Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/27778
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Williams, J | - |
dc.contributor.author | Ahlqvist, H | - |
dc.contributor.author | Cunningham, A | - |
dc.contributor.author | Kirby, A | - |
dc.contributor.author | Katz, I | - |
dc.contributor.author | Fleming, J | - |
dc.contributor.author | Conway, J | - |
dc.contributor.author | Cunningham, S | - |
dc.contributor.author | Ozel, A | - |
dc.contributor.author | Wolfram, U | - |
dc.date.accessioned | 2023-12-01T13:32:04Z | - |
dc.date.available | 2023-12-01T13:32:04Z | - |
dc.date.issued | 2023-03-02 | - |
dc.identifier.citation | Williams, J. et al. (2023) 'Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks', arXiv:2303.01036v1 [physics.med-ph], (preprint), pp. 1 - 37. doi: 10.48550/arXiv.2303.01036. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27778 | - |
dc.description | 37 pages main text (including frontmatter). 9 figures. Additional supplementary material. [v1] Thu, 2 Mar 2023 07:47:07 UTC (14,131 KB). The file archived on this institutional repository is an arXiv preprint. It may not have been certified by peer review. | en_US |
dc.description.abstract | 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. | en_US |
dc.description.sponsorship | Simulations reported in this study were performed on Oracle cloud computing platform, funded by Open Clouds Research Environments (OCRE) ‘Cloud Funding for Research’. JW was funded by a 2019 PhD Scholarship from the Carnegie-Trust for the Universities of Scotland. The in vivo deposition data used in this study was obtained from a project sponsored by Air Liquide. | en_US |
dc.format.extent | 1 - 37 (37) | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Cornell University | en_US |
dc.rights | Copyright © The Author(s) 2023. This is an arXiv preprint licensed under a CC BY: Creative Commons Attribution (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | medical physics (physics.med-ph | en_US |
dc.subject | computer vision and pattern recognition (cs.CV) | en_US |
dc.subject | fluid dynamics (physics.flu-dyn) | en_US |
dc.title | Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.48550/arXiv.2303.01036 | - |
dc.relation.isPartOf | arXiv | - |
pubs.issue | preprint | - |
pubs.publication-status | Submitted | - |
dc.identifier.eissn | 2331-8422 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Health Sciences Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Preprint.pdf | Copyright © The Author(s) 2023. This is an arXiv preprint licensed under a CC BY: Creative Commons Attribution (https://creativecommons.org/licenses/by/4.0/). | 12.12 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License