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Related Experiment Video

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High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem
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Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising.

Qiyuan Tian1, Natalia Zaretskaya2, Qiuyun Fan1

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States.

Neuroimage
|March 12, 2021
PubMed
Summary

Image denoising significantly improves sub-millimeter resolution T1-weighted MRI for accurate cerebral cortical surface reconstruction. This method enhances brain imaging analysis without lengthy scan times or motion artifacts.

Keywords:
BM4DConvolutional neural networkDeep learningHigh-resolutionMagnetic resonance imagingNon-local meansT(1)-weighted image

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Area of Science:

  • Neuroimaging and Computational Anatomy
  • Magnetic Resonance Imaging (MRI) analysis

Background:

  • Sub-millimeter isotropic resolution T1-weighted MRI offers superior accuracy for cortical surface reconstruction compared to standard 1-mm resolution.
  • High-resolution MRI is inherently noisy, requiring multiple repetitions to improve signal-to-noise ratio for precise cortical boundary delineation.
  • Prolonged acquisition times and motion artifacts limit the clinical and neuroscientific application of sub-millimeter resolution cortical surface reconstruction.

Purpose of the Study:

  • To evaluate the efficacy of image denoising techniques on single-repetition sub-millimeter T1-weighted MRI for cortical surface reconstruction.
  • To systematically characterize the impact of denoising on image quality and accuracy of cortical measurements.

Main Methods:

  • Empirical data acquired at 0.6 mm isotropic resolution was denoised using three methods: denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D), and adaptive optimized non-local means (AONLM).
  • Denoised single-repetition images were compared to 6-repetition averaged images to assess similarity, signal-to-noise ratio, contrast loss, and surface placement accuracy.
  • Cortical thickness estimation and scan-rescan variability were evaluated.

Main Results:

  • Denoised single-repetition images closely resembled 6-repetition averaged images, exhibiting low mean absolute difference (~0.016), high peak signal-to-noise ratio (~33.5 dB), and high structural similarity (~0.92).
  • Gray matter-white matter and gray matter-cerebrospinal fluid surface placement discrepancies were below 165 μm and 155 μm, respectively, with cortical thickness estimation below 145 μm, outperforming 1-mm data by a factor of 2-3.
  • Denoising performance was equivalent to averaging 1.6-2.5 repetitions, significantly reducing scan time and motion artifact potential.

Conclusions:

  • Image denoising is a viable strategy to achieve high-quality cortical surface reconstruction from single-repetition sub-millimeter T1-weighted MRI.
  • This approach enables accurate cortical anatomy quantification, analysis, and visualization, overcoming limitations of prolonged acquisition times and motion artifacts.
  • The findings support the widespread adoption of denoising methods for advanced neuroscientific and clinical applications utilizing high-resolution brain imaging.