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

Publication ,  Journal Article
Azizmohammadi, F; Navarro Castellanos, I; Miró, J; Segars, P; Samei, E; Duong, L
Published in: Med Phys
June 2022

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.

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Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

June 2022

Volume

49

Issue

6

Start / End Page

4071 / 4081

Location

United States

Related Subject Headings

  • X-Rays
  • Radiography
  • Radiation Dosage
  • Nuclear Medicine & Medical Imaging
  • Humans
  • Fluoroscopy
  • Drug Tapering
  • Child
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
 

Citation

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Azizmohammadi, F., Navarro Castellanos, I., Miró, J., Segars, P., Samei, E., & Duong, L. (2022). Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions. Med Phys, 49(6), 4071–4081. https://doi.org/10.1002/mp.15654
Azizmohammadi, Fariba, Iñaki Navarro Castellanos, Joaquim Miró, Paul Segars, Ehsan Samei, and Luc Duong. “Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions.Med Phys 49, no. 6 (June 2022): 4071–81. https://doi.org/10.1002/mp.15654.
Azizmohammadi F, Navarro Castellanos I, Miró J, Segars P, Samei E, Duong L. Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions. Med Phys. 2022 Jun;49(6):4071–81.
Azizmohammadi, Fariba, et al. “Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions.Med Phys, vol. 49, no. 6, June 2022, pp. 4071–81. Pubmed, doi:10.1002/mp.15654.
Azizmohammadi F, Navarro Castellanos I, Miró J, Segars P, Samei E, Duong L. Generative learning approach for radiation dose reduction in X-ray guided cardiac interventions. Med Phys. 2022 Jun;49(6):4071–4081.

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

June 2022

Volume

49

Issue

6

Start / End Page

4071 / 4081

Location

United States

Related Subject Headings

  • X-Rays
  • Radiography
  • Radiation Dosage
  • Nuclear Medicine & Medical Imaging
  • Humans
  • Fluoroscopy
  • Drug Tapering
  • Child
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering