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Generation of fluoroscopy-alike radiographs as alternative datasets for deep learning in interventional radiology

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Abstract

In fluoroscopy-guided interventions (FGIs), obtaining large quantities of labelled data for deep learning (DL) can be difficult. Synthetic labelled data can serve as an alternative, generated via pseudo 2D projections of CT volumetric data. However, contrasted vessels have low visibility in simple 2D projections of contrasted CT data. To overcome this, we propose an alternative method to generate fluoroscopy-like radiographs from contrasted head CT Angiography (CTA) volumetric data. The technique involves segmentation of brain tissue, bone, and contrasted vessels from CTA volumetric data, followed by an algorithm to adjust HU values, and finally, a standard ray-based projection is applied to generate the 2D image. The resulting synthetic images were compared to clinical fluoroscopy images for perceptual similarity and subject contrast measurements. Good perceptual similarity was demonstrated on vessel-enhanced synthetic images as compared to the clinical fluoroscopic images. Statistical tests of equivalence show that enhanced synthetic and clinical images have statistically equivalent mean subject contrast within 25% bounds. Furthermore, validation experiments confirmed that the proposed method for generating synthetic images improved the performance of DL models in certain regression tasks, such as localizing anatomical landmarks in clinical fluoroscopy images. Through enhanced pseudo 2D projection of CTA volume data, synthetic images with similar features to real clinical fluoroscopic images can be generated. The use of synthetic images as an alternative source for DL datasets represents a potential solution to the application of DL in FGIs procedures.

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Funding

This study is supported by the Fundamental Research Grant Scheme (FRGS) (FRGS/1/2020/SKK0/UM/02/30) under the Ministry of Higher Education Malaysia.

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Authors and Affiliations

  1. Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Wilbur K. S. Fum, Mohammad Nazri Md Shah, Raja Rizal Azman Raja Aman, Khairul Azmi Abd Kadir & Li Kuo Tan

  2. Division of Radiological Sciences, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore

    Wilbur K. S. Fum

  3. Department of Vascular and Interventional Radiology, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore

    David Wei Wen & Sum Leong

Authors
  1. Wilbur K. S. Fum
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  2. Mohammad Nazri Md Shah
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Contributions

WKSF: Conceptualization, Methodology, Software, Investigation, Data Curation, Formal analysis, Writing- Original draft preparation, Writing- Reviewing and Editing, Visualization, MNMS: Resources, Validation, RRARA: Resources, KAAK: Resources, DWW: Validation, SL: Validation, LKT: Software, Validation, Formal analysis, Resources, Investigation, Writing- Reviewing and Editing, Supervision, Funding acquisition.

Corresponding author

Correspondence to Li Kuo Tan.

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Competing interest

The authors have no relevant conflicts of interest to disclose.

Ethical approval

This study involving retrospective patient clinical imaging data, was reviewed, and approved by the ethic committee of the University Malaya Medical Centre. Ethical approval for this study was obtained from the medical research ethics committee of University of Malaya Medical Centre (MREC ID No.: 202115–9665). All experiments were carried out in accordance with the approved guidelines. Written informed consent was waived because this was a retrospective study of preexisting imaging data and was de-identified.

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Fum, W.K.S., Md Shah, M.N., Raja Aman, R.R.A. et al. Generation of fluoroscopy-alike radiographs as alternative datasets for deep learning in interventional radiology. Phys Eng Sci Med 46, 1535–1552 (2023). https://doi.org/10.1007/s13246-023-01317-5

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