Patient-specific cardio-respiratory motion prediction in x-ray angiography using LSTM networks


 Image-guided cardiac interventions are highly affected by cardio-respiratory motion. This study presents a novel patient-specific cardio-respiratory motion prediction approach for X-ray angiography time series based on a simple Long Short-Term Memory (LSTM) model. The model can facilitate the navigation process of the cardiac intervention. The cardio-respiratory motion behavior in an X-ray image sequence was represented as a sequence of 2D affine transformation matrices, which provide the displacement information of contrasted moving objects (arteries and medical devices) in a sequence that included translation, rotation, shearing, and scaling in 2D. A many- to-many LSTM model was developed to predict 2D transformation parameters in matrix form for future frames based on previously generated images. The method was developed with 64 simulated phantom datasets (pediatric and adult patients) using a realistic cardio-respiratory motion simulator (XCAT) and was validated using 10 different patient X-ray angiography sequences. Based on our results, this method is robust enough to predict complex motion with irregularities in simulated and patient angiography sequences. The following mean prediction error values were recorded over all the simulated sequences: 0.33 mm (for both motions), 0.39 mm (for only cardiac motion), and 0.47 mm (for only respiratory motion). The mean prediction error for the patient dataset was 0.58 mm.