Objective To aim of this study was to compare the performance of different iterative metal artifact reduction (iMAR) algorithms at reducing metal artifacts after lumbar spine internal fixation surgery.

Methods Patients who underwent internal lumbar spine fixation were prospectively enrolled. Image reconstruction was performed without the iMAR algorithm and with eight recommended iMAR algorithms tailored for different clinical scenarios (neurocoils, dental fillings, spinal implants, shoulder implants, pacemakers, thoracic coils, hip implants, and extremity implants). The regions of interest, namely, the vertebral body, spinal canal, erector spinae, and subcutaneous fat, were delineated on optimal cross-sectional images along the long axis of the screws, as well as on cross-sectional images without obvious artifacts. The computed tomography (CT) values and their standard deviation (SD) were recorded, and the CT value difference (ΔCT) of each region, average background SD, average artifact SD, and artifact index (AI) value of each region were calculated. Tanking images without the iMAR algorithm were used as the control group, and those reconstructed with the iMAR algorithm were used as the experimental group. The effectiveness of metal artifact removal and its impact on diagnosis were assessed subjectively and objectively. Subjective and objective evaluation results were compared. Results: Thirty-six patients, including 22 males (61.11%), were included in the study, with a mean age ± SD of 62.75 ± 12.23 years. The median (interquartile range) of the volume CT dose index value was 16.25 (14.55, 19.62) mGy, and the median (interquartile range) of the dose-length product was 448.45 (410.52, 566.88) mGy·cm. The ΔCT values of each region, average artifact SD value, and AI values of each region were significantly lower in images reconstructed with the spinal iMAR algorithm than those reconstructed without the iMAR algorithm. The muscle ΔCT value, fat ΔCT value, average artifact SD value, and AI values of each region were significantly lower in images reconstructed with the spinal iMAR algorithm than in those reconstructed using other iMAR algorithms. The subjective scores were significantly higher for images reconstructed with the spinal iMAR algorithm than for those reconstructed without the iMAR algorithm. Conclusion: Compared with the iMAR algorithms used for other clinical scenarios, the iMAR algorithm for spinal implants was more effective at removing metal artifacts in patients after lumbar spine internal fixation surgery and is recommended for clinical diagnosis.