Chronic lung lesions in COVID-19 survivors: predictive clinical model.

OBJECTIVE: This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection. DESIGN: This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospital discharge. The pulmonary function was assessed using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO2), spirometry (forced vital capacity (FVC)) and chest X-ray (CXR) during an in-person consultation. Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO2, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT. SETTING: A tertiary hospital in Sao Paulo, Brazil. PARTICIPANTS: 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged ≥18 years. PRIMARY OUTCOME MEASURE: A predictive clinical model for lung lesion detection on chest CT. RESULTS: There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. Almost half of them (48%) had significant pulmonary abnormalities, including ground-glass opacities, parenchymal bands, reticulation, traction bronchiectasis and architectural distortion. The machine learning model, including the results of 257 patients with complete data on mMRC, SpO2, FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO2 and FVC (sensitivity, 0.85±0.08; specificity, 0.70±0.06; F1-score, 0.79±0.06 and area under the curve, 0.80±0.07). CONCLUSION: A predictive clinical model based on CXR, mMRC, oximetry and spirometry data can accurately screen patients with lung lesions after SARS-CoV-2 infection. Given that these examinations are highly accessible and low cost, this protocol can be automated and implemented in different countries for early detection of COVID-19 sequelae.

Complementary use of priors for pulmonary imaging with electrical impedance and ultrasound computed tomography.

For medical professionals caring for patients undergoing mechanical ventilation due to respiratory failure, the ability to quickly and safely obtain images of pulmonary function at the patient's bedside would be highly desirable. Such images could be used to provide early warnings of developing pulmonary pathologies in real time, thereby reducing the incidence of complications and improving patient outcomes. Electrical impedance tomography (EIT) and low-frequency ultrasound computed tomography (USCT) are two imaging techniques with the potential to provide real-time non-ionizing pulmonary monitoring in the ICU setting, and each method has its own unique advantages as well as drawbacks. In this work, we describe a new algorithm for a system in which the strengths of the two modalities are combined in a complementary fashion. Specifically, preliminary reconstructions from each modality are used as priors to stabilize subsequent reconstructions, providing improved spatial resolution, sharper organ boundaries, and enhanced appearance of pathologies and other features. Results are validated using three numerically simulated thoracic phantoms representing pulmonary pathologies.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction.

BACKGROUND: Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. METHOD: An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. RESULTS: The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. CONCLUSIONS: In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.

Segmentação do lúmen em imagens de IOCT usando Fuzzy Connectedness e Reconstrução Binária Morfológica / Lumen Segmentation in IOCT images using Fuzzy and Binary Morphological Reconstruction

No ano 2010, doenças cardiovasculares (CVD) causaram 33% do total das mortes no Brasil. Tomografia Ótica Coerente Intravascular (IOCT) é uma tecnologia que oferece imagens in vivo para detecção e monitoramento da progressão de CVD. O exame de IOCT permite mais precisão no diagnóstico; contudo, ainda é pequena a variedade de métodos quantitativos aplicados a IOCT na literatura, em comparação à outras modalidades relacionadas. Portanto neste trabalho é proposto um método de segmentação do lúmen, baseado em uma combinação de Fuzzy Connectedness, com múltiplas funções de afinidade, e Operações Morfológicas. As funções de afinidade usadas neste trabalho são: (I) Clássica, (II) Pesos Dinâmicos e (III) Bhattacharyya. Esta última é baseada no coeficiente de Bhattacharyya, utilizado habitualmente para speckle tracking. Primeiro, características não desejadas da imagem são atenuadas. Depois, informações da parede do vaso são obtidas utilizando Fuzzy Connectedness e um processo de binarização dinâmico. Finalmente, operações morfológicas são realizadas para melhorar o lúmen segmentado. Para avaliar o método proposto, um conjunto de 130 imagens advindas de humanos, porcos, e coelhos foram segmentadas e comparadas com seus respectivos "Gold Standards" feitos por especialistas. Uma média de verdadeiros positivos (TP%) = 98,08 e de falsos positivos (FP%) = 2,34 foram obtidas. Com isso, o método proposto resultou em uma maior eficácia do que os estudos publicados anteriormente, encorajando seu uso.

In 2010 cardiovascular disease (CVD) caused 33% of the total deaths in Brazil. Intravascular Optical Coherent Tomography (IOCT) is an imaging technology that provides in vivo detection and monitoring of the progression of coronary heart disease. IOCT exam allows more accurate diagnoses; nonetheless, the set of quantitative methods applied to IOCT in the literature is small compared to other related modalities. Therefore, the proposed approach presents a lumen segmentation method, based on a combination of Fuzzy Connectedness, with multiple affinity functions, and Morphological Operations. The affinity functions used in this work are: (I) classical, (II) Dynamic weights (III) Bhattacharyya. The latter is based on the Bhattacharyya coefficient, commonly used for speckle tracking. Firstly, unwanted features of the image are attenuated. Then, vessel-wall information is obtained using Fuzzy Connectedness and dynamic binarization process. Finally, morphological operations are performed to improve the segmented lumen. To evaluate the proposed method, a set of 130 images from humans, pigs and rabbits were segmented and compared to their corresponding gold standard made by experts. An average of true positive (TP%) = 98.08, and false positive (FP%) = 2.34 were obtained. Hence, the use of the proposed method is suggested since it has yielded higher efficiency than previously published studies.