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Effect of basis choice on quantitative parameter estimation in accelerated subspace reconstructions.

Haoran Bai1, Ke Dai1, Yueqi Qiu1

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.

Magnetic Resonance Letters
|July 12, 2026
PubMed
Summary

Data-driven subspaces in accelerated MRI reconstructions reduce bias in quantitative T2 mapping. Simulated bases can overestimate T2 values, particularly in white/gray matter, while data-driven approaches yield more accurate T2 estimates.

Keywords:
Compressed sensingQuantitative MRISubspace reconstructions

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Quantitative Imaging
  • Spectroscopic Imaging

Background:

  • Accelerated MRI techniques are crucial for reducing scan times.
  • Subspace reconstruction methods are used to improve image quality in accelerated MRI.
  • Quantitative parameter estimation, such as T2 relaxation time, is important for tissue characterization.

Purpose of the Study:

  • To assess the impact of different simulated and data-driven subspaces on quantitative parameter estimation in highly accelerated subspace reconstructions.
  • To compare the accuracy of T2 mapping using various basis generation methods under different undersampling scenarios.

Main Methods:

  • Retrospective undersampling of fully sampled echo planar spectroscopic imaging (EPSI) datasets (9-shot and 3-shot k_y-t traversals).
  • Generation of three types of basis: data-driven from low-resolution data, simulated with uniform T2 distribution, and simulated with oversampled white matter (WM) and gray matter (GM).
  • Assessment of regularization effects by comparing T2 maps and qualitative images from fully sampled and undersampled data.

Main Results:

  • Under 3-shot undersampling, uniform T2 distribution basis led to significant WM/GM T2 overestimations (10-15 ms bias).
  • Oversampled WM/GM basis improved T2 accuracy but still introduced bias.
  • Data-driven basis provided the most accurate T2 estimates, minimizing bias in quantitative relaxation maps.

Conclusions:

  • Highly accelerated subspace reconstructions can introduce bias in quantitative relaxation maps.
  • Data-driven subspaces are effective in reducing this bias, leading to more accurate T2 parameter estimation.
  • The choice of basis generation method significantly impacts the accuracy of quantitative MRI.