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Chronic Obstructive Pulmonary Disease: Thoracic Ct Texture Analysis And Machine Learning To Predict Pulmonary Ventilation, Andrew Westcott, Dante P I Capaldi, David G Mccormack, Aaron D Ward, Aaron Fenster, Grace Parraga
Chronic Obstructive Pulmonary Disease: Thoracic Ct Texture Analysis And Machine Learning To Predict Pulmonary Ventilation, Andrew Westcott, Dante P I Capaldi, David G Mccormack, Aaron D Ward, Aaron Fenster, Grace Parraga
Medical Biophysics Publications
Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods In this prospective study (
Oscillometry And Pulmonary Magnetic Resonance Imaging In Asthma And Copd, Rachel L Eddy, Andrew Westcott, Geoffrey N Maksym, Grace Parraga, Ronald J Dandurand
Oscillometry And Pulmonary Magnetic Resonance Imaging In Asthma And Copd, Rachel L Eddy, Andrew Westcott, Geoffrey N Maksym, Grace Parraga, Ronald J Dandurand
Medical Biophysics Publications
Developed over six decades ago, pulmonary oscillometry has re-emerged as a noninvasive and effort-independent method for evaluating respiratory-system impedance in patients with obstructive lung disease. Here, we evaluated the relationships between hyperpolarized