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Functional respiratory imaging : opening the black box

(2013)
Author
Promoter
(UGent) and Wilfried De Backer
Organization
Abstract
In respiratory medicine, several quantitative measurement tools exist that assist the clinicians in their diagnosis. The main issue with these traditional techniques is that they lack sensitivity to detect changes and that the variation between different measurements is very high. The result is that the development of respiratory drugs is the most expensive of all drug development. This limits innovation, resulting in an unmet need for sensitive quantifiable outcome parameters in pharmacological development and clinical respiratory practice. In this thesis, functional respiratory imaging (FRI) is proposed as a tool to tackle these issues. FRI is a workflow where patient specific medical images are combined with computational fluid dynamics in order to give patient specific local information of anatomy and functionality in the respiratory system. A robust high throughput automation system is designed in order get a workflow that is of a high quality, consistent and fast. This makes it possible to apply this technology on large datasets as typically seen in clinical trials. FRI is performed on 486 unique geometries of patients with various pathologies such as asthma, chronic obstructive lung disease, sleep apnea and cystic fibrosis. This thesis shows that FRI can have an added value in multiple research domains. The high sensitivity and specificity of FRI make it very well suited as a tool to make decisions early in the development process of a device or drug. Furthermore, FRI also seems to be an interesting technology to gain better insight in rare diseases and can possibly be useful in personalized medicine.
Keywords
Respiratory Medicine, Computational Fluid Dynamics, Patient Specific, Scalability, Pharmacological Development, Functional Respiratory Imaging, Large datasets, Sensitivity, Design Tool

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Citation

Please use this url to cite or link to this publication:

MLA
Van Holsbeke, Cedric. “Functional Respiratory Imaging : Opening the Black Box.” 2013 : n. pag. Print.
APA
Van Holsbeke, C. (2013). Functional respiratory imaging : opening the black box. Ghent University. Faculty of Engineering and Architecture, Ghent, Belgium.
Chicago author-date
Van Holsbeke, Cedric. 2013. “Functional Respiratory Imaging : Opening the Black Box”. Ghent, Belgium: Ghent University. Faculty of Engineering and Architecture.
Chicago author-date (all authors)
Van Holsbeke, Cedric. 2013. “Functional Respiratory Imaging : Opening the Black Box”. Ghent, Belgium: Ghent University. Faculty of Engineering and Architecture.
Vancouver
1.
Van Holsbeke C. Functional respiratory imaging : opening the black box. [Ghent, Belgium]: Ghent University. Faculty of Engineering and Architecture; 2013.
IEEE
[1]
C. Van Holsbeke, “Functional respiratory imaging : opening the black box,” Ghent University. Faculty of Engineering and Architecture, Ghent, Belgium, 2013.
@phdthesis{4186876,
  abstract     = {In respiratory medicine, several quantitative measurement tools exist that assist the clinicians in their diagnosis. The main issue with these traditional techniques is that they lack sensitivity to detect changes and that the variation between different measurements is very high. The result is that the development of respiratory drugs is the most expensive of all drug development. This limits innovation, resulting in an unmet need for sensitive quantifiable outcome parameters in pharmacological development and clinical respiratory practice. In this thesis, functional respiratory imaging (FRI) is proposed as a tool to tackle these issues. FRI is a workflow where patient specific medical images are combined with computational fluid dynamics in order to give patient specific local information of anatomy and functionality in the respiratory system. A robust high throughput automation system is designed in order get a workflow that is of a high quality, consistent and fast. This makes it possible to apply this technology on large datasets as typically seen in clinical trials. FRI is performed on 486 unique geometries of patients with various pathologies such as asthma, chronic obstructive lung disease, sleep apnea and cystic fibrosis. This thesis shows that FRI can have an added value in multiple research domains. The high sensitivity and specificity of FRI make it very well suited as a tool to make decisions early in the development process of a device or drug. Furthermore, FRI also seems to be an interesting technology to gain better insight in rare diseases and can possibly be useful in personalized medicine.},
  author       = {Van Holsbeke, Cedric},
  isbn         = {9789085786412},
  keywords     = {Respiratory Medicine,Computational Fluid Dynamics,Patient Specific,Scalability,Pharmacological Development,Functional Respiratory Imaging,Large datasets,Sensitivity,Design Tool},
  language     = {eng},
  pages        = {var. p.},
  publisher    = {Ghent University. Faculty of Engineering and Architecture},
  school       = {Ghent University},
  title        = {Functional respiratory imaging : opening the black box},
  year         = {2013},
}