Automated bidirectional coupling of multiscale models of aerosol dosimetry: Validation with subject-specific deposition data

Kuprat, Andrew P.; Price, Owen; Asgharian, Bahman; Singh, Rajesh; Colby, Sean; Yugulis, Kevin; Corley, Richard A.; Darquenne, Chantal

doi:10.1016/j.jaerosci.2023.106233

Cited by 7 publications

(2 citation statements)

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“…Our approach has the benefit that it requires less radiation (only one CT or X-ray image required) to model regional ventilation to each lung. Kuprat et al [114] determined particle deposition in the deep lung using a multiple-path particle deposition (called 'MPPD') model, which showed good agreement with experimental measurements of the steady-state dosage retained in the patients lungs after five breaths [115]. Implementing an OpenFOAM-MPPD model coupling for predictions of particle deposition in the deep lung is a potential next step to allow us to validate deposition in the deep lung.…”

Section: Discussionmentioning

confidence: 96%

“…Additionally, their outlet boundary conditions uses a fixed flowrate based on the regional lung space volume change from CT images at full inhalation and exhalation. This similar approach was also used for defining 3D domain outlet boundary conditions in a recent validation study [114]. In contrast, the outlet pressure in our study is approximated with a model based on lung space volume fraction, resistance and compliance (Section 2.5).…”

Section: Discussionmentioning

confidence: 99%

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Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks

Williams,

Ahlqvist,

Cunningham

et al. 2024

PLoS ONE

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For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%