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

• DOI: 10.48550/arxiv.2203.06823
• Publication Date: 2023-08-03
• Authors: Desai, Arjun D; Schmidt, Andrew M; Rubin, Elka B; Sandino, Christop...; Black, Marianne S; Mazzoli, Valentina; Stevens, Kathryn J; Boutin, Robert; Ré, Christopher; Gold, Garry E; Hargreaves, Brian A; Chaudhari, Akshay S
• Keywords: FOS: Computer and information sciences; 03 medical and health sciences; 0302 clinical medicine; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; FOS: Electrical engineering, electronic engineering, information engineering; Electrical Engineering and Systems Science - Image and Video Processing; 3. Good health

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.