A target-oriented and multi-patch based framework for image quality assessment on carotid artery MRI

Hongjian Jiang, Li Chen, Dongxiang Xu, Huilin Zhao, Hiroko Watase, Xihai Zhao, Chun Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Image quality assessment (IQA) of carotid vessel walls from magnetic resonance imaging (MRI) is critical to accurate diagnosis and prevention of stroke. However, most existing solutions for IQA are either manual or based only on holistic information. The low efficiency and accuracy of these methods hampers the transition of vessel wall imaging into clinical use. In this paper, we propose an IQA framework which assesses image quality using local features from multiple patches close to the target region in the image. Following criterion for target-oriented medical imaging quality assessment, we highlight the patch covering the artery detected by a neural network built on YOLOv2 and set the weights for other patches based on the human visual system both in training and testing. Finally, the image score is determined by a weighted average of patch scores. This method proved able to identify and quantify image quality using MRI datasets of different sequences with over 82% sensitivity and 90% specificity for four sequences (3D-MERGE, T1, T2, TOF) separately tasked with binary classification. Our proposed system shows the method's advantages on accuracy, efficiency, and adaptability in clinical use.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
ISBN (Electronic)9781510633933
Publication statusPublished - 2020
Externally publishedYes
EventMedical Imaging 2020: Image Processing - Houston, United States
Duration: 17-02-202020-02-2020

Publication series

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


ConferenceMedical Imaging 2020: Image Processing
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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