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

Objective.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.Approach.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. 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.Main results.Using this method we achieved less than 1 mm prediction error for complex cardio-respiratory motion prediction. The following mean prediction error values were recorded over all the simulated sequences: 0.39 mm (for both motions), 0.33 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.Significance.This study paves the road for a patient-specific cardio-respiratory motion prediction model, which might improve navigation guidance during cardiac interventions.

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. METHODS: Frame predictions are based on a model-free motion estimation approach using a long short-term memory architecture and a content predictor using a convolutional neural network structure. The presented model thus estimates contrast-enhanced vascular structures such as the coronary arteries and their motion in X-ray sequences in an end-to-end system. This work was validated with 56 simulated and 52 patients' X-ray angiography sequences. RESULTS: Using the predicted images can reduce the number of pulses by up to three new frames without affecting the image quality. The average required acquisition can drop by 30% per second for a 15 fps acquisition. The average structural similarity index measurement was 97% for the simulated dataset and 82% for the patients' dataset. CONCLUSIONS: Frame prediction using a learning-based method is promising for minimizing radiation dose exposure. The required pulse rate is reduced while preserving the frame rate and the image quality. With proper integration in X-ray angiography systems, this method can pave the way for improved dose management.

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. The new displacement parameters from a sequence of transformation matrices are predicted using an LSTM model. LSTM is a particular form of Recurrent Neural Network (RNN) architecture suitable for learning sequential data and predicting time series. The method was developed and validated by simulated data using a realistic cardiorespiratory motion simulator (XCAT). The results show that this method converges quickly and can predict the complex motion in the angiography sequences with irregularities. The mean values of prediction error over all the patients are approximately 0.29 mm (2 pixels) difference for the combination of both motions, 0.51 mm (3.5 pixels) difference for cardiac motion and 0.44 mm (3 pixels) difference for respiratory motion.