Fastand automated image qualityassessment (IQA) of diffusion MR images is crucial for making timely decisions for rescans. However, learning a model for this task is challenging as the number of annotated data is limited and the annotation labels might not always be correct. As a remedy, we will introduce in this paper an automatic image quality assessment (IQA)method based on hierarchical non-local residual networks for pediatric diffusion MR images. Our IQA is performed in three sequential stages, i.e., 1) slice-wise IQA, where a nonlocal residual network is first pre-trained to annotate each slice with an initial quality rating (i.e., pass/questionable/fail), which is subsequently refined via iterative semi-supervised learning and slice selftraining; 2) volume-wise IQA, which agglomerates the features extracted from the slices of a volume, and uses a nonlocal network to annotate the quality rating for each volume via iterative volume self-training; and 3) subjectwise IQA, which ensembles the volumetric IQA results to determine the overall image quality pertaining to a subject. Experimental results demonstrate that our method, trained using only samples ofmodest size, exhibits great generalizability, and is capable of conducting rapid hierarchical IQA with near-perfect accuracy.
Bibliographical notePublisher Copyright:
© 2020 IEEE.
- Image quality assessment
- hierarchical nonlocal residual networks
- semi-supervised learning
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Electrical and Electronic Engineering
- Computer Science Applications