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<p>The increasing number of patients with metal implants raises concerns about metal-induced geometric distortions (MD) in MR-guided treatments. This study proposes a method for three-dimensional quantification of MD and evaluates its accuracy and reliability. A 3D lattice phantom was designed and measured with two sequences (VIBE and SPACE) and two implants (crown-supported-dental-implant and stainless-steel-bracket). Automated detection of displacement of 9360 crossing points caused by MD was performed. Distortion-quantification accuracy was improved by correcting for noise-induced error (NE), related to different signal-to-noise ratios (SNR), and implant-related signal loss and pile-up artifact volumes (SLPUA). The method’s accuracy was validated against computed tomography. Results showed high reliability, with an excellent intraclass correlation coefficient (≥ 0.99) and low mean residual errors in all directions (2.6%/1.6%/1.8% of voxel size in X/Y/Z direction). SNR/SLPUA volumes were significant confounders (p-value ≤ 0.001) when comparing different sequences/implants, but corrections significantly reduced their impacts (p-value ≤ 0.001). This method enables accurate 3D MD quantification and fair comparison across different sequences/implants. By optimizing MRI protocols for MD minimization and defining implant-specific MD profiles for patient data correction, it may help improve spatial accuracy in MRI-guided treatments in the future.</p>

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3D quantification of metal-induced geometric distortions in MRI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月28日

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  • Hao Li,
  • Ali C. Özen,
  • Alexander Juerchott,
  • Michael Breckwoldt,
  • Jessica Jesser,
  • Dominik F. Vollherbst,
  • Daniel Schwarz,
  • Martin Bendszus,
  • Sabine Heiland,
  • Tim Hilgenfeld

摘要

The increasing number of patients with metal implants raises concerns about metal-induced geometric distortions (MD) in MR-guided treatments. This study proposes a method for three-dimensional quantification of MD and evaluates its accuracy and reliability. A 3D lattice phantom was designed and measured with two sequences (VIBE and SPACE) and two implants (crown-supported-dental-implant and stainless-steel-bracket). Automated detection of displacement of 9360 crossing points caused by MD was performed. Distortion-quantification accuracy was improved by correcting for noise-induced error (NE), related to different signal-to-noise ratios (SNR), and implant-related signal loss and pile-up artifact volumes (SLPUA). The method’s accuracy was validated against computed tomography. Results showed high reliability, with an excellent intraclass correlation coefficient (≥ 0.99) and low mean residual errors in all directions (2.6%/1.6%/1.8% of voxel size in X/Y/Z direction). SNR/SLPUA volumes were significant confounders (p-value ≤ 0.001) when comparing different sequences/implants, but corrections significantly reduced their impacts (p-value ≤ 0.001). This method enables accurate 3D MD quantification and fair comparison across different sequences/implants. By optimizing MRI protocols for MD minimization and defining implant-specific MD profiles for patient data correction, it may help improve spatial accuracy in MRI-guided treatments in the future.