Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions.
Med Phys 2022;
49:4071-4081. [PMID:
35383946 DOI:
10.1002/mp.15654]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 03/23/2022] [Accepted: 03/27/2022] [Indexed: 11/09/2022] Open
Abstract
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.
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