Kang Hyesun, Noh Daji, Lee Sang-Kwon, Choi Sooyoung, Lee Kija
College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea.
College of Veterinary Medicine, Kangwon National University, Chuncheon, Republic of Korea.
Vet Radiol Ultrasound. 2023 Nov;64(6):1063-1070. doi: 10.1111/vru.13298. Epub 2023 Sep 5.
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 ( CON) and thicknesses of 3, 2, 1.5, and 1 mm with DLR ( DLR, DLR, DLR, and DLR, 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 CON group, the DLR, DLR, and DLR groups exhibited significantly higher quantitative and qualitative values. Nerve root visibility was significantly higher in DLR, DLR, and DLR images than in DLR and CON 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.
在兽医临床实践中，胸腰椎薄层MRI对于检测小病变很有用，尤其是在小型犬中。然而，由于部分容积平均效应和扫描时间增加，这具有挑战性。目前，作为人工智能一部分的基于深度学习的重建（DLR）已应用于诊断成像。我们假设使用DLR的胸腰椎薄层MRI的诊断性能将优于传统MRI。这项前瞻性方法比较研究旨在确定胸腰椎薄层MRI深度学习模型的合适层厚。对12只临床健康的比格犬进行胸腰椎区域的矢状面和横断面T2加权MRI检查；根据层厚将获得的图像分为五组：传统的3mm层厚（CON）以及使用DLR的3mm、2mm、1.5mm和1mm层厚（分别为DLR、DLR、DLR和DLR）。使用信噪比（SNR）和对比噪声比进行定量分析。定性分析包括使用四点量表评估感知的SNR、结构可见性和整体图像质量。此外，使用横断面图像评估神经根可见性。比较五组之间的定量和定性值。与CON组相比，DLR、DLR和DLR组表现出显著更高的定量和定性值。DLR、DLR和DLR图像中的神经根可见性显著高于DLR和CON图像。与传统MRI相比，在这个临床健康比格犬样本中，DLR将层厚减少了多达一半，并提高了图像质量。
