostic kits and POC devices by FDA for timely and efficient conduction of diagnostic tests. The majority of the literature cited in this paper is collected from guidelines on protocols and other considerations on diagnostic strategies of COVID-19 issued by WHO, CDC, and FDA under emergency authorization.

EXPERT OPINION

Such information holds importance to the health professionals in conducting error-free diagnostic tests and researches in producing better clinical strategies by addressing the limitations associated with the available methods.

COVID-19 Control by Computer Vision Approaches: A Survey

The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at test to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources, and an indication of future research directions. We want to make it possible for computer vision researchers to find existing and future research directions. This survey article presents a preliminary review of the literature on research community efforts against COVID-19 pandemic.

Imaging Diagnostics and Pathology in SARS-CoV-2-Related Diseases

In December 2019, physicians reported numerous patients showing pneumonia of unknown origin in the Chinese region of Wuhan. Following the spreading of the infection over the world, The World Health Organization (WHO) on 11 March 2020 declared the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak a global pandemic. The scientific community is exerting an extraordinary effort to elucidate all aspects related to SARS-CoV-2, such as the structure, ultrastructure, invasion mechanisms, replication mechanisms, or drugs for treatment, mainly through in vitro studies. Thus, the clinical in vivo data can provide a test bench for new discoveries in the field of SARS-CoV-2, finding new solutions to fight the current pandemic. During this dramatic situation, the normal scientific protocols for the development of new diagnostic procedures or drugs are frequently not completely applied in order to speed up these processes. In this context, interdisciplinarity is fundamental. Specifically, a great contribution can be provided by the association and interpretation of data derived from medical disciplines based on the study of images, such as radiology, nuclear medicine, and pathology. Therefore, here, we highlighted the most recent histopathological and imaging data concerning the SARS-CoV-2 infection in lung and other human organs such as the kidney, heart, and vascular system. In addition, we evaluated the possible matches among data of radiology, nuclear medicine, and pathology departments in order to support the intense scientific work to address the SARS-CoV-2 pandemic. In this regard, the development of artificial intelligence algorithms that are capable of correlating these clinical data with the new scientific discoveries concerning SARS-CoV-2 might be the keystone to get out of the pandemic.

A chronicle of SARS-CoV-2: Part-I - Epidemiology, diagnosis, prognosis, transmission and treatment

In order to benefit the public, community workers and scientific community, we hereby present a chronicle of SARS-CoV-2 that leads to the unseen precedent of social distancing and lockdown owing to coronavirus disease (COVID-19). Information on this life-threatening pandemic of COVID-19 is sparse and discrete; and the urgency is such that the dissemination of information is increasing with numerous daily publications on the topic. Therefore, we developed a comprehensive review on various aspects of SARS-CoV-2 and COVID-19. We scientifically compiled published research, news, and reports from various sources to comprehend and summarize the information and findings on Coronaviruses. The review explicitly covers the aspects like genome and pedigree of SARS-CoV-2; epidemiology, prognosis, pathogenesis, symptoms and diagnosis of COVID-19 in order to catalog the right information on transmission route, and influence of environmental factors on virus transmissions, for the robust understanding of right strategical steps for proper COVID-19 management. We have explicitly highlighted several useful information and facts like: i) No established relationship between progression of SARS-CoV-2 with temperature, humidity and/or both, ii) The underlying mechanism of SARS-CoV-2 is not fully understood, iii) Respiratory droplet size determines drop and airborne-based transmission, iv) Prognosis of COVID-19 can be done by its effects on various body organs, v) Infection can be stopped by restricting the binding of S protein and AE2, vi) Hydroxychloroquine is believed to be better than chloroquine for COVID-19, vii) Ivermectin with Vero-hSLAM cells is able to reduce infection by ~5000 time within 2 days, and viii) Nafamostat mesylate can inhibit SARS-CoV-2 S protein-initiated membrane fusion. We have also suggested future research perspectives, challenges and scope.

Optical techniques, computed tomography and deep learning role in the diagnosis of COVID-19 pandemic towards increasing the survival rate of vulnerable populations

•Severe lung complications can be explored using computed tomography during COVID-19 pandemic. •Ultra-low dose CT can enhance COVID-19 infected patients diagnostic capability. •Optically monitored CT along with deep learning is the best solution for diagnosis of COVID-19 during pandemic. •CT scans sensitivity (88 %) is preferable on clinical approach sensitivity (59 %) for COVID-19 suspected patients. •CT and Computer aided approaches helps the radiologist to make fast and accurate diagnosis during COVID-19 pandemic.

A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19

In the past few months, several works were published in regards to the dynamics and early detection of COVID-19 via mathematical modeling and Artificial intelligence (AI). The aim of this work is to provide the research community with comprehensive overview of the methods used in these studies as well as a compendium of available open source datasets in regards to COVID-19. In all, 61 journal articles, reports, fact sheets, and websites dealing with COVID-19 were studied and reviewed. It was found that most mathematical modeling done were based on the Susceptible-Exposed-Infected-Removed (SEIR) and Susceptible-infected-recovered (SIR) models while most of the AI implementations were Convolutional Neural Network (CNN) on X-ray and CT images. In terms of available datasets, they include aggregated case reports, medical images, management strategies, healthcare workforce, demography, and mobility during the outbreak. Both Mathematical modeling and AI have both shown to be reliable tools in the fight against this pandemic. Several datasets concerning the COVID-19 have also been collected and shared open source. However, much work is needed to be done in the diversification of the datasets. Other AI and modeling applications in healthcare should be explored in regards to this COVID-19.

Current epidemiological and clinical features of COVID-19; a global perspective from China

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and represents a potentially fatal disease of great global public health importance. As of March 26, 2020, the outbreak of COVID-19 has resulted in 462,801 confirmed cases and 20,839 deaths globally, which is more than those caused by SARS and Middle East respiratory syndrome (MERS) in 2003 and 2013, respectively. The epidemic has posed considerable challenges worldwide. Under a strict mechanism of massive prevention and control, China has seen a rapid decrease in new cases of coronavirus; however, the global situation remains serious. Additionally, the origin of COVID-19 has not been determined and no specific antiviral treatment or vaccine is currently available. Based on the published data, this review systematically discusses the etiology, epidemiology, clinical characteristics, and current intervention measures related to COVID-19 in the hope that it may provide a reference for future studies and aid in the prevention and control of the COVID-19 epidemic.

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

BACKGROUND

The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system.

OBJECTIVE

One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images.

METHODS

Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task.

RESULTS

A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively.

CONCLUSION

This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.