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Sci-Sat AM(1): Imaging-06: Proximity-based modification to an automatic method for tumor delineation using MRSI.

A A Heikal1, K Wachowicz2, B G Fallone1,2

  • 1University of Alberta, Department of Physics, Edmonton, AB.

Medical Physics
|May 18, 2017
PubMed
Summary

This study refines the Choline-to-NAA Index (CNI) for brain tumor identification using MRSI. The modified method improves accuracy by accounting for increased variability in normal tissue, reducing false positives.

Keywords:
BrainCancerMagnetic resonance imagingMedical imagingStatistical model calculationsTissue engineeringTissues

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Oncology

Background:

  • The Choline-to-NAA Index (CNI) is a standard method for biologically identifying brain tumors using Magnetic Resonance Spectroscopic Imaging (MRSI).
  • The original CNI method assumes minimal variation in Choline (Cho) to N-Acetylaspartate (NAA) ratios within normal brain tissue.
  • Advancements in MRSI sequences reveal greater variability in Cho-to-NAA levels in normal tissue, challenging the specificity of the existing CNI method.

Purpose of the Study:

  • To modify the Choline-to-NAA Index (CNI) to enhance its specificity in brain tumor delineation.
  • To address the increased uncertainty in Cho-to-NAA levels within normal brain tissue observed with advanced MRSI techniques.

Main Methods:

  • Introduced a modified CNI method that defines a high-certainty tumor volume and an adjacent uncertainty region.
  • Segmented voxels within the uncertainty region as either tumor or normal tissue based on proximity to high-certainty tumor areas.
  • Moved away from an arbitrary CNI threshold to a more nuanced approach for tumor boundary definition.

Main Results:

  • Preliminary results indicate that the modified CNI method reduces the number of false positives compared to the original CNI method.
  • The new approach effectively segments voxels in the uncertainty region, improving diagnostic accuracy.
  • The proposed modification enhances the biological identification of brain tumors by accounting for tissue variability.

Conclusions:

  • The modified CNI method offers improved specificity for brain tumor delineation in MRSI.
  • This approach is essential for accurate tumor identification given the increasing detection of variability in normal brain tissue.
  • The method shows promise in decreasing false-positive diagnoses in neuro-oncology.