Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks.

To develop 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 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. The displacement information includes translation, rotation, shearing, and scaling in 2D.

Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions.

Background: Navigation guidance in cardiac interventions is provided by X-ray angiography. Cumulative radiation exposure is a serious concern for pediatric cardiac interventions.

Purpose: A generative learning-based approach is proposed to predict X-ray angiography frames to reduce the radiation exposure for pediatric cardiac interventions while preserving the image quality.

Model-free Cardiorespiratory Motion Prediction from X-ray Angiography Sequence with LSTM Network.

We present a novel model-free approach for cardiorespiratory motion prediction from X-ray angiography time series based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN). Cardiorespiratory motion prediction is defined as a problem of estimating the future displacement of the coronary vessels in the next image frame in an X-ray angiography sequence. The displacement of the vessels is represented as a sequence of 2D affine transformation matrices allowing 2D X-ray registrations in a sequence.