Metal Artifact Reduction Magnetic Resonance Imaging Around Arthroplasty Implants

Kumar, Neil M.; Schon, Lew C.; Fritz, Jan

doi:10.1097/rli.0000000000000350

Cited by 34 publications

(13 citation statements)

References 27 publications

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“…Regarding artifact size, the standard nonoptimized sequence produced the largest peri-implant signal void, and each optimization (oBW, VAT, oBW-VAT, and SEMAC) resulted in artifact size reduction in both phantoms, which is consistent with findings at lower field strengths 16,18,27,42 . However, at 7-T MRI, only the combination of oBW and VAT (oBW-VAT) as well as SEMAC produced a significantly smaller peri-implant signal void for all image contrasts (T1-w, T2-w, and IM-w) compared with the nonoptimized sequence (STA), whereas at 1.5-T and 3-T MRI, basic techniques such as increasing the receive bandwidth alone also have the potential for a significant signal void reduction compared with nonoptimized sequences 33,50 …”

Section: Discussionsupporting

confidence: 78%

“…In summary, the findings in this study indicate no significant overall metal artifact reduction of either oBW or of VAT alone compared with standard nonoptimized TSE sequences at 7-T MRI. In contradiction to these findings, oBW alone at 1.5-T and 3-T MRI has been shown to significantly reduce image distortions 18,33,50 . An explanation for this discrepancy between ultra-high field MRI and lower field strengths may be that an increase of the receive bandwidth to around 500 Hz/pixel at 7-T MRI may be insufficient to yield a significant artifact reduction, and even higher receive bandwidths (>>500 Hz/pixel) might be needed.…”

Section: Discussionmentioning

confidence: 93%

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Basic and Advanced Metal-Artifact Reduction Techniques at Ultra-High Field 7-T Magnetic Resonance Imaging—Phantom Study Investigating Feasibility and Efficacy

et al. 2022

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Objectives: The aim of this study was to demonstrate the feasibility and efficacy of basic (increased receive bandwidth) and advanced (view-angle tilting [VAT] and slice-encoding for metal artifact correction [SEMAC]) techniques for metalartifact reduction in ultra-high field 7-T magnetic resonance imaging (MRI). Materials and Methods: In this experimental study, we performed 7-T MRI of titanium alloy phantom models composed of a spinal pedicle screw (phantom 1) and an intervertebral cage (phantom 2) centered in a rectangular LEGO frame, embedded in deionized-water-gadolinium (0.1 mmol/L) solution. The following turbo spin-echo sequences were acquired: (1) nonoptimized standard sequence;(2) optimized, that is, increased receive bandwidth sequence (oBW); (3) VAT; (4) combination of oBW and VAT (oBW-VAT); and (5) SEMAC. Two fellowshiptrained musculoskeletal radiologists independently evaluated images regarding peri-implant signal void and geometric distortion (a, angle measurement and b, presence of circular shape loss). Statistics included Friedman test and Cochran Q test with Bonferroni correction for multiple comparisons. P values <0.05 were considered to represent statistical significance. Results: All metal-artifact reduction techniques reduced peri-implant signal voids and diminished geometric distortions, with oBW-VAT and SEMAC being most efficient. Compared with nonoptimized sequences, oBW-VAT and SEMAC produced significantly smaller peri-implant signal voids (all P ≤ 0.008) and significantly smaller distortion angles ( P ≤ 0.001). Only SEMAC could significantly reduce distortions of circular shapes in the peri-implant frame ( P ≤ 0.006). Notably, increasing the number of slice-encoding steps in SEMAC sequences did not lead to a significantly better metal-artifact reduction (all P ≥ 0.257). Conclusions: The use of basic and advanced methods for metal-artifact reduction at 7-T MRI is feasible and effective. Both a combination of increased receive bandwidth and VAT as well as SEMAC significantly reduce the peri-implant signal void and geometric distortion around metal implants.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 93%

Basic and Advanced Metal-Artifact Reduction Techniques at Ultra-High Field 7-T Magnetic Resonance Imaging—Phantom Study Investigating Feasibility and Efficacy

et al. 2022

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“…Such artifacts were visible on a few SPGR images. Fortunately, UTE sequences are less prone to susceptibility artifacts of this kind (35, 36).…”

Section: Discussionmentioning

confidence: 99%

Ultrashort Time-to-Echo Magnetic Resonance Imaging at 3 T for the Detection of Spondylolysis in Cadaveric Spines

et al. 2019

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“…However, MRI is not the most common test used to evaluate treatment options and ONFH during postoperative FNF follow-up. MRIs are affected by metal implants, which may cause potential internal fixation losses and thermal effect (43). MRI tests are more expensive, take longer, and require the radiologist to have a higher level of diagnostic experience.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Improves Osteonecrosis Prediction of Femoral Head After Internal Fixation Using Hybrid Patient and Radiograph Variables

et al. 2020

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Femoral neck fractures (FNFs) are a great public health problem that leads to a high incidence of death and dysfunction. Osteonecrosis of the femoral head (ONFH) after internal fixation of FNF is a frequently reported complication and a major cause for reoperation. Early intervention can prevent osteonecrosis aggravation at the preliminary stage. However, at present, failure to diagnose asymptomatic ONFH after FNF fixation hinders effective intervention at early stages. The primary objective of this study was to develop a predictive model for postoperative ONFH using deep learning (DL) methods developed using plain X-ray radiographs and hybrid patient variables. A two-center retrospective study of patients who underwent closed reduction and cannulated screw fixation was performed. We trained a convolutional neural network (CNN) model using postoperative pelvic radiographs and the output regressive radiograph variables. A less experienced orthopedic doctor, and an experienced orthopedic doctor also evaluated and diagnosed the patients using postoperative pelvic radiographs. Hybrid nomograms were developed based on patient and radiograph variables to determine predictive performance. A total of 238 patients, including 95 ONFH patients and 143 non-ONFH patients, were included. A CNN model was trained using postoperative radiographs and output radiograph variables. The accuracy of the validation set was 0.873 for the CNN model, and the algorithm achieved an area under the curve (AUC) value of 0.912 for the prediction. The diagnostic and predictive ability of the algorithm was superior to that of the two doctors, based on the postoperative X-rays. The addition of DL-based radiograph variables to the clinical nomogram improved predictive performance, resulting in an AUC of 0.948 (95% CI, 0.920-0.976) and better calibration. The decision curve analysis showed that adding the DL increased the clinical usefulness of the nomogram compared with a clinical approach alone. In conclusion, we constructed a DL facilitated nomogram that incorporated a hybrid of radiograph and patient variables, which can be used to improve the prediction of preoperative osteonecrosis of the femoral head after internal fixation.

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Metal Artifact Reduction Magnetic Resonance Imaging Around Arthroplasty Implants

Cited by 34 publications

References 27 publications

Basic and Advanced Metal-Artifact Reduction Techniques at Ultra-High Field 7-T Magnetic Resonance Imaging—Phantom Study Investigating Feasibility and Efficacy

Basic and Advanced Metal-Artifact Reduction Techniques at Ultra-High Field 7-T Magnetic Resonance Imaging—Phantom Study Investigating Feasibility and Efficacy

Ultrashort Time-to-Echo Magnetic Resonance Imaging at 3 T for the Detection of Spondylolysis in Cadaveric Spines

Deep Learning Improves Osteonecrosis Prediction of Femoral Head After Internal Fixation Using Hybrid Patient and Radiograph Variables

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