An efficient cardiac mapping strategy for radiofrequency catheter ablation with active learning
File(s)
Accepted version
Published version
Author(s)
Type
Journal Article
Abstract
Objective
A major challenge in radiofrequency catheter ablation procedure
(RFCA) is the voltage and activation mapping of the endocardium, given a limited
mapping time. By learning from expert interventional electrophysiologists (operator),
while also making use of an active-learning framework, guidance on performing car-
diac voltage mapping can be provided to novice operators, or even directly to catheter
robots.
Methods
A Learning from Demonstration (LfD) framework, based upon previous car-
diac mapping procedures performed by an expert operator, in conjunction with Gaus-
sian process (GP) model-based active learning, was developed to efficiently perform
voltage mapping over right ventricles (RV). The GP model was used to output the
next best mapping point, while getting updated towards the underlying voltage data
pattern, as more mapping points are taken. A regularized particle filter was used to
keep track of the kernel hyperparameter used by GP. The travel cost of the catheter
tip was incorporated to produce time-efficient mapping sequences.
Results
The proposed strategy was validated on a simulated 2D grid mapping task,
with leave-one-out experiments on 25 retrospective datasets, in an RV phantom using
the Stereotaxis Niobe
R
©
remote magnetic navigation system, and on a tele-operated
catheter robot. In comparison to an existing geometry-based method, regression error
was reduced, and was minimized at a faster rate over retrospective procedure data.
Conclusion
A new method of catheter mapping guidance has been proposed based on
LfD and active learning. The proposed method provides real-time guidance for the
procedure, as well as a live evaluation of mapping sufficiency.
A major challenge in radiofrequency catheter ablation procedure
(RFCA) is the voltage and activation mapping of the endocardium, given a limited
mapping time. By learning from expert interventional electrophysiologists (operator),
while also making use of an active-learning framework, guidance on performing car-
diac voltage mapping can be provided to novice operators, or even directly to catheter
robots.
Methods
A Learning from Demonstration (LfD) framework, based upon previous car-
diac mapping procedures performed by an expert operator, in conjunction with Gaus-
sian process (GP) model-based active learning, was developed to efficiently perform
voltage mapping over right ventricles (RV). The GP model was used to output the
next best mapping point, while getting updated towards the underlying voltage data
pattern, as more mapping points are taken. A regularized particle filter was used to
keep track of the kernel hyperparameter used by GP. The travel cost of the catheter
tip was incorporated to produce time-efficient mapping sequences.
Results
The proposed strategy was validated on a simulated 2D grid mapping task,
with leave-one-out experiments on 25 retrospective datasets, in an RV phantom using
the Stereotaxis Niobe
R
©
remote magnetic navigation system, and on a tele-operated
catheter robot. In comparison to an existing geometry-based method, regression error
was reduced, and was minimized at a faster rate over retrospective procedure data.
Conclusion
A new method of catheter mapping guidance has been proposed based on
LfD and active learning. The proposed method provides real-time guidance for the
procedure, as well as a live evaluation of mapping sufficiency.
Date Issued
2017-05-05
Date Acceptance
2017-02-28
Citation
International Journal of Computer Assisted Radiology and Surgery, 2017, 12 (12), pp.199-1207
ISSN
1861-6410
Publisher
Springer Verlag (Germany)
Start Page
199
End Page
1207
Journal / Book Title
International Journal of Computer Assisted Radiology and Surgery
Volume
12
Issue
12
Copyright Statement
© The Author(s) 2017. This article is an open access publication
License URL
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Radiology, Nuclear Medicine & Medical Imaging
Surgery
Engineering
Radiofrequency catheter ablation
Cardiac mapping
Learning from demonstration
Active learning
Catheter robot guidance
1103 Clinical Sciences
Nuclear Medicine & Medical Imaging
Publication Status
Published