A motion-level-aware denoising framework for x-ray fluoroscopic images

Geonhui Jo, Minhee Jang, Sun Young Jeon, Wonjin Kim, Jang Hwan Choi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering the radiation dose inevitably increases random noise in x-ray images, resulting in poor diagnostic image quality, which requires noise reduction for accurate diagnosis. Also, in the case of non-static objects, the image is blurred due to motion. The most-used denoiser with a recursive filter (RF) preserves details well when applied to temporal data, but it is vulnerable to motion blur. Existing convolutional neural network (CNN)-based algorithms with single-frame input cannot use the temporary context, and others with multi-frame input are good for motion detection but poor for detail preservation. Therefore, we propose a motion-level-aware denoising framework to combine the results of RF- and CNN-based algorithms depending on the pixel-wise magnitude of motion to complement each other. The data we use are fluoroscopy images taken in continuous time, and we aim at many-to-one so that one frame is denoised by considering sequential frames. Also, since both RF- and CNN-based algorithms used in our architecture are many-to-one methods, they can consider spatiotemporal information. In the multi-frame input, the difference in intensity of each pixel between frames is calculated to obtain a moving map. Depending on the factor value from the moving map, the final image is obtained by reflecting the outputs of the RF- and CNN-based algorithms. If the factor value is high, the pixel intensity of the final image is like the CNN-based output, which is good for motion detection, and vice versa, it more reflects the intensity of RF output, which is excellent in perceptual quality. Therefore, it prevents motion blur and does not over-smooth microdetails, such as bones and muscles. The results show that combining the two outputs together records higher peak signal-to-noise ratio (PSNR) and has better perceptual quality for diagnosis than using only one method. Furthermore, our combining method can output x-ray images of higher quality by using more advanced networks in future fluoroscopy denoising studies, since the proposed denoising framework is not only applicable to specific architectures used in this study but can also be broadly applied to other alternative networks.

Original languageEnglish
Title of host publicationMedical Imaging 2023
Subtitle of host publicationPhysics of Medical Imaging
EditorsLifeng Yu, Rebecca Fahrig, John M. Sabol
ISBN (Electronic)9781510660311
StatePublished - 2023
EventMedical Imaging 2023: Physics of Medical Imaging - San Diego, United States
Duration: 19 Feb 202323 Feb 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2023: Physics of Medical Imaging
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

Funding Information:
This work was partly supported by the Technology development Program of MSS [S3146559], the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C1092072), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174276, RS-2020-KD000016).

Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.


  • deep-learning
  • fluoroscopy denoising
  • object motion
  • recursive filter


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