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Updated: Jul 2, 2025

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Published on: May 17, 2018

Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

Austen N Curcuru¹, Deshan Yang², Hongyu An³

¹Department of Radiation Oncology, Washington University in St. Louis, St. Louis, Missouri, USA.

Journal of Applied Clinical Medical Physics

|February 18, 2024

Summary

This summary is machine-generated.

This study used CycleGAN to reduce implantable cardioverter defibrillator (ICD) artifacts in MRI-guided radiotherapy (MRgRT) images. The AI model significantly improved image quality and target tracking accuracy for better patient treatment.

Keywords:

ICD MRI artifact deep learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiotherapy

Background:

Implantable cardioverter defibrillators (ICDs) create artifacts that challenge magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT).
Artifacts can compromise image quality and treatment accuracy.

Purpose of the Study:

To evaluate an unsupervised generative adversarial network (CycleGAN) for mitigating ICD artifacts in balanced steady-state free precession (bSSFP) cine MRIs.
To enhance image quality and tracking performance for MRgRT procedures.

Main Methods:

A CycleGAN model was trained on bSSFP MRI data from 14 healthy volunteers with simulated ICDs.
The model was tested using a Leave-One-Out cross-validation scheme.
Image quality and tracking metrics (DSC, TRE, 95% HD, nRMSE, PSNR, MS-SSIM) were evaluated, alongside qualitative assessment on patient data.

Main Results:

CycleGAN reconstruction significantly improved whole-heart contour tracking: Dice similarity coefficient (DSC) increased from 0.910 to 0.935, target registration error (TRE) decreased from 4.488 to 2.877 mm, and 95% Hausdorff distance (95% HD) decreased from 10.236 to 7.700 mm.
Image quality metrics also improved: normalized root mean square error (nRMSE) decreased from 0.644 to 0.420, multiscale structural similarity (MS-SSIM) increased from 0.779 to 0.819, and peak signal-to-noise ratio (PSNR) increased from 18.744 to 22.368.
Qualitative analysis of additional datasets confirmed a reduction in ICD artifacts.

Conclusions:

CycleGAN effectively mitigates implantable cardioverter defibrillator (ICD) artifacts in MRI-guided radiotherapy (MRgRT).
The AI-driven approach significantly enhances both image quality and tracking accuracy, crucial for precise radiotherapy delivery.