High-resolution Computed Tomography Lung Images Research Articles

Pulmonary Emphysema Disease Classification Model Using Deep Structured Techniques

Emphysema is normally employed in the sense of morphology, and thus, imaging the modalities has a significant part in detecting the disorder. Most importantly, the High-Resolution Computed Tomography (HRCT) is a trustworthy component for illustrating the emphysema pathology. Hence, the pulmonary emphysema diagnosis at the starting stage is very important and can secure the life of a human. When there is a failure in the technique, there is a possibility of the death that displays the importance of effective diagnosis. The endotracheal intubation’s delay is prevented by implementing distinct mechanisms that result in various requirements. The presented work develops a deep learning-aided pulmonary emphysema disease classification model that is implemented to defeat the above difficulties. At first, the images are accumulated from a standard dataset. Then, the collected images are provided to the phase of segmentation. Here, the newly developed Adaptive Trans-Residual Dense UNet (ATRDUNet) is employed to perform the segmentation process. Also, the recommended ATRDUNet model helps to locate the disease in the appropriate location. Moreover, the performance of the segmentation process is enriched to fine-tune the parameter using the Fitness Revised Position of Wild Horse Optimizer (FRP-WHO). While optimizing the parameters effectively, it tends to handle the complex issues to strengthen the model accurately. The segmented results from the ATRDUNet are passed to Vision Transformer-based Residual DenseNet with LSTM layer (ViTrans-RDLSTM) for getting the final classified outcome. To this end, the analysis of the suggested model is correlated to find the effectiveness of the recommended model. However, these results are examined with the help of standard performance measures. Also, the experimentation is carried out using the standard “Automatic emphysema detection using weakly labeled HRCT lung images” dataset to attain better outcomes in the developed model. The findings of the recommended approach show 95% in terms of accuracy. Also, the statistical analysis of the developed model shows 23.9%, 12.3%, 15.2%, and 13.0% better performance than BWO, CO, HBA, and WHO in terms of mean analysis to show enriched performance.

Feasibility of high-frequency percussions in people with severe acquired brain injury and tracheostomy: an observational study.

People with severe acquired brain injury (pwSABI) frequently experience pulmonary complications. Among these, atelectasis can occur as a result of pneumonia, thus increasing the chance of developing acute respiratory failure. Respiratory physiotherapy contribution to the management of atelectasis in pwSABI is yet poorly understood. We conducted a retrospective analysis on 15 non-cooperative pwSABI with tracheostomy and spontaneously breathing, hospitalized and treated with high-frequency percussion physiotherapy between September 2018 and February 2021 at the Neurological Rehabilitation Unit of the IRCCS "S.Maria Nascente - Fondazione Don Gnocchi", Milan. Our primary aim was to investigate the feasibility of such a physiotherapy intervention method. Then, we assessed changes in respiratory measures (arterial blood gas analysis and peripheral night-time oxygen saturation) and high-resolution computed tomography lung images, evaluated before and after the physiotherapy treatment. The radiological measures were a modified radiological atelectasis score (mRAS) assigned by two radiologists, and an opacity score automatically provided by the software CT Pneumonia Analysis® that identifies the regions of abnormal lung patterns. Treatment diaries showed that all treatments were completed, and no adverse events during treatment were registered. Among the 15 pwSABI analyzed, 8 were treated with IPV® and 7 with MetaNeb®. After a median of 14 (I-III quartile=12.5-14.5) days of treatment, we observed a statistical improvement in various arterial blood gas measures and peripheral night-time oxygen saturation measures. We also found radiological improvement or stability in more than 80% of pwSABI. In conclusion, our physiotherapy approach was feasible, and we observed respiratory parameters and radiological improvements. Using technology to assess abnormal tomographic patterns could be of interest to disentangle the short-term effects of respiratory physiotherapy on non-collaborating people.

Content-based image retrieval system for HRCT lung images: assisting radiologists in self-learning and diagnosis of Interstitial Lung Diseases

Content-based Image Retrieval (CBIR) is a technique that can exploit the wealth of the data stored in a repository and help radiologists in decision making by providing references to the image in hand. A CBIR system for High-Resolution Computed Tomography (HRCT) lung images depicting signs of Interstitial Lung Diseases (ILDs) can be built and used as a self-learning tool for budding radiologists. The study of a few lung image retrieval systems available in the literature identifies some important issues that need to be taken care of. In most of the works, the creation of the reference database involves painstaking manual activity, which is time-consuming and needs skilled labor. A lot of human interventions are required, particularly for the proper delineation of the region of interest (ROI) that represents pathology in each of the images in a database. In most cases, the size of the ROIs representing different disease findings are fixed (i.e., either a fixed size square or circle), which at times may not be a proper representation of the disease pattern and as a consequence, it might limit the system’s performance. Until date, a few learning-based approaches have been developed for content-based image retrieval of HRCT lung images, which either learn the similarity using a classifier or get trained through relevance feedback. For medical image analysis, the availability of labelled data for learning makes these learning-based retrieval systems meaningful as it enhances their performance in contrast to their simple distance-based counterpart. The objective of this paper is to develop a CBIR system for ILDs that is reliable and needs minimal human intervention. The paper evaluates the performance of three popular segmentation algorithms. It identifies the best for the effective and automated delineation of an arbitrary region of interest (AROI) depicting the sign of ILDs on HRCT images of the thorax in contrast to the manual delineation of fixed size ROI. This minimizes the manual effort for the creation and maintenance of the reference database, as well as the manual delineation of AROI during query formation. Moreover, AROI created through the automated clustering is found to have a better representation of disease patterns. Three recently proposed general-purpose learning based CBIR techniques are implemented and tested for retrieval of HRCT lung images depicting the sign of ILDs. The best method is suggested after careful evaluation of all the competing techniques.

OP0038 IMPROVING RISK-STRATIFICATION OF RHEUMATOID ARTHRITIS PATIENTS FOR INTERSTITIAL LUNG DISEASE

Background: Interstitial lung disease (ILD) is the most common pulmonary manifestation of rheumatoid arthritis (RA). It has emerged in recent series as a key prognostic factor including survival. The big challenge for rheumatologists is now the risk-stratification of RA patients for ILD. Chest high-resolution computed tomography (HRCT) is the gold standard for RA-ILD diagnosis, but costs and ionizing radiation may limit its use in clinical practice. Thus, circulating biomarkers could aid in this risk-stratification. Objectives: to evaluate the merit of 3 circulating markers for the diagnosis and the progression of RA-ILD. Methods: We included consecutive patients with RA, >18 years of age, from 3 tertiary rheumatology centers (Paris, France, Tokyo, Japan and Zurich, Switzerland) over a 36-month period. All patients had at least one chest HRCT during the inclusion period. In the subset of French patients with ILD, HRCT lung images were obtained both at baseline (time of blood sample collection) and at a follow-up visit. The ILD status of patients with RA was established by chest HRCT. The chest HRCT pattern was classified as usual interstitial pneumonia, UIP or non-specific interstitial pneumonia, NSIP, by the local radiologist. Serum levels of lung epithelial-derived surfactant protein D, SDP, CCL-18 et Krebs von den Lungen-6 glycoprotein, KL-6 were measured by ELISA. Results: 147 patients were included (age: 66+/-12 ans, 69% of women, disease duration 11+/-10 years). Among these patients, 40 (27%) had fibrosing ILD on HRCT: 21 had a UIP pattern, 17 a NSIP pattern, and 2 had NSIP associated with chronic obstructive pulmonary disease. SPD (21.91+/-2.17 vs. 15.76+/-1.34 ng/mL, p=0.017) CCL18 (102+/-13 vs. 78+/-5 ng/mL, p=0.026) and KL6 (961+/-128 vs. 376+/-26 U/mL, p 15% as compared to patients with milder disease. ROC curve analysis to assess the diagnostic abilities of the three markers for the diagnosis of RA-ILD showed a superiority of KL-6 (Area under the curve, AUC: 0.79 95% CI 0.72-0.86) compared to SPD (AUC: 0.66 95% CI 0.58-0.74) and CCL18 (AUC: 0.62, 95% CI 0.53-0.70) (Figure 1D). The sensitivity of KL-6 for the diagnosis of RA-ILD was 68% with a specificity of 83%. In the French subset with longitudinal data (n=15), extension of ILD was detected on 7 patients. Baseline KL6 serum levels were significantly increased in patients who experienced ILD progression (1987+/-1294 vs. 799+/-375 U/mL, p=0.027) (Figure 1E). The degree of ILD progression on HRCT was also proportional to baseline KL-6 concentrations (Figure 1F). Conclusion: KL-6 is relevant for the diagnosis and the prognosis of RA-ILD. It may be used as a circulating non-invasive first-line marker to stratify for indication of HRCT. Indeed, given the emerging lung issues in RA patients, this simple and highly reproducible marker, which is already available in routine care in some countries, could be a good prerequisite to chest HRCT in rheumatology clinics. Disclosure of Interests: Jerome Avouac Grant/research support from: Pfizer, Bristol Myers Squibb, Consultant of: Sanofi, Bristol Myers Squibb, Abbvie, Boerhinger, Nordic Pharma, Speakers bureau: Sanofi, Bristol Myers Squibb Abbvie, MSD, Pfizer, Nordic Pharma, Alexia Steelandt: None declared, Anne Cauvet: None declared, Yuichiro Shirai: None declared, Masataka Kuwana Grant/research support from: Acetelion, Consultant of: Acetelion, Bayer, Chugai, Corbus Pharmaceuticals, CSL Behring and Reata Pharmaceuticals. He was a member of the SENSCIS trial Steering Committee (Boehringer Ingelheim), Oliver Distler Grant/research support from: Grants/Research support from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd. and Mitsubishi Tanabe; he also holds the issued Patent on mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143)., Consultant of: Consultancy fees from Actelion, Acceleron Pharma, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, Catenion, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi and UCB, Speakers bureau: Speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Pfizer and Roche, Yannick Allanore Shareholder of: Sanofi, Roche, Consultant of: Actelion, Bayer, BMS, Boehringer Ingelheim, Inventiva, Sanofi

Radiological patterns of primary graft dysfunction after lung transplantation evaluated by 64-multi-slice computed tomography: a descriptive study

We evaluated the diagnostic value of high-resolution computed tomography (HRCT) images generated from 64 detector multi-slice CT scanners (HRCT(64-MSCT) imaging) in relation to primary graft dysfunction (PGD) after lung-transplantation (LUTX) in a pilot study. PGD has mortality rates ranging from 17 to 50% over a 90-day period. Detailed HRCT lung images, reconstructed using 64-MSCT, may aid diagnostic and therapeutic efforts in PGD. Thirty-two patients were scanned four times within a year post-LUTX, in a single-centre prospective study. HRCT lung images were reviewed, evaluated and scored by two observers, for ground-glass (GG) opacities, consolidation, septal thickening (ST) and pulmonary embolism. Image and PGD scores were compared in each patient. GG and consolidation changes were largely present up until 2 weeks post-LUTX, and markedly reduced by the 12th week. ST was predominantly found in patients with PGD. There were no vascular changes found at CT angiographies. The most severe cases of GG opacities and consolidation were found in patients with PGD. ST seems to be an important indicator of PGD. HRCT(64-MSCT) imaging may be a useful tool for the identification of pathological features of PGD not detected by classical evaluation in patients undergoing LUTX.

Image based diagnostic aid system for interstitial lung diseases

Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, we propose a new image based system for classification of lung tissue patterns. The proposed system comprises three stages. In the first stage, the parenchyma region in HRCT lung images is separated using a set of thresholding, filtering and morphological operators. In the second stage, two sets of overcomplete wavelet filters, namely discrete wavelet frames and rotated wavelet frames, are utilized to extract features from defined regions of interest (ROIs) within parenchyma. Then, in the third stage, the fuzzy k-nearest neighbor algorithm is employed to perform the pattern classification. The proposed method is tested for classifying four different lung tissue patterns (ground glass, honeycombing, reticular, and normal) selected from a database of 339 images from 17 subjects. After applying our technique to classify these patterns in isolated ROIs, we extend the classification scheme to the whole lung in order to produce quantitative scores of abnormalities in lung parenchyma of patients. The performance of the proposed method is compared with two state-of-the-art texture based methods for lung tissue characterization and is also validated against experienced observers. The average kappa statistic of the agreement between two radiologists and the computer was found to be 0.6543 where as the average kappa statistic for the inter-observer agreement was 0.6848. We also performed an experiment to show the correlation between pulmonary function test parameters and quantitative scores of computerized system. Results show that extent of HRCT findings correlates significantly with functional impairment. The computer system is shown to approach the performance of the expert observers in diagnosing regions of interest and can help to produce objective measures of abnormal patterns in lung HRCT images.

Primary graft dysfunction; possible evaluation by high resolution computed tomography, and suggestions for a scoring system☆

We have reviewed and discussed current knowledge on existing scoring systems regarding high resolution computed tomography (HRCT) images for the assessment of primary graft dysfunction (PGD) after lung transplantation. Adult respiratory distress syndrome (ARDS) has been more widely studied and appears to have many morphological features similar to what is found in PGD, and might, therefore, be usefully extrapolated to PGD. Principles of HRCT, scoring systems based on HRCT and various terms describing PGD were reviewed and summarized. The sensitivity, inter-intra observer variability, and reproducibility of these systems were discussed. Lastly, the future perspectives for 64-multi-slice computed tomography (MSCT) in relation to PGD were discussed. Few studies on scoring systems of lung tissue by HRCT in ARDS patients and idiopathic pulmonary fibrosis (IPF) patients were found. Most studies were performed on patients with cystic fibrosis (CF). Sensitivity of HRCT for the detection of parenchymal changes is superior to other imaging methods. High levels of reproducibility are achievable amongst observers who score HRCT lung images. Development of standardized criteria that specify the inclusion/exclusion criteria of patients, pilot testing, and training investigators through review of disagreements, were possibilities suggested for decreasing inter/intra observer variability. Factors affecting the image attenuation (Hounsfield numbers) and thus, the reproducibility of CT densitometric measurements were of minimal influence. Studies have reported on how lung tissue images, derived by HRCT, can be scored and graded. There does not seem to be a golden standard for evaluating these images, which makes comparison between methods challenging. These scoring systems assess the presence, severity, and extent of parenchymal change in the lung. HRCT is considered relevant and superior in evaluating disease severity, disease progression, and in evaluating the effects of therapy regimes in the lung. It is, however, not clear to what extent these scoring methods may be implemented for grading PGD. Further efforts could be made to standardize scoring methods for lung tissue with regards to PGD.

Multi-level classification of emphysema in HRCT lung images

Emphysema is a common chronic respiratory disorder characterised by the destruction of lung tissue. It is a progressive disease where the early stages are characterised by a diffuse appearance of small air spaces, and later stages exhibit large air spaces called bullae. A bullous region is a sharply demarcated region of emphysema. In this paper, it is shown that an automated texture-based system based on co-training is capable of achieving multiple levels of emphysema extraction in high-resolution computed tomography (HRCT) images. Co-training is a semi-supervised technique used to improve classifiers that are trained with very few labelled examples using a large pool of unseen examples over two disjoint feature sets called views. It is also shown that examples labelled by experts can be incorporated within the system in an incremental manner. The results are also compared against “density mask”, currently a standard approach used for emphysema detection in medical image analysis and other computerized techniques used for classification of emphysema in the literature. The new system can classify diffuse regions of emphysema starting from a bullous setting. The classifiers built at different iterations also appear to show an interesting correlation with different levels of emphysema, which deserves more exploration.

Knowledge-Driven Multidimensional Indexing Structure for Biomedical Media Database Retrieval

Today, biomedical media data are being generated at rates unimaginable only years ago. Content-based retrieval of biomedical media from large databases is becoming increasingly important to clinical, research, and educational communities. In this paper, we present the recently developed entropy balanced statistical (EBS) k-d tree and its applications to biomedical media, including a high-resolution computed tomography (HRCT) lung image database and the first real-time protein tertiary structure search engine. Our index utilizes statistical properties inherent in large-scale biomedical media databases for efficient and accurate searches. By applying concepts from pattern recognition and information theory, the EBS k-d tree is built through top-down decision tree induction. Experimentation shows similarity searches against a protein structure database of 53 363 structures consistently execute in less than 8.14 ms for the top 100 most similar structures. Additionally, we have shown improved retrieval precision over adaptive and statistical k-d trees. Retrieval precision of the EBS k-d tree is 81.6% for content-based retrieval of HRCT lung images and 94.9% at 10% recall for protein structure similarity search. The EBS k-d tree has enormous potential for use in biomedical applications embedded with ground-truth knowledge and multidimensional signatures.