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Outlier detection in cardiac diffusion tensor imaging: Shot rejection or robust fitting?

Sam Coveney1, Maryam Afzali1, Lars Mueller1

  • 1Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom.

Medical Image Analysis
|December 12, 2024
PubMed
Summary
This summary is machine-generated.

Robust fitting methods, particularly multiple voxel outlier detection (MVOD), improve cardiac diffusion tensor imaging (cDTI) analysis by enhancing statistical significance and reducing errors. This approach offers superior image quality assessment compared to traditional methods.

Keywords:
CardiacDiffusion tensor imagingM-estimatorMagnetic resonance imagingOutlier detectionRobust estimation

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Cardiovascular Research

Background:

  • Cardiac diffusion tensor imaging (cDTI) is susceptible to image corruption, impacting analysis accuracy.
  • Current outlier detection methods like single voxel outlier detection (SVOD) and shot-rejection (SR) have limitations in addressing these corruptions effectively.

Purpose of the Study:

  • To develop and evaluate robust fitting methods, incorporating multiple voxel outlier detection (MVOD), to improve the reliability of cDTI analysis.
  • To compare the performance of robust fitting with MVOD against traditional methods (SVOD, SR) in healthy and diseased hearts.

Main Methods:

  • Derivation of robust fitting methods using M-estimators for both non-linear least squares and weighted least squares fitting.
  • Application of single voxel outlier detection (SVOD) and multiple voxel outlier detection (MVOD) for outlier identification in cDTI datasets.
  • Comparison of robust fitting methods with and without SR against non-robust fitting with/without SR on datasets from healthy volunteers and hypertrophic cardiomyopathy patients.

Main Results:

  • Robust fitting methods, especially with MVOD, yielded significantly larger group differences and statistical significance for key diffusion metrics (MD, FA, E2A) compared to non-robust methods.
  • MVOD demonstrated the greatest improvements in group differences for MD and FA.
  • Visual analysis confirmed the superiority of robust fitting over SR, particularly in challenging image quality scenarios.
  • Synthetic experiments showed MVOD achieved lower root-mean-square error than SVOD.

Conclusions:

  • Robust fitting methods, particularly MVOD, offer a significant advancement for cDTI analysis, enhancing sensitivity to detect group differences.
  • MVOD effectively addresses shared deficiencies of SR and SVOD, providing more accurate and reliable diffusion parameter estimation.
  • The proposed robust methods are superior to traditional approaches, especially in the presence of image corruption, leading to more robust and statistically powerful findings in cardiovascular research.