Deep learning techniques have emerged as a promising approach to highly accelerated MRI. However, recent reconstruction challenges have shown several drawbacks in current deep learning approaches, including the loss of fine image details even using models that perform well in terms of global quality metrics. In this study, we propose an end-to-end deep learning framework for image reconstruction and pathology detection, which enables a clinically aware evaluation of deep learning reconstruction quality. The solution is demonstrated for a use case in detecting meniscal tears on knee MRI studies, ultimately finding a loss of fine image details with common reconstruction methods expressed as a reduced ability to detect important pathology like meniscal tears. Despite the common practice of quantitative reconstruction methodology evaluation with metrics such as SSIM, impaired pathology detection as an automated pathology-based reconstruction evaluation approach suggests existing quantitative methods do not capture clinically important reconstruction outcomes.

翻译：然而,最近的重建挑战在目前的深层学习方法中显示出若干缺点,包括即使使用在全球质量衡量标准方面表现良好的模型,也丧失了精细的图像细节;在这项研究中,我们提议为图像重建和病理学检测建立一个端到端深的学习框架,以便能够对深层学习重建质量进行临床认知评估;为在膝盖MRI研究中发现红眼眼泪,最终发现丢失了美貌细节,以常见的重建方法表示,发现重要病理学(如红眼眼眼)的能力降低。 尽管以SSIM等衡量标准进行定量重建方法评估的常见做法是,但受损病理学检测作为一种基于自动病理学的重建评估方法,显示现有定量方法并不反映具有临床重要性的重建结果。