Sensitivity analysis of different quality assurance methods for magnetic resonance imaging in radiotherapy

Mary Adjeiwaah1, Anders Garpebring1, Tufve Nyholm1

  • 1Department of Radiation Sciences, Umeå University, Umeå, Sweden.

Abstract

Insights

Standard quality assurance (QA) methods for magnetic resonance imaging (MRI) in radiotherapy (RT) are lacking. This study evaluated two QA protocols, finding they can detect common issues affecting MR image quality in RT settings.

Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Quality Assurance

Background:

  • Standardized quality assurance (QA) protocols for magnetic resonance imaging (MRI) in radiotherapy (RT) are currently unavailable.
  • Ensuring high-quality MR images is critical for accurate RT planning and delivery.
  • Existing QA methods may not adequately address the unique challenges of MRI in RT environments.

Purpose of the Study:

  • To evaluate the efficacy of two distinct QA protocols in detecting common image quality degradations specific to MRI in RT.
  • To assess the sensitivity of the American College of Radiology (ACR) MRI QA phantom and a commercial geometric accuracy phantom.
  • To identify potential solutions for monitoring MRI system performance in RT.

Main Methods:

  • The American College of Radiology (ACR) MRI QA phantom was utilized to establish machine-specific action limits for image quality parameters.
  • An exploratory survey identified common issues affecting MR image quality.
  • These identified issues were simulated as provocations to degrade MR image quality.
  • The detection capabilities of the ACR phantom and a geometric accuracy phantom were assessed against these simulated degradations.

Main Results:

  • Machine-specific action limits were established and found comparable to ACR acceptable values.
  • The geometric accuracy phantom detected significant distortions (up to 22.2 mm) due to uncorrected gradient nonlinearity and metal artifacts (3.4 mm).
  • The ACR phantom effectively identified signal variations, electrical interference, and metal artifacts but failed to detect individual coil element failures.

Conclusions:

  • The combined use of the ACR MRI QA phantom and a large field-of-view geometric accuracy phantom demonstrated sensitivity in identifying several common MR image quality problems.
  • These integrated QA protocols offer a potential tool for consistent performance monitoring of MRI systems within the radiotherapy setting.
  • Further development may be needed to address all potential failure modes, such as individual coil element issues.

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