Development and evaluation of pulmonary imaging multi-parametric response maps for deep phenotyping of chronic obstructive pulmonary disease

Jonathan L. MacNeil; Dante P. I. Capaldi; Rachel L. Eddy; Andrew Westcott; Alexander M. Matheson; Andrea L. Barker; Cathy Ong-Ly; David G. McCormack M.D.; Grace Parraga

doi:10.1117/12.2512849

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15 March 2019 Development and evaluation of pulmonary imaging multi-parametric response maps for deep phenotyping of chronic obstructive pulmonary disease

Jonathan L. MacNeil, Dante P. I. Capaldi, Rachel L. Eddy, Andrew Westcott, Alexander M. Matheson, Andrea L. Barker, Cathy Ong-Ly, David G. McCormack M.D., Grace Parraga

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Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109530J (2019) https://doi.org/10.1117/12.2512849
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Objective: Our aim was to develop and evaluate multi-parametric response maps derived from pulmonary x-ray computed tomography (CT), ¹H and hyperpolarized ³He static ventilation and diffusion-weighted magnetic resonance imaging (MRI). These maps were generated to phenotype patients with chronic obstructive pulmonary disease (COPD) based on the presence of airways disease, air trapping, emphysema, alveolar distension, and ventilation defects. Methods: To generate thoracic imaging multi-parametric response maps (mPRM), multispectral ¹H, ³He and CT images were segmented and co-registered. ¹H and ³He MR images were segmented using a semi-automated segmentation algorithm, the diffusion weighted MR images were segmented using a threshold-based algorithm and CT images were segmented using Pulmonary Workstation 2.0 (VIDA Diagnostics, Coralville, IA). The volume-matched segmented ¹H/³He maps were registered using landmark rigid registration. The ³He maps/the diffusion weighted images were registered using an intensity-based rigid registration. CT-to-MRI co-registration was achieved using modality-independent neighborhood descriptor (MIND) deformable registration; inspiratory and expiratory CT were co-registered using an affine registration with a deformable step provided by the NiftyReg toolkit. The co-registered thoracic maps were used to generate multiparametric maps. Results: mPRM maps were generated for six different voxel classifications with increasing disease abnormality/severity as follows: 1) ventilated voxels with >-856HU/>-950HU and normal apparent diffusion coefficient (ADC) values, 2) ventilated voxels with <-856HU/<-950HU and abnormal ADC values, 3) ventilated voxels with >-856HU/>-950HU and normal ADC values, 4) ventilated voxels with <-856HU/<-950HU and abnormal ADC values, 5) unventilated voxels with >-856HU/>-950HU, and, 6) unventilated voxels with <-856HU/<-950HU. Conclusion: mPRM measurements were automated in a dedicated pipeline for MRI and CT measurements to phenotype COPD patients.

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Jonathan L. MacNeil, Dante P. I. Capaldi, Rachel L. Eddy, Andrew Westcott, Alexander M. Matheson, Andrea L. Barker, Cathy Ong-Ly, David G. McCormack M.D., and Grace Parraga "Development and evaluation of pulmonary imaging multi-parametric response maps for deep phenotyping of chronic obstructive pulmonary disease", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530J (15 March 2019); https://doi.org/10.1117/12.2512849

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