Abstract
This thesis describes two novel catheter-based 2D-3D cardiac image registration algorithms for overlaying preoperative 3D MR or CT data onto intraoperative fluoroscopy, and fusing electroanatomical data onto clinical images. The work is intended for use in cardiac catheterisation procedures. To fulfil this objective, the algorithms must be accurate, robust and minimally disruptive to the clinical workflow.The first algorithm relies on the catheterisation of vessels of the heart and registers by minimising a vessel-radius-weighted distance between the catheters and corresponding vessel centrelines. A novelty here is a global-fit search strategy that considers all vessel branches during registration, adding robustness and avoiding manual branch selection.
Another contribution to knowledge is an analysis of catheter configurations for registration. Results show that accuracy is highly dependent on the catheter configuration, and that using a coronary vessel (CV) with the aorta (Ao) was most accurate, yielding mean 3D target registration errors (TRE) between 0.55 and 7.0 mm with phantom data. Using two large-diameter vessels was least accurate, with TRE between 10 and 43 mm, and should be avoided. When applied to clinical data, registrations with the CV/Ao configuration resulted an estimated mean 2D-TRE of 5.9 mm, on average.
The second 2D-3D registration algorithm extends the novelty of exploring catheter configurations by registering using catheters looped inside chambers of the heart. In phantom experiments, two-view registration yielded an average accuracy of 4.0 mm 3D-TRE (7.8-mm capture range). Using a single view, average reprojection distance was 2.7 mm (6.0-mm capture range). Application of the algorithm to a clinical dataset resulted in an estimated average 2D-TRE of 10 mm. Single view registrations are ideal when biplane X-ray acquisition is undesirable and for correcting bulk patient motion.
In current practice, registration is performed manually. The algorithms in this thesis can register with comparable accuracy to manual registration, but are automated and can therefore fit better with the clinical workflow.
| Date of Award | 2014 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Kawal Rhode (Supervisor) & Graeme Penney (Supervisor) |