ArticleVeterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association2026
Application of Deep Learning-Based Reconstruction to Magnetic Resonance Imaging of Canine Stifle Joint in Healthy Beagles: Achieving Enhanced Image Quality With Reduced Slice Thickness.
Article in Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 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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Abstract
Stifle joint magnetic resonance imaging (MRI) is a valuable modality for diagnosing stifle joint disorders. In veterinary practice, reducing slice thickness is crucial for the detailed assessment of intra-articular structures. Recent advancements in deep learning-based reconstruction (DLR) have overcome the conventional trade-off between spatial resolution and noise, mitigating image quality degradation in thin-slice MRI. We hypothesized that DLR-applied stifle joint MRI could surpass conventional MRI in terms of diagnostic performance, especially with reduced slice thicknesses. This prospective, comparative pilot study compared conventional versus DLR-applied stifle joint MRI data of eight healthy beagle dogs by using sagittal T2-weighted fat saturation (T2W
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