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

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530J (15 March 2019); doi: 10.1117/12.2512849
Show Author Affiliations
Jonathan L. MacNeil, Univ. of Western Ontario (Canada)
Dante P. I. Capaldi, Univ. of Western Ontario (Canada)
Standford Univ. (United States)
Rachel L. Eddy, Univ. of Western Ontario (Canada)
Andrew R. Westcott, Univ. of Western Ontario (Canada)
Alexander M. Matheson, Univ. of Western Ontario (Canada)
Andrea L. Barker, Univ. of Western Ontario (Canada)
Cathy Ong-Ly, Univ. of Western Ontario (Canada)
David G. McCormack, Univ. of Western Ontario (Canada)
Grace Parraga, Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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