Translator Disclaimer
10 March 2020 A target-oriented and multi-patch-based framework for image quality assessment on carotid artery MRI
Author Affiliations +
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.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hongjian Jiang, Li Chen, Dongxiang Xu, Huilin Zhao, Hiroko Watase, Xihai Zhao, and Chun Yuan "A target-oriented and multi-patch-based framework for image quality assessment on carotid artery MRI", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132A (10 March 2020);

Cited by 1 scholarly publication.
Image quality


Magnetic resonance imaging

Neural networks

Image processing

Data modeling

Image analysis

Back to Top