Abstract #1381

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

Natalia Konovalova¹, Aniket Tolpadi^1,2, Felix Liu¹, Rupsa Bhattacharjee¹, Felix Gassert¹, Paula Giesler¹, Johanna Luitjens¹, Sharmila Majumdar¹, and Valentina Pedoia¹

¹Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Department of Bioengineering, University of California, Berkeley, Berkeley, CA, United States

Synopsis

Keywords: Joints, Machine Learning/Artificial Intelligence

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.

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