ArXiv Preprint
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.
Josh Williams, Haavard Ahlqvist, Alexander Cunningham, Andrew Kirby, Ira Katz, John Fleming, Joy Conway, Steve Cunningham, Ali Ozel, Uwe Wolfram
2023-03-02
