Mr. Andrew R. Westcott Profile

Andrew R. Westcott

at Robarts Research Institute

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Author

Publications (3)

Proceedings Article | 14 February 2021 Presentation + Paper

Hyperpolarized gas magnetic resonance imaging texture analysis and machine learning to explain accelerated lung function decline in ex-smokers with and without COPD

Maksym Sharma, Andrew Westcott, David McCormack, Grace Parraga

Proceedings Volume 11600, 116000E (2021) https://doi.org/10.1117/12.2580451

KEYWORDS: Magnetic resonance imaging, Chronic obstructive pulmonary disease, Machine learning, Lung, Modeling, Image segmentation, Image classification, Feature selection, Feature extraction, Data modeling

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Proceedings Article | 15 March 2019 Presentation + Paper

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

Andrew Westcott, Dante P. Capaldi, David McCormack, Aaron Fenster, Grace Parraga

Proceedings Volume 10953, 109530H (2019) https://doi.org/10.1117/12.2512851

KEYWORDS: Magnetic resonance imaging, Computed tomography, X-ray computed tomography, Chronic obstructive pulmonary disease, Lung, Image segmentation, Image registration, Machine learning, Feature extraction, Medicine

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Proceedings Article | 15 March 2019 Presentation + Paper

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

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

Proceedings Volume 10953, 109530J (2019) https://doi.org/10.1117/12.2512849

KEYWORDS: Image segmentation, Chronic obstructive pulmonary disease, Magnetic resonance imaging, Image registration, Gold, Lung, Computed tomography, X-ray computed tomography, Image processing algorithms and systems, Emphysema

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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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