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Task-based assessment for neural networks: evaluating undersampled MRI reconstructions based on human observer signal

Joshua D Herman1, Rachel E Roca1, Alexandra G O'Neill1

  • 1Manhattan College, Department of Mathematics, The Bronx, New York, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|August 15, 2024
PubMed
Summary

Conventional metrics overestimated undersampling rates in neural network-reconstructed MRI. Human observers preferred lower acceleration (2×) than metrics (3×) for detecting subtle signals, highlighting the need for task-based image quality assessment.

Keywords:
magnetic resonance imagingneural networkstask-based assessmentundersampling

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Image Reconstruction

Background:

  • Neural networks are increasingly used for reconstructing undersampled magnetic resonance imaging (MRI).
  • Assessing image quality in these reconstructions is challenging due to complex artifacts.
  • Task-based approaches are needed to evaluate image quality relevant to specific diagnostic tasks.

Purpose of the Study:

  • To compare conventional global quantitative metrics with human observer performance for evaluating image quality in neural network-reconstructed undersampled MRI.
  • To determine the optimal undersampling acceleration (2×, 3×, 4×, 5×) based on both conventional metrics and human detection task performance.

Main Methods:

  • Reconstruction of undersampled MRI using a U-Net with 1D undersampling rates (2× to 5×).
  • Evaluation using conventional metrics: normalized root mean squared error (NRMSE) and structural similarity (SSIM).
  • Comparison with a task-based assessment using a two-alternative forced choice (2-AFC) observer study for detecting a subtle signal.

Main Results:

  • Human observers in the 2-AFC study selected 2× undersampling as optimal.
  • Conventional metrics (SSIM and NRMSE) favored 3× undersampling.
  • This discrepancy was observed for both SSIM and MSE loss functions across different training set sizes.

Conclusions:

  • Conventional metrics like SSIM and NRMSE overestimate achievable undersampling rates when evaluated against human performance in subtle signal detection tasks.
  • Task-based image quality assessment is crucial for accurately determining optimal undersampling in neural network-based MRI reconstruction.
  • A steep decline in image quality occurs between 2× and 3× undersampling, which is better identified by human observers than global metrics.