Motion estimation in beating heart surgery

• Published in: IEEE transactions on biomedical engineering Vol. 52; no. 10; pp. 1729 - 1740
• Main Authors: Ortmaier, T., Groger, M., Boehm, D.H., Falk, V., Hirzinger, G.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-10-2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Beating heart surgery; Electrocardiography - methods; Heart; Heart - physiopathology; Heart surgery; Humans; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Minimally invasive surgery; Motion analysis; Motion compensation; Motion estimation; Motion measurement; Movement; Myocardial Contraction; Myocardium - pathology; Neural networks; robotic surgery; Robotics - methods; Robots; Robustness; Studies; Surgery, Computer-Assisted - methods; Surges; Tracking; virtual immobilization
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2005.855716

Minimally invasive beating-heart surgery offers substantial benefits for the patient, compared to conventional open surgery. Nevertheless, the motion of the heart poses increased requirements to the surgeon. To support the surgeon, algorithms for an advanced robotic surgery system are proposed, which offer motion compensation of the beating heart. This implies the measurement of heart motion, which can be achieved by tracking natural landmarks. In most cases, the investigated affine tracking scheme can be reduced to an efficient block matching algorithm allowing for realtime tracking of multiple landmarks. Fourier analysis of the motion parameters shows two dominant peaks, which correspond to the heart and respiration rates of the patient. The robustness in case of disturbance or occlusion can be improved by specially developed prediction schemes. Local prediction is well suited for the detection of single tracking outliers. A global prediction scheme takes several landmarks into account simultaneously and is able to bridge longer disturbances. As the heart motion is strongly correlated with the patient's electrocardiogram and respiration pressure signal, this information is included in a novel robust multisensor prediction scheme. Prediction results are compared to those of an artificial neural network and of a linear prediction approach, which shows the superior performance of the proposed algorithms.