Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects

Wonjin Kim, Wonkyeong Lee, Sun Young Jeon, Nayeon Kang, Geonhui Jo, Jang Hwan Choi

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

Abstract

Prolonged fluoroscopy procedures may involve high patient radiation doses, and a low-dose fluoroscopy protocol has been proven to be effective in reducing doses in an interventional suite. However, the low-dose protocol-caused noise degrades fluoroscopic image quality and then impacts clinical diagnosis accuracy. Here, we propose a novel deep denoising network for low-dose fluoroscopic image sequences of moving objects. The existing deep learning-based denoising approaches showed promising performance in denoising static fluoroscopic images, but their dynamic image denoising performance is relatively poor because they are not able to accurately track moving objects, losing detailed textures of the dynamic objects. To overcome the limitations of current methods, we introduce a self-attention-based network with the incorporation of flow-guided feature parallel warping. Parallel warping is able to jointly extract, align, and propagate features of dynamic objects in adjacent fluoroscopic frames, and self-attention effectively learns long-range spatiotemporal features between the adjacent frames. Our extensive experiments on real datasets of clinically relevant dynamic phantoms reveals that the performance of the proposed method achieves superior performance, both quantitatively and qualitatively, over state-of-the-art methods on a denoising task.

Original languageEnglish
Title of host publicationMachine Learning for Medical Image Reconstruction - 5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsNandinee Haq, Patricia Johnson, Andreas Maier, Chen Qin, Tobias Würfl, Jaejun Yoo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages95-104
Number of pages10
ISBN (Print)9783031172465
DOIs
StatePublished - 2022
Event5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 22 Sep 202222 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13587 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period22/09/2222/09/22

Keywords

  • Deep neural network
  • Image denoising
  • Moving objects
  • Multi-frame images
  • X-ray fluoroscopy

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