Koch M (2020)
Publication Language: English
Publication Type: Thesis
Publication year: 2020
Pages Range: 140
URI: https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/16020
As one of the most common types of heart arrhythmias, atrial fibrillation is a severe disorder of the heart rhythm affecting the left atrium. Among the most serious
complications count stroke and tachycardia mediated cardiomyopathy. Moreover, recurrent symptoms impair patients’ quality of life and functional status. Common
treatment options for rhythm control are antiarrhythmic drug therapy and
catheter ablation procedures. Cardiac ablation procedures are usually performed
minimally invasive in electrophysiology labs. During these procedures, ablation
catheters are navigated into the heart chamber via the venous system to ablate
specific areas involved in conduction of irregular impulses.
For treatment of paroxysmal atrial fibrillation, ipsilateral pulmonary vein isolation is a common ablation pattern. Interventional X-ray imaging is commonly employed for catheter guidance and control. Furthermore, electroanatomic mapping
systems can be used for treatment of complex arrhythmia. Ablation planning
data can be used during X-ray guided procedures as well as included in mapping systems to support the physician by supplying further context information.
In this thesis, artificial intelligence based methods for interventional treatment
of atrial fibrillation ablation procedures are presented. We developed an algorithm for automatic lesion planning targeted at pulmonary vein isolation procedures
for treatment of atrial fibrillation. This method facilitates a landmark-constrained non-rigid registration algorithm for accurate alignment of left atrium heart models.
Procedure planning data is generated for the individual patient anatomy to be superimposed during the ablation procedure. A quantitative and qualitative
evaluation of the algorithm was performed on clinical datasets. The accuracy of
the automatically generated ablation planning lines fulfilled clinical requirements. The mean error of 2.7 mm achieved implies a 29 % improvement compared to the state of the art algorithm. The qualitative evaluation showed full acceptance of the
automatically generated planning lines.
Another aspect investigated in this thesis is the optimization of individual fluoroscopic projection angles for X-ray guided cardiac procedures. We developed an
algorithm to estimate individual X-ray C-arm angulations based on pre-procedure planning information, taking individual patient anatomy into consideration. The C-arm angulations are optimized in respect to the orientation of the planning structure to minimize the foreshortening in the projection image. The mathematical framework can be applied for monoplane and biplane C-arm imaging systems.
Limitations of C-arm imaging systems in terms of feasible rotation angles are also
taken into account during optimization. The algorithm was evaluated on clinical
data for ipsilateral pulmonary vein isolation. Patient-specific C-arm angulations
were computed and compared against commonly used standard angulations in terms of foreshortening of planning structures in projection images. By applying individually optimized X-ray angulations, 28 % less foreshortening could be
achieved on average for biplane systems.
APA:
Koch, M. (2020). Artificial Intelligence based Methods for Atrial Fibrillation Ablation Procedures (Dissertation).
MLA:
Koch, Martin. Artificial Intelligence based Methods for Atrial Fibrillation Ablation Procedures. Dissertation, 2020.
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