Deep learning-based reconstruction can improve canine thoracolumbar magnetic resonance image quality and reduce slice thickness

In veterinary practice, thin-sliced thoracolumbar MRI is useful in detecting small lesions, especially in small-breed dogs. However, it is challenging due to the partial volume averaging effect and increase in scan time. Currently, deep learning-based reconstruction (DLR), a part of artificial intelligence, has been applied in diagnostic imaging. We hypothesized that the diagnostic performance of thin-slice thoracolumbar MRI with DLR would be superior to conventional MRI. This prospective, method comparison study aimed to determine the adequate slice thickness of a deep learning model for thin-slice thoracolumbar MRI. Sagittal and transverse T2-weighted MRI at the thoracolumbar region were performed on 12 clinically healthy beagle dogs; the images obtained were categorized into five groups according to slice thickness: conventional thickness of 3 mm (₃CON) and thicknesses of 3, 2, 1.5, and 1 mm with DLR (₃DLR, ₂DLR, _1.5DLR, and ₁DLR, respectively). Quantitative analysis was performed using signal-to-noise ratio (SNR) and contrast-to-noise ratio. Qualitative analysis involved the evaluation of perceived SNR, structural visibility, and overall image quality using a four-point scale. Moreover, nerve root visibility was evaluated using transverse images. Quantitative and qualitative values were compared among the five groups. Compared with the ₃CON group, the ₃DLR, ₂DLR, and _1.5DLR groups exhibited significantly higher quantitative and qualitative values. Nerve root visibility was significantly higher in ₂DLR, _1.5DLR, and ₁DLR images than in ₃DLR and ₃CON images. Compared with conventional MRI, DLR reduced the slice thickness by up to one-half and improved image quality in this sample of clinically healthy beagles.