ResearchPad - thoracic-imaging https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![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[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[Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia]]> https://www.researchpad.co/article/elastic_article_15647 <![CDATA[Chest CT Features of COVID-19 in Rome, Italy]]> https://www.researchpad.co/article/elastic_article_15432 The standard for diagnosis of SARS-CoV-2 virus is reverse transcription polymerase chain reaction (RT-PCR) test, but chest CT may play a complimentary role in the early detection of COVID-19 pneumonia.PurposeTo investigate CT features of patients with COVID-19 in Rome, Italy, and to compare the accuracy of CT with RT-PCR.MethodsIn this prospective study from March 4, 2020, until March 19, 2020, consecutive patients with suspected COVID-19 infection and respiratory symptoms were enrolled. Exclusion criteria were: chest CT with contrast medium performed for vascular indications, patients who refused chest CT or hospitalization, and severe CT motion artifact. All patients underwent RT-PCR and chest CT. Diagnostic performance of CT was calculated using RT-PCR as reference. Chest CT features were calculated in a subgroup of RT-PCR-positive and CT-positive patients. CT features of hospitalized patients and patient in home isolation were compared by using Pearson chi squared test.ResultsOur study population comprised 158 consecutive study participants (83 male and 75 female, mean age 57 y ±17). Fever was observed in 97/158 (61%), cough in 88/158 (56%), dyspnea in 52/158 (33%), lymphocytopenia in 95/158 (60%), increased C-reactive protein level in 139/158 (88%), and elevated lactate dehydrogenase in 128/158 (81%) study participants. Sensitivity, specificity, and accuracy of CT were 97% (60/62)[95% IC, 88-99%], 56% (54/96)[95% IC,45-66%] and 72% (114/158)[95% IC 64-78%], respectively. In the subgroup of RT-PCR-positive and CT-positive patients, ground-glass opacities (GGO) were present in 58/58 (100%), multilobe and posterior involvement were both present in 54/58 (93%), bilateral pneumonia in 53/58 (91%), and subsegmental vessel enlargement (> 3 mm) in 52/58 (89%) of study participants.ConclusionThe typical pattern of COVID-19 pneumonia in Rome, Italy, was peripherally ground-glass opacities with multilobe and posterior involvement, bilateral distribution, and subsegmental vessel enlargement (> 3 mm). Chest CT sensitivity was high (97%) but with lower specificity (56%). ]]> <![CDATA[Small Solitary Ground-Glass Nodule on CT as an Initial Manifestation of Coronavirus Disease 2019 (COVID-19) Pneumonia]]> https://www.researchpad.co/article/Nf154870e-177b-4f54-95d4-b6da0fc787e8

The 2019 novel coronavirus (2019-nCoV) outbreak in Wuhan, Hubei Province, China in 2019 led to large numbers of people being infected and developing atypical pneumonia (coronavirus disease 2019, COVID-19). Typical imaging manifestations of patients infected with 2019-nCoV has been reported, but we encountered an atypical radiological manifestation on baseline computed tomography (CT) images in three patients from Wuhan, China infected with the 2019-nCoV. Surprisingly, the only similar CT finding was a solitary sub-centimeter ground-glass nodule adjacent to bronchovascular bundles, which could be easily overlooked. In addition, the follow-up images in these patients showed how COVID-19 pneumonia evolved from these small nodules. The radiologic manifestation of the three cases will expand contemporary understanding of COVID-19.

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<![CDATA[Usefulness of CT-Guided Percutaneous Transthoracic Needle Lung Biopsies in Patients with Suspected Pulmonary Infection]]> https://www.researchpad.co/article/N3afbb800-4c00-44fc-a859-92173f592f97

Objective

This study aimed to evaluate the clinical benefits and risks of CT-guided percutaneous transthoracic needle lung biopsies (PTNBs) in patients with a suspected pulmonary infection.

Materials and Methods

This study included 351 CT-guided PTNBs performed in 342 patients (mean age, 58.9 years [range, 17–91 years]) with suspected pulmonary infection from January 2010 to December 2016. The proportion of biopsies that revealed the causative organism for pulmonary infection and that influenced patient's treatment were measured. Multivariate analyses were performed to identify factors associated with PTNB that revealed the causative organism or affected the treatment. Finally, the complication rate was measured.

Results

CT-guided PTNB revealed the causative organism in 32.5% of biopsies (114/351). The presence of necrotic components in the lesion (odds ratio [OR], 1.7; 95% confidence interval [CI], 1.1–2.7; p = 0.028), suspected pulmonary tuberculosis (OR, 2.0; 95% CI, 1.2–3.5; p = 0.010), and fine needle aspiration (OR, 2.5; 95% CI, 1.1–5.8; p = 0.037) were factors associated with biopsies that revealed the causative organism. PTNB influenced patient's treatment in 40.7% (143/351) of biopsies. The absence of leukocytosis (OR, 1.9; 95% CI, 1.0–3.7; p = 0.049), presence of a necrotic component in the lesion (OR, 2.4; 95% CI, 1.5–3.8; p < 0.001), and suspected tuberculosis (OR, 1.7; 95% CI, 1.0–2.8; p = 0.040) were factors associated with biopsies that influenced the treatment. The overall complication rate of PTNB was 19% (65/351).

Conclusion

In patients with suspected pulmonary infection, approximately 30–40% of CT-guided PTNBs revealed the causative organism or affected the treatment. The complication rate of PTNB for suspected pulmonary infection was relatively low.

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<![CDATA[Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges]]> https://www.researchpad.co/article/N8416d306-abba-40f8-829b-23b4ac7c6914

Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.

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<![CDATA[2019 Novel Coronavirus (COVID-19) Pneumonia with Hemoptysis as the Initial Symptom: CT and Clinical Features]]> https://www.researchpad.co/article/N453df9b5-c797-4204-8d88-2cd42d25f06e

Recently, some global cases of 2019 novel coronavirus (COVID-19) pneumonia have been caused by second- or third-generation transmission of the viral infection, resulting in no traceable epidemiological history. Owing to the complications of COVID-19 pneumonia, the first symptom and imaging features of patients can be very atypical and early diagnosis of COVID-19 infections remains a challenge. It would aid radiologists and clinicians to be aware of the early atypical symptom and imaging features of the disease and contribute to the prevention of infected patients being missed.

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<![CDATA[Mediastinal Emphysema, Giant Bulla, and Pneumothorax Developed during the Course of COVID-19 Pneumonia]]> https://www.researchpad.co/article/Nc3acd324-501e-4ac2-ad20-fe0cf2a9aeb8

The coronavirus disease 2019 (COVID-19) pneumonia is a recent outbreak in mainland China and has rapidly spread to multiple countries worldwide. Pulmonary parenchymal opacities are often observed during chest radiography. Currently, few cases have reported the complications of severe COVID-19 pneumonia. We report a case where serial follow-up chest computed tomography revealed progression of pulmonary lesions into confluent bilateral consolidation with lower lung predominance, thereby confirming COVID-19 pneumonia. Furthermore, complications such as mediastinal emphysema, giant bulla, and pneumothorax were also observed during the course of the disease.

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<![CDATA[Chest Radiographic and CT Findings of the 2019 Novel Coronavirus Disease (COVID-19): Analysis of Nine Patients Treated in Korea]]> https://www.researchpad.co/article/N1f07a6a9-0813-4d38-9a51-b6cc0b2100a7

Objective

This study presents a preliminary report on the chest radiographic and computed tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) pneumonia in Korea.

Materials and Methods

As part of a multi-institutional collaboration coordinated by the Korean Society of Thoracic Radiology, we collected nine patients with COVID-19 infections who had undergone chest radiography and CT scans. We analyzed the radiographic and CT findings of COVID-19 pneumonia at baseline. Fisher's exact test was used to compare CT findings depending on the shape of pulmonary lesions.

Results

Three of the nine patients (33.3%) had parenchymal abnormalities detected by chest radiography, and most of the abnormalities were peripheral consolidations. Chest CT images showed bilateral involvement in eight of the nine patients, and a unilobar reversed halo sign in the other patient. In total, 77 pulmonary lesions were found, including patchy lesions (39%), large confluent lesions (13%), and small nodular lesions (48%). The peripheral and posterior lung fields were involved in 78% and 67% of the lesions, respectively. The lesions were typically ill-defined and were composed of mixed ground-glass opacities and consolidation or pure ground-glass opacities. Patchy to confluent lesions were primarily distributed in the lower lobes (p = 0.040) and along the pleura (p < 0.001), whereas nodular lesions were primarily distributed along the bronchovascular bundles (p = 0.006).

Conclusion

COVID-19 pneumonia in Korea primarily manifested as pure to mixed ground-glass opacities with a patchy to confluent or nodular shape in the bilateral peripheral posterior lungs. A considerable proportion of patients with COVID-19 pneumonia had normal chest radiographs.

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<![CDATA[False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases]]> https://www.researchpad.co/article/Nbcf7aea6-b203-420a-8ffa-3a28f3d7b17c

The epidemic of 2019 novel coronavirus, later named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still gradually spreading worldwide. The nucleic acid test or genetic sequencing serves as the gold standard method for confirmation of infection, yet several recent studies have reported false-negative results of real-time reverse-transcriptase polymerase chain reaction (rRT-PCR). Here, we report two representative false-negative cases and discuss the supplementary role of clinical data with rRT-PCR, including laboratory examination results and computed tomography features. Coinfection with SARS-COV-2 and other viruses has been discussed as well.

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<![CDATA[2019 Novel Coronavirus (COVID-19) Pneumonia: Serial Computed Tomography Findings]]> https://www.researchpad.co/article/Nab6898a4-ed5d-4316-8c5a-510f13d44fea

From December 2019, Coronavirus disease 2019 (COVID-19) pneumonia (formerly known as the 2019 novel Coronavirus [2019-nCoV]) broke out in Wuhan, China. In this study, we present serial CT findings in a 40-year-old female patient with COVID-19 pneumonia who presented with the symptoms of fever, chest tightness, and fatigue. She was diagnosed with COVID-19 infection confirmed by real-time reverse-transcriptase-polymerase chain reaction. CT showed rapidly progressing peripheral consolidations and ground-glass opacities in both lungs. After treatment, the lesions were shown to be almost absorbed leaving the fibrous lesions.

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<![CDATA[Novel Coronavirus Pneumonia Outbreak in 2019: Computed Tomographic Findings in Two Cases]]> https://www.researchpad.co/article/N29c06d55-76ae-4be6-8e3a-000a74ed64f1

Since the 2019 novel coronavirus (2019-nCoV or officially named by the World Health Organization as COVID-19) outbreak in Wuhan, Hubei Province, China in 2019, there have been a few reports of its imaging findings. Here, we report two confirmed cases of 2019-nCoV pneumonia with chest computed tomography findings of multiple regions of patchy consolidation and ground-glass opacities in both lungs. These findings were characteristically located along the bronchial bundle or subpleural lungs.

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<![CDATA[Follow-up chest radiographic findings in patients with MERS-CoV after recovery]]> https://www.researchpad.co/article/5bed0a4dd5eed0c48424752c

Purpose:

To evaluate the follow-up chest radiographic findings in patients with Middle East respiratory syndrome coronavirus (MERS-CoV) who were discharged from the hospital following improved clinical symptoms.

Materials and Methods:

Thirty-six consecutive patients (9 men, 27 women; age range 21–73 years, mean ± SD 42.5 ± 14.5 years) with confirmed MERS-CoV underwent follow-up chest radiographs after recovery from MERS-CoV. The 36 chest radiographs were obtained at 32 to 230 days with a median follow-up of 43 days. The reviewers systemically evaluated the follow-up chest radiographs from 36 patients for lung parenchymal, airway, pleural, hilar and mediastinal abnormalities. Lung parenchyma and airways were assessed for consolidation, ground-glass opacity (GGO), nodular opacity and reticular opacity (i.e., fibrosis). Follow-up chest radiographs were also evaluated for pleural thickening, pleural effusion, pneumothorax and lymphadenopathy. Patients were categorized into two groups: group 1 (no evidence of lung fibrosis) and group 2 (chest radiographic evidence of lung fibrosis) for comparative analysis. Patient demographics, length of ventilations days, number of intensive care unit (ICU) admission days, chest radiographic score, chest radiographic deterioration pattern (Types 1-4) and peak lactate dehydrogenase level were compared between the two groups using the student t-test, Mann-Whitney U test and Fisher's exact test.

Results:

Follow-up chest radiographs were normal in 23 out of 36 (64%) patients. Among the patients with abnormal chest radiographs (13/36, 36%), the following were found: lung fibrosis in 12 (33%) patients GGO in 2 (5.5%) patients, and pleural thickening in 2 (5.5%) patients. Patients with lung fibrosis had significantly greater number of ICU admission days (19 ± 8.7 days; P value = 0.001), older age (50.6 ± 12.6 years; P value = 0.02), higher chest radiographic scores [10 (0-15.3); P value = 0.04] and higher peak lactate dehydrogenase levels (315-370 U/L; P value = 0.001) when compared to patients without lung fibrosis.

Conclusion:

Lung fibrosis may develop in a substantial number of patients who have recovered from Middle East respiratory syndrome coronavirus (MERS-CoV). Significantly greater number of ICU admission days, older age, higher chest radiographic scores, chest radiographic deterioration patterns and peak lactate dehydrogenase levels were noted in the patients with lung fibrosis on follow-up chest radiographs after recovery from MERS-CoV.

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<![CDATA[Quantitative Image Quality and Histogram-Based Evaluations of an Iterative Reconstruction Algorithm at Low-to-Ultralow Radiation Dose Levels: A Phantom Study in Chest CT]]> https://www.researchpad.co/article/5bf330bdd5eed0c4843e5146

Objective

To describe the quantitative image quality and histogram-based evaluation of an iterative reconstruction (IR) algorithm in chest computed tomography (CT) scans at low-to-ultralow CT radiation dose levels.

Materials and Methods

In an adult anthropomorphic phantom, chest CT scans were performed with 128-section dual-source CT at 70, 80, 100, 120, and 140 kVp, and the reference (3.4 mGy in volume CT Dose Index [CTDIvol]), 30%-, 60%-, and 90%-reduced radiation dose levels (2.4, 1.4, and 0.3 mGy). The CT images were reconstructed by using filtered back projection (FBP) algorithms and IR algorithm with strengths 1, 3, and 5. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were statistically compared between different dose levels, tube voltages, and reconstruction algorithms. Moreover, histograms of subtraction images before and after standardization in x- and y-axes were visually compared.

Results

Compared with FBP images, IR images with strengths 1, 3, and 5 demonstrated image noise reduction up to 49.1%, SNR increase up to 100.7%, and CNR increase up to 67.3%. Noteworthy image quality degradations on IR images including a 184.9% increase in image noise, 63.0% decrease in SNR, and 51.3% decrease in CNR, and were shown between 60% and 90% reduced levels of radiation dose (p < 0.0001). Subtraction histograms between FBP and IR images showed progressively increased dispersion with increased IR strength and increased dose reduction. After standardization, the histograms appeared deviated and ragged between FBP images and IR images with strength 3 or 5, but almost normally-distributed between FBP images and IR images with strength 1.

Conclusion

The IR algorithm may be used to save radiation doses without substantial image quality degradation in chest CT scanning of the adult anthropomorphic phantom, down to approximately 1.4 mGy in CTDIvol (60% reduced dose).

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<![CDATA[Middle East Respiratory Syndrome-Coronavirus Infection: A Case Report of Serial Computed Tomographic Findings in a Young Male Patient]]> https://www.researchpad.co/article/5bc6988640307c42d894946e

Radiologic findings of Middle East respiratory syndrome (MERS), a novel coronavirus infection, have been rarely reported. We report a 30-year-old male presented with fever, abdominal pain, and diarrhea, who was diagnosed with MERS. A chest computed tomographic scan revealed rapidly developed multifocal nodular consolidations with ground-glass opacity halo and mixed consolidation, mainly in the dependent and peripheral areas. After treatment, follow-up imaging showed that these abnormalities markedly decreased but fibrotic changes developed.

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<![CDATA[CT Quantification of Lungs and Airways in Normal Korean Subjects]]> https://www.researchpad.co/article/5b3e5ca8463d7e44f6232382

Objective

To measure and compare the quantitative parameters of the lungs and airways in Korean never-smokers and current or former smokers (“ever-smokers”).

Materials and Methods

Never-smokers (n = 119) and ever-smokers (n = 45) who had normal spirometry and visually normal chest computed tomography (CT) results were retrospectively enrolled in this study. For quantitative CT analyses, the low attenuation area (LAA) of LAAI-950, LAAE-856, CT attenuation value at the 15th percentile, mean lung attenuation (MLA), bronchial wall thickness of inner perimeter of a 10 mm diameter airway (Pi10), total lung capacity (TLCCT), and functional residual capacity (FRCCT) were calculated based on inspiratory and expiratory CT images. To compare the results between groups according to age, sex, and smoking history, independent t test, one way ANOVA, correlation test, and simple and multiple regression analyses were performed.

Results

The values of attenuation parameters and volume on inspiratory and expiratory quantitative computed tomography (QCT) were significantly different between males and females (p < 0.001). The MLA and the 15th percentile value on inspiratory QCT were significantly lower in the ever-smoker group than in the never-smoker group (p < 0.05). On expiratory QCT, all lung attenuation parameters were significantly different according to the age range (p < 0.05). Pi10 in ever-smokers was significantly correlated with forced expiratory volume in 1 second/forced vital capacity (r = −0.455, p = 0.003). In simple and multivariate regression analyses, TLCCT, FRCCT, and age showed significant associations with lung attenuation (p < 0.05), and only TLCCT was significantly associated with inspiratory Pi10.

Conclusion

In Korean subjects with normal spirometry and visually normal chest CT, there may be significant differences in QCT parameters according to sex, age, and smoking history.

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<![CDATA[Can Peripheral Bronchopleural Fistula Demonstrated on Computed Tomography be Treated Conservatively? A Retrospective Analysis]]> https://www.researchpad.co/article/5bc6d4fe40307c648397aab3

Purpose

Peripheral bronchopleural fistulas (BPF) are communications between a peripheral bronchus or the lung parenchyma and the pleural space. Although reported cases with peripheral BPF might have typical symptoms, we postulate that there may be BPF patients without typical symptoms who are diagnosed on computed tomography (CT) for the first time.

Materials and Methods

We searched retrospectively for how frequently BPF is found on CT in cases with known or suspected empyema or hydropneumothorax. Also, we examined the clinical charts to ascertain if a diagnosis of BPF was suspected in the CT reports or clinically, and to determine the outcome of each case.

Results

Thirteen thoracic cavities of 12 patients were included in this study. Of these, BPF was suspected clinically in only 1. Mention in the CT report about the presence of BPF was found in 2 cases. An apparent finding of BPF on CT was found in 7 of 13 (53%) thoracic cavities of 6 cases. The outcomes were that 1 patient died 1 month later due to multiple organ failure, and 1 patient was discharged subsequently after CT. In the other 10 cases, there was no exacerbation of the symptom regardless of definite evidence of BPF on CT.

Conclusions

In conclusion, when there is hydropneumothorax on CT, it is important for radiologists to diligently search for findings of peripheral BPF and to document it. However, a reference about the need for a surgical approach for BPF may not be required.

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