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

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 (LSTM) architecture and a content predictor using a Convolutional Neural Network (CNN) 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 3 new frames without affecting the image quality. The average required acquisition can drop by 30% per second for a 15 frame per second acquisition. The average Structural Similarity Index Measurement (SSIM) 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. This article is protected by copyright. All rights reserved.