New insights on COPD imaging via CT and MRI

Multidetector-row computed tomography (MDCT) can be used to quantify morphological features and investigate structure/function relationship in COPD. This approach allows a phenotypical definition of COPD patients, and might improve our understanding of disease pathogenesis and suggest new therapeutical options. In recent years, magnetic resonance imaging (MRI) has also become potentially suitable for the assessment of ventilation, perfusion and respiratory mechanics. This review focuses on the established clinical applications of CT, and novel CT and MRI techniques, which may prove valuable in evaluating the structural and functional damage in COPD

Lung disease classification using GLCM and deep features from different deep learning architectures with principal component analysis

Lung disease classification is an important stage in implementing a Computer Aided Diagnosis (CADx) system. CADx systems can aid doctors as a second rater to increase diagnostic accuracy for medical applications. It has also potential to reduce waiting time and increasing patient throughput when hospitals high workload. Conventional lung classification systems utilize textural features. However textural features may not be enough to describe properties of an image. Deep features are an emerging source of features that can combat the weaknesses of textural features. The goal of this study is to propose a lung disease classification framework using deep features from five different deep networks and comparing its results with the conventional Gray-level Co-occurrence Matrix (GLCM). This study used a dataset of 81 diseased and 15 normal patients with five levels of High Resolution Computed Tomography (HRCT) slices. A comparison of five different deep learning networks namely, Alexnet, VGG16, VGG19, Res50 and Res101, with textural features from Gray-level Co-occurrence Matrix (GLCM) was performed. This study used a K-fold validation protocol with K = 2, 3, 5 and 10. This study also compared using five classifiers; Decision Tree, Support Vector Machine, Linear Discriminant Analysis, Regression and k-nearest neighbor (k-NN) classifiers. The usage of PCA increased the classification accuracy from 92.01% to 97.40% when using k-NN classifier. This was achieved with only using 14 features instead of the initial 1000 features. Using SVM classifier, a maximum accuracy of 100% was achieved when using all five of the deep learning features. Thus deep features show a promising application for classifying diseased and normal lungs

Multidetector row CT for imaging the paediatric tracheobronchial tree

The introduction of multidetector row computed tomography (MDCT) scanners has altered the approach to imaging the paediatric thorax. In an environment where the rapid acquisition of CT data allows general hospitals to image children instead of referring them to specialist paediatric centres, it is vital that general radiologists have access to protocols appropriate for paediatric applications. Thus a dramatic reduction in the delivered radiation dose is ensured with optimal contrast bolus delivery and timing, and inappropriate repetition of the scans is avoided. This article focuses on the main principles of volumetric CT imaging that apply generically to all MDCT scanners. We describe the reconstruction techniques for imaging the paediatric thorax and the low-dose protocols used in our institution on a 16-slice detector CT scanner. Examples of the commonest clinical applications are also given

Diagnosis and monitoring of systemic sclerosis-associated interstitial lung disease using high-resolution computed tomography

Patients with systemic sclerosis are at high risk of developing systemic sclerosis-associated interstitial lung disease. Symptoms and outcomes of systemic sclerosis-associated interstitial lung disease range from subclinical lung involvement to respiratory failure and death. Early and accurate diagnosis of systemic sclerosis-associated interstitial lung disease is therefore important to enable appropriate intervention. The most sensitive and specific way to diagnose systemic sclerosis-associated interstitial lung disease is by high-resolution computed tomography, and experts recommend that high-resolution computed tomography should be performed in all patients with systemic sclerosis at the time of initial diagnosis. In addition to being an important screening and diagnostic tool, high-resolution computed tomography can be used to evaluate disease extent in systemic sclerosis-associated interstitial lung disease and may be helpful in assessing prognosis in some patients. Currently, there is no consensus with regards to frequency and scanning intervals in patients at risk of interstitial lung disease development and/or progression. However, expert guidance does suggest that frequency of screening using high-resolution computed tomography should be guided by risk of developing interstitial lung disease. Most experienced clinicians would not repeat high-resolution computed tomography more than once a year or every other year for the first few years unless symptoms arose. Several computed tomography techniques have been developed in recent years that are suitable for regular monitoring, including low-radiation protocols, which, together with other technologies, such as lung ultrasound and magnetic resonance imaging, may further assist in the evaluation and monitoring of patients with systemic sclerosis-associated interstitial lung disease. A video abstract to accompany this article is available at: https://www.globalmedcomms.com/respiratory/Khanna/HRCTinSScILD

Pulmonary manifestations in smoking-related diseases : clinical studies with emphasis on chronic obstructive pulmonary disease and rheumatoid arthritis

Smoking is a risk factor for a number of diseases including chronic obstructive pulmonary disease (COPD) and rheumatoid arthritis (RA). Cigarette smoke initiates an inflammatory response which leads to structural changes in the airways and in the lung parenchyma. The present work was undertaken in order to shed light on pulmonary manifestations of two common smoking-related diseases, COPD and RA. A retrospective review on bronchoalveolar lavage (BAL) constituents, encompassing 132 smokers with normal lung function and 44 ex-smokers, was performed. Two hundred and ninety- five neversmokers served as reference group. The median (5-95 percentile) cell concentration in smokers were 382.1 (189.7-864.3) X 106 /L which was higher compared to the neversmokers. The majority of cells were alveolar macrophages (median 96.7%; range 73.2-99.6%, lymphocytes (2%; range 0.2-26%) and neutrophils (0.6%; range 0-6%). Cell concentration was positively correlated to cumulative smoking history. One hundred and five patients with newly-diagnosed RA, (70% ACPA+), underwent high resolution computer tomography (HRCT) examination and a sub group of 23 patients also performed bronchoscopy and BAL. A group of 43 non-diseased smokers and never smokers were examined as control. Parenchymal lung abnormality on HRCT was found in 63% of ACPA+ compared to 37% ACPA- RA patients, 30% control regardless of smoking status. The level of ACPA was higher in BAL fluid than sera in ACPA+ RA patients. Forty smokers with normal lung function,(mean 35 pack-years), 40 healthy never-smokers, and 40 COPD-patients of GOLD,I-II, (38 PY), performed HRCT. In addition BAL was performed. Percentage of pixelsbetween -750- -900 HU (%HDS) was calculated. Lung density was increased in smokers (44.0% ± 5.8%) compared to never smokers (38.3 ± 5.8%), p<0.001. Cell concentration in BAL was positively correlated to lung density in smokers (r=0.50, p<0.001). Females had denser lungs than males. Regional air trapping was assessed on expiatory HRCT on 40 never-smokers, 40 smokers and 40 COPD-patients. Emphysema, micronoduli, bronchial wall thickening was determined on inspiratory HRCT. Air trapping index (AI) was quantified as the ratio of mean lung attenuation at expiration and inspiration. Regional air trapping was present in 63% of smokers and 45% of never smokers. Smokers with visible regional air trapping had an AI of 0.81, while smokers without visible air trapping had an AI of 0.91. A negative correlation between AI and neutrophils in BAL was observed. Smokers with regional air trapping had better lung function and less emphysema compared to smokers without. We demonstrate inflammatory and structural changes in the lungs in smokers by means of HRCT and BAL. These changes are apparent even before clinical symptoms occur. The studies highlight the heterogeneity in smoking-related diseases which may be of importance in terms of disease progression and patient phenotypes