ResearchPad - pulmonary-imaging https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![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[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[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 <![CDATA[COVID-19 Infection Presenting with CT Halo Sign]]> https://www.researchpad.co/article/elastic_article_15430 <![CDATA[Clustered micronodules as predominant manifestation on CT: A sign of active but indolently evolving pulmonary tuberculosis]]> https://www.researchpad.co/article/N48b20e2f-c3ed-4c3c-a251-c583ed3c8c8a

Objective

To investigate the prevalence, patient characteristics, and natural history of clustered micronodules (CMs) in active pulmonary tuberculosis.

Materials and methods

From January 2013 through July 2018, 833 consecutive patients with bacteriologically or polymerase chain reaction–proven active pulmonary tuberculosis were retrospectively evaluated. CMs were defined as a localized aggregation of multiple dense discrete micronodules, which primarily distributed around small airways distal to the level of the segmental bronchus: small airways surrounded by CMs maintained luminal patency and the CMs might coalesce into a larger nodule. The patients were dichotomized according to whether the predominant computed tomography (CT) abnormalities were CMs. We analyzed radiologic and pathologic findings in patients whose predominant diagnostic CT abnormalities were CMs, along with those of incidental pre-diagnostic CT scans, if available. Chi-square, McNemar, Student t-test and Wilcoxon-signed rank test were performed.

Results

CMs were the predominant CT abnormality in 2.6% of the patients (22/833, 95% CI, 1.8–4.0%) with less sputum smear-positivity (4.8% vs 31.0%; p = .010) and a similar proportion of immunocompromised status (40.9% vs 46.0%; p = .637) than those without having CMs as the predominant CT abnormality. The time interval for minimal radiologic progression was 6.4 months. The extent of CMs increased with disease progression, frequently accompanied by consolidation and small airway wall thickening. Pathologically, smaller CMs were non-caseating granulomas confined to the peribronchiolar interstitium, whereas larger CMs were caseating granulomas involving lung parenchyma. Two of the five patients with a pre-diagnostic CT scan obtained more than 50 months pre-diagnosis showed an incipient stage of CMs, in which they were small peribronchiolar nodules.

Conclusion

Active pulmonary tuberculosis manifested predominantly as CMs in 2.6% of patients, with scarce of acid-fast bacilli smear-positivity and no association with impaired host immunity. CMs indolently progressed, accompanied by consolidation and small airway wall thickening, and originated from small nodules.

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<![CDATA[Cardio-Respiratory synchronized bSSFP MRI for high throughput in vivo lung tumour quantification]]> https://www.researchpad.co/article/5c6c757ed5eed0c4843cfe16

The identification and measurement of tumours is a key requirement in the study of tumour development in mouse models of human cancer. Disease burden in autochthonous tumours, such as those arising in the lung, can be seen with non-invasive imaging, but cannot be accurately measured using standard tools such as callipers. Lung imaging is further complicated in the mouse due to instabilities arising from the rapid but cyclic cardio-respiratory motions, and the desire to use free-breathing animals. Female A/JOlaHsd mice were either injected (i.p.) with PBS 0.1ml/10g body weight (n = 6), or 10% urethane/PBS 0.1ml/10g body weight (n = 12) to induce autochthonous lung tumours. Cardio-respiratory synchronised bSSFP MRI, at 200 μm isotropic resolution was performed at 8, 13 and 18 weeks post induction. Images from the same mouse at different time points were aligned using threshold-based segmented masks of the lungs (ITK-SNAP and MATLAB) and tumour volumes were determined via threshold-based segmentation (ITK-SNAP).Scan times were routinely below 10 minutes and tumours were readily identifiable. Image registration allowed serial measurement of tumour volumes as small as 0.056 mm3. Repetitive imaging did not lead to mouse welfare issues. We have developed a motion desensitised scan that enables high sensitivity MRI to be performed with high throughput capability of greater than 4 mice/hour. Image segmentation and registration allows serial measurement of individual, small tumours. This allows fast and highly efficient volumetric lung tumour monitoring in cohorts of 30 mice per imaging time point. As a result, adaptive trial study designs can be achieved, optimizing experimental and welfare outcomes.

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<![CDATA[Correlation between the native lung volume change and postoperative pulmonary function after single lung transplantation for lymphangioleiomyomatosis: Evaluation of lung volume by three-dimensional computed tomography volumetry]]> https://www.researchpad.co/article/5c6b26b2d5eed0c484289ec2

Purpose

Whereas native lung overinflation has been thought to happen in recipients of single lung transplantation for lymphangioleiomyomatosis because of its increased compliance, there is no study that has reported the details on the change of the native lung volume after single lung transplantation by three-dimensional computed tomography volumetry. The purpose of the present study was to evaluate the lung volume after single lung transplantation for lymphangioleiomyomatosis by three-dimensional computed tomography volumetry and investigate the correlation between the native lung volume change and postoperative pulmonary function.

Methods

We retrospectively reviewed the data of 17 patients who underwent single lung transplantation for lymphangioleiomyomatosis. We defined the ratio of the native lung volume to total lung volume (N/T ratio) as an indicator of overinflation of the native lung. In order to assess changes in the N/T ratio over time, we calculated the rate of change in the N/T ratio which is standardized by the N/T ratio at 1 year after single lung transplantation: rate of change in N/T ratio (%) = {(N/T ratio at a certain year)/(N/T ratio at 1 year)– 1}× 100.

Results

We investigated the correlations between the N/T ratio and the pulmonary function test parameters at 1 year and 5 years; however, there was no significant correlation between them. On the other hand, there was a significant negative correlation between the rate of change in the N/T ratio and that in forced expiratory volume in 1 second %predicted (%FEV1) at 5 years after single lung transplantation.

Conclusion

The single lung transplantation recipients for lymphangioleiomyomatosis showed increased rate of change in the N/T ratio in the long-time course after lung transplantation with the decrease of %FEV1. We expect that these cases will probably cause the overinflation of the native lung in the future.

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<![CDATA[Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning]]> https://www.researchpad.co/article/5c5ca307d5eed0c48441f020

Background

Exhaled aerosols from lungs have unique patterns, and their variation can be correlated to the underlying lung structure and associated abnormities. However, it is challenging to characterize such aerosol patterns and differentiate their difference because of their complexity. This challenge is even greater for small airway diseases, where the disturbance signals are weak.

Objectives and methods

The objective of this study is exploiting different feature extraction algorithms to develop a practical classifier to diagnose obstructive lung diseases using exhaled aerosol images. These include proper orthogonal decomposition (POD), principal component analysis (PCA), dynamic mode decomposition (DMD), and DMD with control (DMDC). Aerosol images were generated via physiology-based simulations in one normal and four diseased airway models in G7-9 bronchioles. The image data were classified using both the support vector machine (SVM) and random forest (RF) algorithms. The effectiveness of different features was evaluated by classification accuracy and misclassification rate.

Findings

Results show a significantly higher performance using dynamic feature extractions (DMD and DMDC) than static algorithms (POD and PCA). Adding the control variables to DMD further improved classification accuracy. Comparing the classification methods, RF persistently outperformed SVM for all types of features considered. While the performance of RF constantly increased with the number of features retained, the performance of SVM peaked at 50 and decreased thereafter. The 5-class classification accuracy was 94.8% using the DMDC-RF model and 93.0% using the DMD-RF model, both of which were higher than 87.0% in the previous study that used fractal dimension features.

Conclusion

Considering that disease progression is inherently a dynamic process, DMD(C)-based feature extraction preserves temporal information and is preferred over POD and PCA. Compared with hand-crafted features like fractals, feature extraction by DMD and DMDC is automatic and more accurate.

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<![CDATA[Correlation between maximal tumor diameter of fresh pathology specimens and computed tomography images in lung adenocarcinoma]]> https://www.researchpad.co/article/5c79aff5d5eed0c4841e3b32

The authors compared maximal tumor diameters between fresh lung tissue and axial and multiplanar reformatted chest computed-tomography (CT) images in lung adenocarcinoma and investigated the factors affecting tumor-size discrepancies. This study included 135 surgically resected lung adenocarcinomas. An experienced pulmonary pathologist aimed to cut the largest tumor section and measured pathological tumor size (PTS) in fresh specimens. Radiological maximal tumor sizes (RTS) were retrospectively measured on axial (RTSax) and multiplanar reformatted (RTSre) chest CT images. Mean PTS, RTSax, and RTSre were 19.13 mm, 18.63 mm, and 20.80 mm, respectively. RTSre was significantly larger than PTS (mean difference, 1.68 mm; p<0.001). RTSax was also greater than PTS for 6−10-mm and 11−20-mm tumors. PTS and RTS were strongly positively correlated (RTSax, r2 = 0.719, p<0.001; RTSre, r2 = 0.833, p<0.001). The intraclass correlation coefficient was 0.915 between PTS and RTSax and 0.954 between PTS and RTSre. Postoperative down-staging occurred in 11.0% and 27.4% of tumors on performing radiological staging using RTSax and RTSre, respectively. Postoperative up-staging occurred in 12.3% and 1.4% of tumors on performing radiological staging using RTSax and RTSre, respectively. Multiple linear regression revealed that pleural dimpling (p = 0.024) was an independent factor affecting differences between PTS and RTSax. Specimen type (p = 0.012) and tumor location (p = 0.020) were independent factors affecting differences between PTS and RTSre. In conclusion, RTSre was significantly larger than PTS and caused postoperative down-staging in 27.4% of the tumors. Reliability analysis revealed that RTSre was more strongly correlated with PTS than RTSax. Specimen type and anatomical tumor location influenced the measured size differences between PTS and RTSre.

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<![CDATA[High-pitch, 120 kVp/30 mAs, low-dose dual-source chest CT with iterative reconstruction: Prospective evaluation of radiation dose reduction and image quality compared with those of standard-pitch low-dose chest CT in healthy adult volunteers]]> https://www.researchpad.co/article/5c5369d9d5eed0c484a46906

Purpose

Objective of this study was to evaluate the effectiveness of the iterative reconstruction of high-pitch dual-source chest CT (IR-HP-CT) scanned with low radiation exposure compared with low dose chest CT (LDCT).

Materials and methods

This study was approved by the institutional review board. Thirty healthy adult volunteers (mean age 44 years) were enrolled in this study. All volunteers underwent both IR-HP-CT and LDCT. IR-HP-CT was scanned with 120 kVp tube voltage, 30 mAs tube current and pitch 3.2 and reconstructed with sinogram affirmed iterative reconstruction. LDCT was scanned with 120 kVp tube voltage, 40 mAs tube current and pitch 0.8 and reconstructed with B50 filtered back projection. Image noise, and signal to noise ratio (SNR) of the infraspinatus muscle, subcutaneous fat and lung parenchyma were calculated. Cardiac motion artifact, overall image quality and artifacts was rated by two blinded readers using 4-point scale. The dose-length product (DLP) (mGy∙cm) were obtained from each CT dosimetry table. Scan length was calculated from the DLP results. The DLP parameter was a metric of radiation output, not of patient dose. Size-specific dose estimation (SSDE, mGy) was calculated using the sum of the anteroposterior and lateral dimensions and effective radiation dose (ED, mSv) were calculated using CT dosimetry index.

Results

Approximately, mean 40% of SSDE (2.1 ± 0.2 mGy vs. 3.5 ± 0.3 mGy) and 34% of ED (1.0 ± 0.1 mSv vs. 1.5 ± 0.1 mSv) was reduced in IR-HP-CT compared to LDCT (P < 0.0001). Image noise was reduced in the IR-HP-CT (16.8 ± 2.8 vs. 19.8 ± 3.4, P = 0.0001). SNR of lung and aorta of IR-HP-CT showed better results compared with that of LDCT (22.2 ± 5.9 vs. 33.0 ± 7.8, 1.9 ± 0.4 vs 1.1 ± 0.3, P < 0.0001). The score of cardiac pulsation artifacts were significantly reduced on IR-HP-CT (3.8 ± 0.4, 95% confidence interval, 3.7‒4.0) compared with LDCT (1.6 ± 0.6, 95% confidence interval, 1.3‒1.8) (P < 0.0001). SNR of muscle and fat, beam hardening artifact and overall subjective image quality of the mediastinum, lung and chest wall were comparable on both scans (P ≥ 0.05).

Conclusion

IR-HP-CT with 120 kVp and 30 mAs tube setting in addition to an iterative reconstruction reduced cardiac motion artifact and radiation exposure while representing similar image quality compared with LDCT.

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<![CDATA[Estimation of lung cancer risk using homology-based emphysema quantification in patients with lung nodules]]> https://www.researchpad.co/article/5c50c491d5eed0c4845e893e

The purpose of this study was to assess whether homology-based emphysema quantification (HEQ) is significantly associated with lung cancer risk. This retrospective study was approved by our institutional review board. We included 576 patients with lung nodules (317 men and 259 women; age, 66.8 ± 12.3 years), who were selected from a database previously generated for computer-aided diagnosis. Of these, 283 were diagnosed with lung cancer, whereas the remaining 293 showed benign lung nodules. HEQ was performed and percentage of low-attenuation lung area (LAA%) was calculated on the basis of computed tomography scans. Statistical models were constructed to estimate lung cancer risk using logistic regression; sex, age, smoking history (Brinkman index), LAA%, and HEQ were considered independent variables. The following three models were evaluated: the base model (sex, age, and smoking history); the LAA% model (the base model + LAA%); and the HEQ model (the base model + HEQ). Model performance was assessed using receiver operating characteristic analysis and the associated area under the curve (AUC). Differences in AUCs among the models were evaluated using Delong’s test. AUCs of the base, LAA%, and HEQ models were 0.585, 0.593, and 0.622, respectively. HEQ coefficient was statistically significant in the HEQ model (P = 0.00487), but LAA% coefficient was not significant in the LAA% model (P = 0.199). Delong’s test revealed significant difference in AUCs between the LAA% and HEQ models (P = 0.0455). In conclusion, after adjusting for age, sex, and smoking history (Brinkman index), HEQ was significantly associated with lung cancer risk.

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<![CDATA[A software tool for the quantification of metastatic colony growth dynamics and size distributions in vitro and in vivo]]> https://www.researchpad.co/article/5c2e7fd5d5eed0c48451b9a6

The majority of cancer-related deaths are due to metastasis, hence improved methods to biologically and computationally model metastasis are required. Computational models rely on robust data that is machine-readable. The current methods used to model metastasis in mice involve generating primary tumors by injecting human cells into immune-compromised mice, or by examining genetically engineered mice that are pre-disposed to tumor development and that eventually metastasize. The degree of metastasis can be measured using flow cytometry, bioluminescence imaging, quantitative PCR, and/or by manually counting individual lesions from metastatic tissue sections. The aforementioned methods are time-consuming and do not provide information on size distribution or spatial localization of individual metastatic lesions. In this work, we describe and provide a MATLAB script for an image-processing based method designed to obtain quantitative data from tissue sections comprised of multiple subpopulations of disseminated cells localized at metastatic sites in vivo. We further show that this method can be easily adapted for high throughput imaging of live or fixed cells in vitro under a multitude of conditions in order to assess clonal fitness and evolution. The inherent variation in mouse studies, increasing complexity in experimental design which incorporate fate-mapping of individual cells, result in the need for a large cohort of mice to generate a robust dataset. High-throughput imaging techniques such as the one that we describe will enhance the data that can be used as input for the development of computational models aimed at modeling the metastatic process.

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