Towards Integrating DL Reconstruction and Diagnosis: Meniscal Anomaly Detection Shows Similar Performance on Reconstructed and Baseline MRI

Meniscal lesions are a common knee pathology, but pathology detection from MRI is usually evaluated on full-length acquisitions. We trained UNet and KIKI I-Net reconstruction algorithms with several loss function configurations, showing k-space losses are not required to obtain robust reconstructions. We trained and evaluated Faster R-CNN to detect meniscal anomalies, showing similar performance on R=8 reconstructions and fully-sampled images, demonstrating its utility as an assessment tool for reconstruction performance and indicating reconstructed images are viable for downstream clinical postprocessing tasks.