Texture analysis of thoracic CT to predict hyperpolarized gas MRI lung function

Andrew Westcott; Dante P. I. Capaldi; David G. McCormack M.D.; Aaron Fenster; Grace Parraga

doi:10.1117/12.2512851

15 March 2019 Texture analysis of thoracic CT to predict hyperpolarized gas MRI lung function

Andrew Westcott, Dante P. I. Capaldi, David G. McCormack M.D., Aaron Fenster, Grace Parraga

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109530H (2019) https://doi.org/10.1117/12.2512851
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Objective: Hyperpolarized noble gas magnetic resonance imaging (MRI) provides valuable insights on lung function, and yet is not widely available, whereas thoracic x-ray computed tomography (CT) protocols are nearly universally accessible. Our aim was to develop a texture analysis pipeline to train and test machine learning classifiers, predicting MRI-based ventilation metrics from single-volume thoracic CT in patients with chronic obstructive pulmonary disease (COPD). Methods: MR ventilation maps were generated and registered to thoracic CT datasets. Images were segmented into volumes of interest (15x15x15mm), resulting in approximately 6,000 volumes-of-interest per subject participant. 85 firstorder and texture features were calculated to describe each volume, including a new texture feature based on the size and occurrence of CT clusters (we called the cluster volume matrix), which is similar to run-length-matrix. A Logistic Regression, Linear Support Vector Machine and Quadratic Support Vector Machine were trained using 5-fold crossvalidation on a cohort of seven subjects. The highest performing classification model was then applied to a test cohort of three subjects. Results: There was qualitative spatial agreement for the experimental MRI ventilation maps and the CT-predicted functional maps. The training set was classified with 71% accuracy, while the test set was classified with 66% accuracy and area under the curve (AUC) = 0.72. Conclusions: This proof-of-concept study demonstrated feasibility in a small group of patients with moderate classification accuracy. Novel insights will be used to optimize this approach with future application to a larger heterogeneous patient cohort.

Conference Presentation

Citation Download Citation

Andrew Westcott, Dante P. I. Capaldi, David G. McCormack M.D., Aaron Fenster, and Grace Parraga "Texture analysis of thoracic CT to predict hyperpolarized gas MRI lung function", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530H (15 March 2019); https://doi.org/10.1117/12.2512851

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