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Deep learning techniques have emerged as a promising approach to highlyaccelerated MRI. However, recent reconstruction challenges have shown severaldrawbacks in current deep learning approaches, including the loss of fine imagedetails 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 imagereconstruction and pathology detection, which enables a clinically awareevaluation of deep learning reconstruction quality. The solution isdemonstrated for a use case in detecting meniscal tears on knee MRI studies,ultimately finding a loss of fine image details with common reconstructionmethods expressed as a reduced ability to detect important pathology likemeniscal tears. Despite the common practice of quantitative reconstructionmethodology evaluation with metrics such as SSIM, impaired pathology detectionas an automated pathology-based reconstruction evaluation approach suggestsexisting quantitative methods do not capture clinically importantreconstruction outcomes.

End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction