scholarly journals Computed Tomography Image Processing Analysis in COVID-19 Patient Follow-Up Assessment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Santiago Tello-Mijares ◽  
Luisa Woo
Keyword(s):  
Computed Tomography ◽  
Clustering Algorithm ◽  
Cross Validation ◽  
Mean Shift ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Second Order ◽  
Border Region ◽  
Pulmonary Parenchyma

The rapid worldwide spread of the COVID-19 pandemic has infected patients around the world in a short space of time. Chest computed tomography (CT) images of patients who are infected with COVID-19 can offer early diagnosis and efficient forecast monitoring at a low cost. The diagnosis of COVID-19 on CT in an automated way can speed up many tasks and the application of medical treatments. This can help complement reverse transcription-polymerase chain reaction (RT-PCR) diagnosis. The aim of this work is to develop a system that automatically identifies ground-glass opacity (GGO) and pulmonary infiltrates (PIs) on CT images from patients with COVID-19. The purpose is to assess the disease progression during the patient’s follow-up assessment and evaluation. We propose an efficient methodology that incorporates oversegmentation mean shift followed by superpixel-SLIC (simple linear iterative clustering) algorithm on CT images with COVID-19 for pulmonary parenchyma segmentation. To identify the pulmonary parenchyma, we described each superpixel cluster according to its position, grey intensity, second-order texture, and spatial-context-saliency features to classify by a tree random forest (TRF). Second, by applying the watershed segmentation to the mean-shift clusters, only pulmonary parenchyma segmentation-identified zones showed GGO and PI based on the description of each watershed cluster of its position, grey intensity, gradient entropy, second-order texture, Euclidean position to the border region of the PI zone, and global saliency features, after using TRF. Our classification results for pulmonary parenchyma identification on CT images with COVID-19 had a precision of over 92% and recall of over 92% on twofold cross validation. For GGO, the PI identification showed 96% precision and 96% recall on twofold cross validation.

scholarly journals Efficacy of chest X-ray in the diagnosis of COVID-19 pneumonia: comparison with computed tomography through a simplified scoring system designed for triage

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Akın Çinkooğlu ◽  
Selen Bayraktaroğlu ◽  
Naim Ceylan ◽  
Recep Savaş
Keyword(s):  
Computed Tomography ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Scoring System ◽  
Imaging Modality ◽  
Ground Glass Opacity ◽  
Initial Presentation ◽  
X Ray ◽  
Chest X Ray

Abstract Background There is no consensus on the imaging modality to be used in the diagnosis and management of Coronavirus disease 2019 (COVID-19) pneumonia. The purpose of this study was to make a comparison between computed tomography (CT) and chest X-ray (CXR) through a scoring system that can be beneficial to the clinicians in making the triage of patients diagnosed with COVID-19 pneumonia at their initial presentation to the hospital. Results Patients with a negative CXR (30.1%) had significantly lower computed tomography score (CTS) (p < 0.001). Among the lung zones where the only infiltration pattern was ground glass opacity (GGO) on CT images, the ratio of abnormality seen on CXRs was 21.6%. The cut-off value of X-ray score (XRS) to distinguish the patients who needed intensive care at follow-up (n = 12) was 6 (AUC = 0.933, 95% CI = 0.886–0.979, 100% sensitivity, 81% specificity). Conclusions Computed tomography is more effective in the diagnosis of COVID-19 pneumonia at the initial presentation due to the ease detection of GGOs. However, a baseline CXR taken after admission to the hospital can be valuable in predicting patients to be monitored in the intensive care units.

scholarly journals Fibrotic progression and radiologic correlation in matched lung samples from COVID-19 post-mortems

2020 ◽  
Author(s):  
Emanuela Barisione ◽  
Federica Grillo ◽  
Lorenzo Ball ◽  
Rita Bianchi ◽  
Marco Grosso ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Diffuse Alveolar Damage ◽  
Ground Glass Opacity ◽  
Post Mortem ◽  
Mechanically Ventilated ◽  
Illness Duration ◽  
Transbronchial Lung Cryobiopsy ◽  
Histopathologic Analysis ◽  
Lung Lobes

Abstract Data on the pathology of COVID-19 are scarce; available studies show diffuse alveolar damage; however, there is scarce information on the chronologic evolution of COVID-19 lung lesions. The primary aim of the study is to describe the chronology of lung pathologic changes in COVID-19 by using a post-mortem transbronchial lung cryobiopsy approach. Our secondary aim is to correlate the histologic findings with computed tomography patterns. SARS-CoV-2-positive patients, who died while intubated and mechanically ventilated, were enrolled. The procedure was performed 30 min after death, and all lung lobes sampled. Histopathologic analysis was performed on thirty-nine adequate samples from eight patients: two patients (illness duration < 14 days) showed early/exudative phase diffuse alveolar damage, while the remaining 6 patients (median illness duration—32 days) showed progressive histologic patterns (3 with mid/proliferative phase; 3 with late/fibrotic phase diffuse alveolar damage, one of which with honeycombing). Immunohistochemistry for SARS-CoV-2 nucleocapsid protein was positive predominantly in early-phase lesions. Histologic patterns and tomography categories were correlated: early/exudative phase was associated with ground-glass opacity, mid/proliferative lesions with crazy paving, while late/fibrous phase correlated with the consolidation pattern, more frequently seen in the lower/middle lobes. This study uses an innovative cryobiopsy approach for the post-mortem sampling of lung tissues from COVID-19 patients demonstrating the progression of fibrosis in time and correlation with computed tomography features. These findings may prove to be useful in the correct staging of disease, and this could have implications for treatment and patient follow-up.

scholarly journals Clinicopathological and imaging features of pulmonary alveolar microlithiasis in a dog – a case report

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Ana Canadas Sousa ◽  
Joana C. Santos ◽  
Clara Landolt ◽  
Catarina Gomes ◽  
Patrícia Dias-Pereira ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Case Report ◽  
Ground Glass Opacity ◽  
Imaging Features ◽  
Ct Guided ◽  
Pulmonary Alveolar Microlithiasis ◽  
Reticular Pattern ◽  
Pulmonary Parenchyma ◽  
Alveolar Microlithiasis ◽  
Ct Features

Abstract Background The aetiology of pulmonary alveolar microlithiasis (PAM) in animals is still unknown. In humans, this pulmonary disorder is a rare autosomal recessive disorder triggered by a mutation in the gene SLC34A2, which causes deposition and aggregation of calcium and phosphate in the pulmonary parenchyma with formation of microliths. Although histopathological examination is required for a definite diagnosis, in humans, imaging modalities such as computed tomography can demonstrate typical patterns of the disease. This is the first description of the computed tomographic (CT) features of a histologically confirmed PAM in dogs. Case presentation The following report describes a case of a 7-year-old female Boxer dog evaluated for paroxysmal loss of muscle tone and consciousness with excitement. The main differential diagnoses considered were syncope, seizures, and narcolepsy-cataplexy. The results of the complete blood count, serum biochemistry panel, urinalysis, arterial blood pressure, echocardiography, abdominal ultrasound, Holter monitoring, and ECG were all within normal limits. Additional exams included thoracic radiographs, head and thorax CT, bronchoalveolar lavage (BAL), and CT-guided cytology. Thoracic radiographs revealed micronodular calcifications in the lungs, with sandstorm appearance. Computed tomography of the thorax showed the presence of numerous mineralized high-density agglomerates of multiple sizes throughout the pulmonary parenchyma, a reticular pattern with ground glass opacity and intense mineralized fibrosis of the pleural lining. Head CT was unremarkable. BAL and CT-guided cytology were inconclusive, but imaging features strongly suggest the diagnosis of PAM, which was histologically confirmed after necropsy. Conclusions This case report contributes to the clinicopathological and imaging characterization of pulmonary alveolar microlithiasis in dogs. In this species, the diagnosis of PAM should be considered when CT features evidence a reticular pattern with ground glass opacity and the presence of an elevated number and size of calcifications.

Non-Rigid Liver Registration in Liver Computed Tomography Images Using Elastic Method with Global and Local Deformations

2021 ◽  
Vol 11 (3) ◽  
pp. 810-816
Author(s):  
Taeyong Park ◽  
Jeongjin Lee ◽  
Juneseuk Shin ◽  
Kyoung Won Kim ◽  
Ho Chul Kang
Keyword(s):  
Computed Tomography ◽  
Liver Cancer ◽  
Ct Images ◽  
Local Deformation ◽  
Rigid Registration ◽  
Liver Ct ◽  
Liver Surface ◽  
Global And Local ◽  
Local Deformations

The study of follow-up liver computed tomography (CT) images is required for the early diagnosis and treatment evaluation of liver cancer. Although this requirement has been manually performed by doctors, the demands on computer-aided diagnosis are dramatically growing according to the increased amount of medical image data by the recent development of CT. However, conventional image segmentation, registration, and skeletonization methods cannot be directly applied to clinical data due to the characteristics of liver CT images varying largely by patients and contrast agents. In this paper, we propose non-rigid liver segmentation using elastic method with global and local deformation for follow-up liver CT images. To manage intensity differences between two scans, we extract the liver vessel and parenchyma in each scan. And our method binarizes the segmented liver parenchyma and vessel, and performs the registration to minimize the intensity difference between these binarized images of follow-up CT images. The global movements between follow-up CT images are corrected by rigid registration based on liver surface. The local deformations between follow-up CT images are modeled by non-rigid registration, which aligns images using non-rigid transformation, based on locally deformable model. Our method can model the global and local deformation between follow-up liver CT scans by considering the deformation of both the liver surface and vessel. In experimental results using twenty clinical datasets, our method matches the liver effectively between follow-up portal phase CT images, enabling the accurate assessment of the volume change of the liver cancer. The proposed registration method can be applied to the follow-up study of various organ diseases, including cardiovascular diseases and lung cancer.

Pulmonary pure ground-glass opacity lesions: Use of computed tomography attenuation for predicting tumor progression.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 7548-7548
Author(s):  
Takashi Eguchi ◽  
Ryoichi Kondo ◽  
Satoshi Kawakami ◽  
Mina Matsushita ◽  
Tetsu Takeda ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ground Glass Opacity ◽  
Attenuation Value ◽  
Kaplan Meier ◽  
Ct Attenuation ◽  
Long Term Follow Up ◽  
The Difference ◽  
Ct Attenuation Value

7548 Background: Cases with pure ground-glass opacity (GGO) are increasing with the use of computed tomography (CT). In some cases, pure GGO on follow-up CT may represent tumor enlargement or the presence of solid components. We evaluated the natural progression of pure GGO lesions during a long-term follow-up period of more than 2 years. Methods: We retrospectively investigated 95 patients with pure GGO lesions detected between February 2003 and December 2010, in whom these lesions were monitored using CT for more than 2 years. Results: The median follow-up period was 64.7 months (range, 24–114 months). During the follow-up period, areas showing GGO increased in size or appeared to have solid components in 49 patients (group 1) and showed no change in 46 patients (group 2). We compared patient characteristics and tumor properties between the 2 groups. Mean CT attenuation values of the tumors differed significantly between groups 1 (-639.9 ± 88.9 HU) and 2 (-709.2 ± 60.9 HU). In contrast, no significant differences were noted with regard to age, gender, smoking history, lung cancer history, tumor size, and total numbers of GGO lesions between the 2 groups. The difference in the time to tumor growth according to the initial mean CT attenuation value was estimated using the Kaplan–Meier method. The growth incidence at 114 months for lesions with a mean CT attenuation value of -650 HU or more (n = 35) and less than -650 HU (n = 60) were estimated to be 96% and 48%, respectively. The difference between the 2 Kaplan–Meier curves was statistically significant (p < 0.0001). The usefulness of the mean CT attenuation value in predicting the growth of GGO lesions was evaluated using receiver operating characteristic analysis. The sensitivity and specificity was 63% and 87%, respectively, for a mean CT attenuation cutoff value of -650 HU. The area under the curve was 0.76. Conclusions: Many pure GGO lesions have potential for growth as seen during long-term follow-up. CT attenuation is useful in predicting the growth of GGO lesions.

scholarly journals Lung Adenocarcinoma with Mixed Solid and Crazy-paving Appearances

2021 ◽  
Author(s):  
Roqiah Abdul Kadir ◽  
Bushra Johari ◽  
Mohammad Hanafiah ◽  
Lily Zainudin
Keyword(s):  
Computed Tomography ◽  
Lung Adenocarcinoma ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Ground Glass ◽  
Linear Pattern ◽  
Alveolar Proteinosis

‘Crazy-paving’ refers to the superimposition of ground-glass opacity and linear pattern on computed tomography (CT) images. ‘Crazy-paving’ was initially pathognomonic for alveolar proteinosis. Lung adenocarcinoma demonstrating both solid and crazy-paving appearances on CT is a rare occurance.

scholarly journals Computed tomography manifestations in super early stage 2019 novel coronavirus pneumonia

2020 ◽  
pp. 028418512092480
Author(s):  
Shan Hu ◽  
Zhen Li ◽  
Xu Chen ◽  
Chang-Hong Liang
Keyword(s):  
Computed Tomography ◽  
Early Stage ◽  
Ground Glass Opacity ◽  
Chest Ct ◽  
Ground Glass ◽  
Reticular Pattern ◽  
Initial Stage ◽  
Pr China ◽  
Novel Coronavirus

Background The recent outbreak of pneumonia cases in Wuhan, PR China, was caused by a novel beta coronavirus, the 2019 novel coronavirus (COVID-19). Purpose To summarize chest computed tomography (CT) manifestations of the early stage of COVID-19 infection and provide a piece of reliable imaging evidence for initial screening and diagnosis. Material and Methods From 10 January 2020 to 10 February 2020, we continuously observed chest CT imaging of 14 patients with clinically suspected new coronavirus infection in the two weeks after onset of symptoms. Ground-glass opacity (GGO), consolidation, reticular pattern, and ground-glass mimic nodules in each patient’s chest CT image were recorded. Results We enrolled 14 patients, of which nine patients had the infection confirmed by reverse transcription polymerase chain reaction (RT-PCR). Five patients were highly suspected of infection. All cases had epidemiological evidence. GGO was a dominant imaging manifestation in the initial days of infection. GGO performance accounts for 40% in 1– 2 days, 90% in 3– 6 days, and 85% in 7– 10 days. With disease progression, consolidation appeared on follow-up CT. Consolidation performance accounts for 0% in 1– 2 days, 40% in 3– 6 days, and 71% in 7– 10 days. The lesions are mostly near the pleura. The number of lesions and the extent of the lesions increased as the disease progressed. Conclusion Patients with novel coronavirus pneumonia have characteristic CT features in the initial stage of infection, which can be used as an essential supplement for nucleic acid examination.

scholarly journals Medium-term chest computed tomography (CT) follow-up of COVID-19 pneumonia patients after recovery to assess the rate of resolution and determine the potential predictors of persistent lung changes

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Arshed Hussain Parry ◽  
Abdul Haseeb Wani ◽  
Naveed Nazir Shah ◽  
Majid Jehangir
Keyword(s):  
Computed Tomography ◽  
Ground Glass Opacity ◽  
Study Cohort ◽  
Medium Term ◽  
Chest Computed Tomography ◽  
Imaging Characteristics ◽  
Lung Abnormalities ◽  
Wbc Count ◽  
Mixed Pattern

Abstract Background The data on medium-term follow-up of coronavirus disease-19 (COVID-19) pneumonia survivors is scarce. Medium-term follow-up will generate knowledge and help in devising a structured follow-up plan and to facilitate enrolment in clinical trials assessing the role of antifibrotic drugs in modifying the course of disease in order to avert long-term pulmonary sequelae of disease. The study was aimed to evaluate the lung findings on a medium-term follow-up (3 months or more) chest computed tomography (CT) in COVID-19 pneumonia survivors, assess the rate of resolution or persistence of lung abnormalities and to identify the initial demographic, clinical, and imaging characteristics that could potentially predict the persistence of lung abnormalities on follow-up. Results Out of the total study cohort of 81 patients, 46 (56.8%) demonstrated complete resolution of lung findings and the remaining 35 (43.2%) had residual lung opacities on follow-up CT. The most common type of residual abnormality was ground glass opacity (GGO) (16/35; 45.7%), followed by parenchymal bands (9/35; 25.7%), mixed pattern of GGO and parenchymal bands (6/35; 17.2%), bronchiectasis (6/35; 17.2%), and interlobular septal thickening (4/35; 11.4%). Patients with residual abnormalities were older, had higher BMI, more comorbidities, lower SpO2, longer hospital stay, higher rate of intensive care unit (ICU) admission, higher WBC count, a higher CT severity score, and lower rate of steroid administration with all p values < 0.05. Conclusion Nearly half of post-COVID-19 survivors had residual lung abnormalities after ≥ 3 months of follow-up. Certain clinico-radiological characteristics have the potential to identify the individuals at risk of having residual lung abnormalities on medium-term follow-up.

scholarly journals Characteristics of chest high resolution computed tomography images of COVID-19: A retrospective study of 46 patients

2021 ◽  
Vol 37 (3) ◽  
Author(s):  
Yibo Lu ◽  
Jingru Zhou ◽  
Yimei Mo ◽  
Shulin Song ◽  
Xue Wei ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Retrospective Study ◽  
High Resolution ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Ground Glass ◽  
High Resolution Computed Tomography ◽  
Creative Commons ◽  
Computed Tomography Images ◽  
Clinical Records

Objective: To analyze the characteristics of chest high resolution computed tomography (CT) images of coronavirus disease 2019 (COVID-19). Methods: This is a retrospective study analyzing the clinical records and chest high-resolution CT images of 46 consecutive patients who were diagnosed with COVID-19 by nucleic acid tests and treated at our hospitals between January 2020 and February 2020. Results: Abnormalities in the CT images were found in 44 patients (95.6%). The lesions were unilateral in eight patients (17.4%), bilateral in 36 patients (78.3%), single in seven patients (15.9%), and multiple in 37 patients (84.1%). The morphology of the lesions was scattered opacity in 10 patients (21.7%), patchy opacity in 38 patients (82.6%), fibrotic cord in 17 patients (37.0%), and wedge-shaped opacity in two patients (4.3%). The lesions can be classified as ground-glass opacity in eight patients (17.4%), consolidation in one patient (2.2%), and ground-glass opacity plus consolidation in 28 patients (60.9%). Conclusion: Most COVID-19 patients showed abnormalities in chest CT images and the most common findings were ground-glass opacity plus consolidation. Abbreviations:COVID-19: coronavirus disease 2019, CT: computed tomography,SARS-CoV-2: severe acute respiratory syndrome coronavirus 2, RNA: ribonucleic acid. doi: https://doi.org/10.12669/pjms.37.3.3504 How to cite this:Lu Y, Zhou J, Mo Y, Song S, Wei X, Ding K. Characteristics of Chest high resolution computed tomography images of COVID-19: A retrospective study of 46 patients. Pak J Med Sci. 2021;37(3):---------. doi: https://doi.org/10.12669/pjms.37.3.3504 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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