  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.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing;Medical Physics