Iterative metal artefact reduction in CT: can dedicated algorithms improve image quality after spinal instrumentation?. Issue 5 (May 2017)

• Record Type: Journal Article
• Authors: Aissa, J.; Thomas, C.; Sawicki, L.M.; Caspers, J.; Kröpil, P.; Antoch, G.; Boos, J.
• Is Part Of: Clinical radiology. Volume 72:Issue 5(2017)
• Page Start: 428.e7
• Page End: 428.e12
• Publication Date: 2017-05
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2016.12.006
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : Aim: To investigate the value of dedicated computed tomography (CT) iterative metal artefact reduction (iMAR) algorithms in patients after spinal instrumentation. Materials and methods: Post-surgical spinal CT images of 24 patients performed between March 2015 and July 2016 were retrospectively included. Images were reconstructed with standard weighted filtered back projection (WFBP) and with two dedicated iMAR algorithms (iMAR-Algo1, adjusted to spinal instrumentations and iMAR-Algo2, adjusted to large metallic hip implants) using a medium smooth kernel (B30f) and a sharp kernel (B70f). Frequencies of density changes were quantified to assess objective image quality. Image quality was rated subjectively by evaluating the visibility of critical anatomical structures including the central canal, the spinal cord, neural foramina, and vertebral bone. Results: Both iMAR algorithms significantly reduced artefacts from metal compared with WFBP ( p <0.0001). Results of subjective image analysis showed that both iMAR algorithms led to an improvement in visualisation of soft-tissue structures (median iMAR-Algo1=3; interquartile range [IQR]:1.5–3; iMAR-Algo2=4; IQR: 3.5–4) and bone structures (iMAR-Algo1=3; IQR:3-4; iMAR-Algo2=4; IQR:4-5) compared to WFBP (soft tissue: median 2; IQR: 0.5–2 and bone structures: median 2; IQR: 1–3; p <0.0001). Compared with iMAR-Algo1, objective artefact reduction and subjective visualisation of soft-tissue and bone structures were improved with iMAR-Algo2 ( p <0.0001). Conclusion: Both iMAR algorithms reduced artefacts compared with WFBP, however, the iMAR algorithm with dedicated settings for large metallic implants was superior to the algorithm specifically adjusted to spinal implants. Highlights: Both iterative MAR algorithms reduced artifacts compared to standard technique. The iterative MAR with parameters adjusted to large metallic implants was superior. Properly adjusting iterative MAR settings helps to achieve optimal image quality.