MRA-free intracranial vessel localization on MR vessel wall images View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-04-14

AUTHORS

Weijia Fan, Yudi Sang, Hanyue Zhou, Jiayu Xiao, Zhaoyang Fan, Dan Ruan

ABSTRACT

Analysis of vessel morphology is important in assessing intracranial atherosclerosis disease (ICAD). Recently, magnetic resonance (MR) vessel wall imaging (VWI) has been introduced to image ICAD and characterize morphology for atherosclerotic lesions. In order to automatically perform quantitative analysis on VWI data, MR angiography (MRA) acquired in the same imaging session is typically used to localize the vessel segments of interest. However, MRA may be unavailable caused by the lack or failure of the sequence in a VWI protocol. This study aims to investigate the feasibility to infer the vessel location directly from VWI. We propose to synergize an atlas-based method to preserve general vessel structure topology with a deep learning network in the motion field domain to correct the residual geometric error. Performance is quantified by examining the agreement between the extracted vessel structures from the pair-acquired and alignment-corrected angiogram, and the estimated output using a cross-validation scheme. Our proposed pipeline yields clinically feasible performance in localizing intracranial vessels, demonstrating the promise of performing vessel morphology analysis using VWI alone. More... »

PAGES

6240

References to SciGraph publications

  • 2018-09-12. Unsupervised Learning for Cross-Domain Medical Image Synthesis Using Deformation Invariant Cycle Consistency Networks in SIMULATION AND SYNTHESIS IN MEDICAL IMAGING
  • 2016-10-02. A Deep Metric for Multimodal Registration in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2016
  • 2016-10-02. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 2017-11-27. SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research in JOURNAL OF DIGITAL IMAGING
  • 2010-01-22. Review of brain MRI image segmentation methods in ARTIFICIAL INTELLIGENCE REVIEW
  • 2020-02-07. Medical Image Synthesis via Deep Learning in DEEP LEARNING IN MEDICAL IMAGE ANALYSIS
  • 2015-11-18. U-Net: Convolutional Networks for Biomedical Image Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2015
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-022-10256-2

    DOI

    http://dx.doi.org/10.1038/s41598-022-10256-2

    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35422490


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