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 (T2WFS) and proton density-weighted fat saturation (PDWFS) sequences. The following groups were formed on the basis of slice thickness: conventional 2 mm group (2CON) and DLR-applied 2, 1.5, and 1 mm groups (2DLR, 1.5DLR, and 1DLR, respectively). Quantitative analysis assessed the signal-to-noise ratio (SNR) and contrast-to-noise ratio, whereas qualitative analysis evaluated the structural visibility of the cranial cruciate ligament (CCL), meniscus, and bone; perceived SNR; and overall image quality using a four-point Likert scale.
In both T2WFS and PDWFS sequences, 2DLR, 1.5DLR, and 1DLR exhibited a significantly higher SNR than 2CON. Moreover, in both T2WFS and PDWFS sequences, the DLR groups scored significantly higher than 2CON for all qualitative indices. Notably, 1DLR exhibited the highest CCL visibility, followed by 1.5DLR and 2DLR.
In conclusion, DLR-applied stifle joint MRI can reduce slice thickness and enhance image quality and anatomical delineation, potentially increasing the diagnostic accuracy of stifle joint disorders.
