Zhu Dan, Zhang Zhengjia, Zou Yixuan, Zhang Guozhi, Cheng Xiaofei, Wan Daqian, Ai Songtao
Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
United Imaging Healthcare, Shanghai, China.
Quant Imaging Med Surg. 2024 Sep 1;14(9):6843-6855. doi: 10.21037/qims-23-1645. Epub 2024 Apr 10.
Low-dose following up computed tomography (CT) of percutaneous vertebroplasty (PVP) that involves the use of bone cement usually suffers from lightweight metal artifacts, where conventional techniques for CT metal artifact reduction are often not sufficiently effective. This study aimed to validate an artificial intelligence (AI)-based metal artifact correction (MAC) algorithm for use in low-dose following up CT for PVP.
In experimental validation, an ovine vertebra phantom was designed to simulate the clinical scenario of PVP. With routine-dose images acquired prior to the cement introduction as the reference, low-dose CT scans were taken on the cemented phantom and processed with conventional MAC and AI-MAC. The resulting image quality was compared in CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), followed by a quantitative evaluation of the artifact correction accuracy based on adaptive segmentation of the paraspinal muscle. In clinical validation, ten cases of low-dose following up CT after PVP were enrolled to test the performance of diagnosing sarcopenia with measured CT attenuation per cemented vertebral segment, via receiver operating characteristic (ROC) analysis.
With respect to the reference image, no significant difference was found for AI-MAC in CT attenuation, image noise, SNRs, and CNR (all P>0.05). The paraspinal muscle segmented on the AI-MAC image was 18.6% and 8.3% more complete to uncorrected and MAC images. Higher area under the curve (AUC) of the ROC analysis was found for AI-MAC (AUC =0.92) compared to the uncorrected (AUC =0.61) and MAC images (AUC =0.70).
In low-dose following up CT for PVP, the AI-MAC has been fully validated for its superior ability compared to conventional MAC in suppressing artifacts and may be a reliable alternative for diagnosing sarcopenia.
经皮椎体成形术（PVP）后进行低剂量计算机断层扫描（CT）随访时，由于使用了骨水泥，通常会出现轻度金属伪影，而传统的CT金属伪影减少技术往往效果不佳。本研究旨在验证一种基于人工智能（AI）的金属伪影校正（MAC）算法在PVP低剂量随访CT中的应用。
在实验验证中，设计了一个羊椎体模型来模拟PVP的临床情况。以注入骨水泥前获取的常规剂量图像作为参考，对注入骨水泥的模型进行低剂量CT扫描，并使用传统MAC和AI-MAC进行处理。比较所得图像在CT衰减、图像噪声、信噪比（SNR）和对比噪声比（CNR）方面的图像质量，然后基于椎旁肌的自适应分割对伪影校正精度进行定量评估。在临床验证中，纳入10例PVP后进行低剂量随访CT的病例，通过受试者操作特征（ROC）分析，以每个注入骨水泥的椎体节段测量的CT衰减来测试诊断肌肉减少症的性能。
与参考图像相比，AI-MAC在CT衰减、图像噪声、SNR和CNR方面均无显著差异（所有P>0.05）。在AI-MAC图像上分割的椎旁肌比未校正图像和MAC图像分别完整18.6%和8.3%。与未校正图像（AUC =0.61）和MAC图像（AUC =0.70）相比，AI-MAC的ROC分析曲线下面积（AUC）更高（AUC =0.92）。
在PVP低剂量随访CT中，与传统MAC相比，AI-MAC在抑制伪影方面具有卓越能力，已得到充分验证，可能是诊断肌肉减少症的可靠替代方法。
