Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. If the motion artefacts alter a correct delineation of structure and substructures of the brain, lesions, tumours and so on, the patients need to be re-scanned. Otherwise, neuro-radiologists could report an inaccurate or incorrect diagnosis. The first step right after scanning a patient is the "\textit{image quality assessment}" in order to decide if the acquired images are diagnostically acceptable. An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network has been proposed here, with the possibility to perform also the classification in different groups - by subdividing with SSIM ranges. This method predicts SSIM values of an input image in the absence of a reference ground truth image. The networks were able to detect motion artefacts, and the best performance for the regression and classification task has always been achieved with ResNet-18 with contrast augmentation. Mean and standard deviation of residuals' distribution were $\mu=-0.0009$ and $\sigma=0.0139$, respectively. Whilst for the classification task in 3, 5 and 10 classes, the best accuracies were 97, 95 and 89\%, respectively. The obtained results show that the proposed method could be a tool in supporting neuro-radiologists and radiographers in evaluating the image quality before the diagnosis.
翻译:磁共振大脑图像中的移动手工艺品是一个关键问题。 在进行临床诊断之前, MR 图像质量评估至关重要。 如果运动手工艺品改变了大脑、腐蚀、肿瘤等结构及子结构的正确划分, 病人需要重新扫描。 否则, 神经放射学家可以报告不准确或不正确的诊断。 扫描病人后的第一个步骤是“ textit{image image legal assessment} ”, 以便确定获得的图像是否在诊断上可以接受。 根据结构相似指数(SSIM)通过残余神经网络回归的自动图像质量评估已经在这里提出, 并有可能在不同组别中进行分类 — 与 SSIM 范围相接轨 。 这种方法预测了输入图像的 SSSIM 值, 没有参考地面真实图像。 网络能够检测运动工艺品, 回归和分类工作的最佳表现总是通过ResNet-18 和对比放大。 剩余分布的平均值和标准偏差评估是 $\=0.009, 和 $\ sigma= 139, 分别展示了 95 和 格式 3 最佳分析结果 。