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

Azizmohammadi, Fariba, Navarro Castellanos, Iñaki, Miró, Joaquim, Segars, Paul, Samei, Ehsan et Duong, Luc. 2022. « Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions ». Medical Physics, vol. 49, nº 6. pp. 4071-4081.
Compte des citations dans Scopus : 1.

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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 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.

Item Type:	Peer reviewed article published in a journal
Professor:	Professor Duong, Luc
Affiliation:	Génie logiciel et des technologies de l'information
Date Deposited:	09 May 2022 15:46
Last Modified:	06 Apr 2023 04:00
URI:	https://espace2.etsmtl.ca/id/eprint/24337

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