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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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A Method for Sensitivity Analysis of Automatic Contouring Algorithms Across Different MRI Contrast Weightings Using

Lucas McCullum1,2, Zayne Belal2,3, Warren Floyd2

  • 1UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.

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Summary
This summary is machine-generated.

Automatic MRI contouring algorithms show variable performance across different magnetic resonance imaging (MRI) scan parameters. This study developed a method to assess algorithm robustness, revealing performance inconsistencies even within the same contrast weighting.

Keywords:
Automatic Contouring Sensitivity AnalysisContouringHead and Neck CancerMRISegmentationSyMRISyntheticMR

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Area of Science:

  • Medical imaging analysis
  • Radiotherapy planning
  • Artificial intelligence in medicine

Background:

  • Automatic MRI-based contouring algorithms are typically trained on a single contrast weighting (e.g., T2-weighted).
  • The performance of these algorithms across varying repetition times (TR) and echo times (TE) within a contrast weighting is not well understood.
  • This lack of understanding can lead to suboptimal performance when algorithms are used at institutions with different MRI protocols.

Purpose of the Study:

  • To develop and present a methodology for evaluating the robustness of automatic MRI contouring algorithms to variations in contrast weightings.
  • To assess how changes in MRI parameters affect the accuracy of automatic gland contouring.

Main Methods:

  • Utilized SyntheticMR to simulate 216 different MRI TR and TE combinations across T1-, T2-, and PD-weighted contrasts.
  • Contoured parotid and submandibular glands using an algorithm trained on T2-weighted images.
  • Established ground truth contours through STAPLE consensus of radiation oncology residents and physicians.
  • Quantified performance using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95).

Main Results:

  • Significant performance variations were observed across different contrast weightings, including within the T2-weighted range.
  • Some algorithms performed comparably or better on T1-weighted images than expected.
  • The PD-weighted range exhibited the poorest performance.
  • Discrepancies in DSC and HD95 exceeded 0.2 and 3.66 mm, respectively, for certain structures.
  • In the T2-weighted training range, DSC values for glands exceeded interobserver variability in a significant percentage of cases.

Conclusions:

  • MRI-based automatic contouring algorithms demonstrate performance variability dependent on TR and TE combinations.
  • The proposed methodology can be used to evaluate algorithm sensitivity to variations in input data.
  • This approach aids in detecting out-of-distribution performance and monitoring performance drift over time.