错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Pre-scan prediction of postoperative MRI reliability for the spinal canal and neural foramina using localizer images in patients with lumbar instrumentation

  • Seung Yun Lee,
  • Seungeun Lee,
  • Soyun Jeon,
  • So-Yeon Lee,
  • Hyemin Park,
  • Joon-Yong Jung

摘要

Purpose

To investigate whether factors from localizer images can predict the extent of metal artifacts on conventional turbo-spin echo T2-weighted images (TSE T2wI) in postoperative lumbar spine (L-spine) MRIs.

Methods

The study included 159 postoperative L-spine MRIs acquired from January 2019 to April 2022. The clinical data reviewed included types of interbody grafts, transpedicular screw diameters, and rod lengths. On localizer image, radiologists assessed the degree of artifact signals at the midline (sag-midline) and neural foramen (sag-foramen) levels on the sagittal plane. They also measured the distance between bilateral transpedicular screws (cor-distance) and the diameter of a unilateral screw-derived artifact (cor-diameter). On TSE T2wI, central canal and neural foramen visibility were rated as mild and severe. Two multivariable logistic regression models were developed to predict the visibility of the central canal and neural foramen based on clinical and localizer image factors, respectively.

Results

Longer rod level length (p < 0.001) and shorter cor-distance (R1, p = 0.001;R2, p = 0.008) were the strongest predictors of severe central canal artifact for both readers. Rod level length (R1, p = 0.001;R2, p < 0.001) was also a dominant predictor for neural foraminal artifact severity, with either cor-diameter (p = 0.01) or cor-distance (p = 0.005) additionally contributing depending on the reader. The area under the receiver operating characteristic curves of all models ranged between 0.772 and 0.857.

Conclusion

Localizer image-based factors combined with clinical implant characteristics can reliably predict metal artifact severity on postoperative TSE T2wI, supporting pre-scan triage and selective metal artifact reduction application by identifying patients at risk for unreliable postoperative MRI.