Metallic artifact reduction by evaluation of the additional value of iterative reconstruction algorithms in hip prosthesis computed tomography imaging

Abstract

To evaluate iterative metal artifact reduction (iMAR) technique in images data of hip prosthesis on computed tomography (CT) and the added value of advanced modeled iterative reconstruction (ADMIRE) compared with standard filtered back projection (FBP). Twenty-eight patients addressed to CT examinations for hip prosthesis were included prospectively. Images were reconstructed with iMAR algorithm in addition to FBP and ADMIRE techniques. Measuring image noise assessed objective image quality and attenuation values with standardized region of interest (ROI) in 4 predefined anatomical structures (gluteus medius and rectus femoris muscles, inferior and anterior abdominal fat, and femoral vessels when contrast media was present). Subjective image quality was graded on a 5-point Likert scale, taking into account the size of artifacts, the metal–bone interface and the conspicuity of pelvic organs, and the diagnostic confidence. Improvement in overall image quality was statistically significant using iMAR ( P <.001) compared with ADMIRE and FBP. ADMIRE did not show any impact in image noise, attenuation value, or global quality image. iMAR showed a significant decrease in image noise in all ROIs (Hounsfield Unit) as compared with FBP and ADMIRE. Interobserver agreement was high in all reconstructions (FBP, FBP+iMAR, ADMIRE, and ADMIRE + iMAR) more than 0.8. iMAR reconstructions showed emergence of new artifacts in bone–metal interface. iMAR algorithm allows a significant reduction of metal artifacts on CT images with unilateral or bilateral prostheses without additional value of ADMIRE. It improves the analysis of surrounding tissue but potentially generates new artifacts in bone–metal interface.

Diagnostic Value of Model-Based Iterative Reconstruction Combined with a Metal Artifact Reduction Algorithm during CT of the Oral Cavity

“…We attribute the improved image quality achieved with MBIRþMAR to the incorporation of MBIR settings such as the focus size, detector size, and voxel size, which would improve the spatial resolution. In previous analyses, Wellenberg et al 39 and Neroladaki et al 40 reported that MBIRþMAR significantly reduced orthopedic metal artifacts on pelvic CT images produced by other vendors., Although the results of those studies were consistent with our findings, the AJNR Am J Neuroradiol : 2020 www.ajnr.org methods differed from ours in that either phantoms or small numbers of large orthopedic metal implants were used. A previous phantom study observed that the metal artifacts increased and the CT image accuracy decreased as the number of metal implants in the oral cavity increased.…”

Metal artifacts reduction in computed tomography: A phantom study to compare the effectiveness of metal artifact reduction algorithm, model-based iterative reconstruction, and virtual monochromatic imaging

“…As quantitative indices for dark bands, the area on the image with CT values lower than the threshold and the mean CT value of the area are frequently used. [ 2 , 6 , 7 , 18 ] Fine streak artifacts were evaluated objectively by using SD and artificial index, which was calculated using SD by placing an ROI within the fine streak artifacts [ 8 , 19 , 20 ] ; however, the SD contained the effect of image noise and did not seem to reflect only metal artifacts. To remove the effect of image noise, it seems to be necessary to subtract SD of CT values within the ROI in the phantom without the metal implant from that in the phantom with the metal implant.…”

“…[ 23 ] In comparing MARs with MBIR, Angeliki et al showed that iterative MAR allowed significant reduction of metal artifacts, including dark streaks, as compared to advanced modeled iterative reconstruction (ADMIRE; Siemens). [ 7 ] Yasaka et al reported that single-energy MAR (Canon Medical Systems, Ohtawara, Japan) significantly reduced metal artifacts, including dark streaks, as compared to a forward-projected MBIR solution (Canon Medical Systems). [ 24 ] In our study, MAR showed significant efficacy in reducing dark bands as compared to other methods of reconstruction.…”

“…Sixth, we did not assess the distortion in the shape and underestimation in the size of the metal implants and the new artifacts caused by MAR, as reported in previous studies. [ 7 , 22 , 25 – 28 ] Lastly, our study did not assess the appearance of the structures adjacent to the metal implants obscured by the metal artifacts.…”

Regularized Three-Dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction in Head and Neck CT Images

“…Although doctors make clinical efforts to manually collect such artifacts, this is a labor-intensive and time-consuming task. Many researchers have studied image filtering or reconstruction methods [2], [4]- [7], but metal artifact reduction (MAR) remains a challenging problem [8]- [10], and no standard algorithm for strong, complex artifacts with missing pixels derived from multiple metal objects has yet been established.…”