IEEE Trans Med Imaging. 2020 Nov;39(11):3691-3702. doi: 10.1109/TMI.2020.3002708. Epub 2020 Oct 28.
Fast and automated image quality assessment (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 self-training; 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) subject-wise 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 of modest size, exhibits great generalizability, and is capable of conducting rapid hierarchical IQA with near-perfect accuracy.
快速且自动化的弥散磁共振图像质量评估（IQA）对于及时决定是否重扫至关重要。然而，由于标注数据的数量有限，且标注标签并不总是正确的，因此学习此类模型具有挑战性。作为一种补救措施，我们将在本文中介绍一种基于分层非局部残差网络的儿科弥散磁共振图像自动图像质量评估（IQA）方法。我们的 IQA 分三个连续阶段进行，即 1）切片级 IQA，首先使用非局部残差网络对每个切片进行预训练，以给出初始质量评分（即通过/可疑/失败），然后通过迭代半监督学习和切片自我训练进行细化；2）体积级 IQA，该方法聚集来自一个体积的切片中提取的特征，并使用非局部网络通过迭代体积自我训练对每个体积的质量评分进行标注；3）主体级 IQA，将体积 IQA 结果进行集成，以确定与主体相关的整体图像质量。实验结果表明，我们的方法仅使用中等大小的样本进行训练，具有很好的泛化能力，能够以近乎完美的准确性进行快速分层 IQA。
