ArticleMethodsX2026
ROI-SSIM guided framework for 3D knee MRI reconstruction under artifact degradation.
Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Magnetic resonance imaging (MRI) reconstruction is highly sensitive to undersampling and acquisition artifacts. Although deep learning-based approaches have significantly improved reconstruction quality, they are typically optimized using global fidelity metrics that may not fully reflect reconstruction quality within clinically important structures such as the anterior cruciate ligament (ACL), meniscus, and articular cartilage. This study proposes a region-aware MRI reconstruction framework based on a Vision Transformer autoencoder (ViT-AE) guided by an ROI Structural Similarity (ROI-SSIM) loss. The method leverages fastMRI+ bounding-box annotations and incorporates a hybrid artifact simulation framework spanning k-space and image-domain degradations. Experiments on the fastMRI knee single-coil validation set (PD contrast, R=4) show that the proposed ViT-AE achieves reconstruction fidelity competitive with established baselines (SSIM 0.823, Peak Signal-to-Noise Ratio (PSNR)
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