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Real-time respiratory signal extraction from X-ray sequences using incremental manifold learning

In X-ray fluoroscopy-guided minimally invasive interventions, overlays of pre-procedurally acquired image data can be used to visualize soft-tissue. In the thoracic and abdominal regions, static overlays are inconsistent to the live X-ray images due to respiratory motion of the patient. This error can be reduced by dynamically adapting the overlay to the respiration. A first step in this direction is the real-time extraction of the respiratory state from the live X-ray images. The respiratory state can drive a motion model to compensate the breathing motion. We present a method to extract respiratory signals from X-ray sequences in real-time. Respiratory signal extraction is viewed as a dimensionality reduction problem, which is performed for each X-ray image using incremental Isomap. The method has a correlation of 0.97 ± 0.02 with internal breathing motion and an average runtime of 42 ms per image. The method is accurate, robust, and can be used in a wide range of clinical applications and fields of view.