Open-source virtual bronchoscopy for image guided navigation

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dc.contributor.advisor Cantillon-Murphy, Padraig en
dc.contributor.author Nardelli, Pietro
dc.date.accessioned 2016-05-23T09:06:28Z
dc.date.available 2016-05-23T09:06:28Z
dc.date.issued 2016
dc.date.submitted 2016
dc.identifier.citation Nardelli, P. 2016. Open-source virtual bronchoscopy for image guided navigation. PhD Thesis, University College Cork. en
dc.identifier.endpage 228 en
dc.identifier.uri http://hdl.handle.net/10468/2596
dc.description.abstract This thesis describes the development of an open-source system for virtual bronchoscopy used in combination with electromagnetic instrument tracking. The end application is virtual navigation of the lung for biopsy of early stage cancer nodules. The open-source platform 3D Slicer was used for creating freely available algorithms for virtual bronchscopy. Firstly, the development of an open-source semi-automatic algorithm for prediction of solitary pulmonary nodule malignancy is presented. This approach may help the physician decide whether to proceed with biopsy of the nodule. The user-selected nodule is segmented in order to extract radiological characteristics (i.e., size, location, edge smoothness, calcification presence, cavity wall thickness) which are combined with patient information to calculate likelihood of malignancy. The overall accuracy of the algorithm is shown to be high compared to independent experts' assessment of malignancy. The algorithm is also compared with two different predictors, and our approach is shown to provide the best overall prediction accuracy. The development of an airway segmentation algorithm which extracts the airway tree from surrounding structures on chest Computed Tomography (CT) images is then described. This represents the first fundamental step toward the creation of a virtual bronchoscopy system. Clinical and ex-vivo images are used to evaluate performance of the algorithm. Different CT scan parameters are investigated and parameters for successful airway segmentation are optimized. Slice thickness is the most affecting parameter, while variation of reconstruction kernel and radiation dose is shown to be less critical. Airway segmentation is used to create a 3D rendered model of the airway tree for virtual navigation. Finally, the first open-source virtual bronchoscopy system was combined with electromagnetic tracking of the bronchoscope for the development of a GPS-like system for navigating within the lungs. Tools for pre-procedural planning and for helping with navigation are provided. Registration between the lungs of the patient and the virtually reconstructed airway tree is achieved using a landmark-based approach. In an attempt to reduce difficulties with registration errors, we also implemented a landmark-free registration method based on a balanced airway survey. In-vitro and in-vivo testing showed good accuracy for this registration approach. The centreline of the 3D airway model is extracted and used to compensate for possible registration errors. Tools are provided to select a target for biopsy on the patient CT image, and pathways from the trachea towards the selected targets are automatically created. The pathways guide the physician during navigation, while distance to target information is updated in real-time and presented to the user. During navigation, video from the bronchoscope is streamed and presented to the physician next to the 3D rendered image. The electromagnetic tracking is implemented with 5 DOF sensing that does not provide roll rotation information. An intensity-based image registration approach is implemented to rotate the virtual image according to the bronchoscope's rotations. The virtual bronchoscopy system is shown to be easy to use and accurate in replicating the clinical setting, as demonstrated in the pre-clinical environment of a breathing lung method. Animal studies were performed to evaluate the overall system performance. en
dc.description.sponsorship Heath Research Board (HRB HRA_POR/2012/31) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2016, Pietro Nardelli. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Virtual bronchoscopy en
dc.subject Airway segmentation en
dc.subject Lung nodule malignancy prediction en
dc.subject Electromagnetic tracking for lung navigation en
dc.title Open-source virtual bronchoscopy for image guided navigation en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral Degree (Structured) en
dc.type.qualificationname PHD (Engineering) en
dc.internal.availability Full text available en
dc.check.info No embargo required en
dc.description.version Accepted Version
dc.contributor.funder Health Research Board en
dc.description.status Not peer reviewed en
dc.internal.school Electrical and Electronic Engineering en
dc.check.type No Embargo Required
dc.check.reason No embargo required en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Not applicable en
ucc.workflow.supervisor p.cantillonmurphy@ucc.ie
dc.internal.conferring Summer 2016 en


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© 2016, Pietro Nardelli. Except where otherwise noted, this item's license is described as © 2016, Pietro Nardelli.
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