Abstract #0048

SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation

Arjun D Desai^1,2, Andrew M Schmidt², Elka B Rubin², Christopher M Sandino¹, Marianne S Black², Valentina Mazzoli², Kathryn J Stevens², Robert Boutin², Christopher Ré³, Garry E Gold^2,4, Brian A Hargreaves^1,2,4, and Akshay S Chaudhari^2,5

¹Electrical Engineering, Stanford University, Stanford, CA, United States, ²Radiology, Stanford University, Stanford, CA, United States, ³Computer Science, Stanford University, Stanford, CA, United States, ⁴Bioengineering, Stanford University, Stanford, CA, United States, ⁵Biomedical Data Science, Stanford University, Stanford, CA, United States

Synopsis

While deep-learning-based MRI reconstruction and image analysis methods have shown promise, few have been translated to clinical practice. This may be a result of (1) paucity of end-to-end datasets that enable comprehensive evaluation from reconstruction to analysis and (2) discordance between conventional validation metrics and clinically useful endpoints. Here, we present the Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA), a dataset of 155 clinical quantitative 3D knee MRI scans with k-space data, DICOM images, and dense tissue segmentation and pathology annotations to facilitate clinically relevant, comprehensive benchmarking of the MRI workflow. Dataset, code, and trained baselines are available at https://github.com/StanfordMIMI/skm-tea.

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