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HDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the

Dilbag Singh1, Ravinder R Regatte1, Marcelo V W Zibetti1

  • 1Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.

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Summary
This summary is machine-generated.

A new hybrid deep learning and non-linear least squares (HDNLS) model accelerates T1ρ mapping in MRI. HDNLS offers a fast and reliable solution for quantitative imaging, outperforming traditional methods in speed and accuracy.

Keywords:
T1ρ mappingdeep learningknee jointmulti-component MRI fittingnon-linear least squares (NLS)

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Area of Science:

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

Background:

  • Non-linear least squares (NLS) is standard for T1ρ mapping but is slow and sensitive to initial guesses.
  • Deep learning (DL) methods are faster but can be noise-sensitive and require NLS data for training.
  • Existing methods struggle to balance speed, accuracy, and noise robustness in quantitative MRI parameter estimation.

Purpose of the Study:

  • To develop a hybrid deep learning and non-linear least squares (HDNLS) model for accelerated multi-component T1ρ parameter mapping.
  • To evaluate the performance of HDNLS and its variants for T1ρ mapping, particularly in the knee joint.
  • To investigate the impact of NLS iterations as a regularization technique within the HDNLS framework.

Main Methods:

  • Developed HDNLS, combining voxel-wise DL trained on synthetic data with iterative NLS.
  • Introduced four HDNLS variants (Ultrafast-NLS, Superfast-HDNLS, HDNLS, Relaxed-HDNLS) to balance speed and accuracy.
  • Analyzed the effect of NLS iterations on HDNLS performance and parameter estimation stability.

Main Results:

  • HDNLS achieved comparable accuracy to NLS and regularized-NLS (RNLS) with a significant speed improvement (minimum 13-fold).
  • HDNLS demonstrated superior estimation quality compared to pure DL methods while maintaining high speed.
  • The number of NLS iterations in HDNLS effectively regularizes parameter estimation, improving robustness to noise.

Conclusions:

  • HDNLS provides a fast, reliable, and accurate solution for multi-component T1ρ mapping, overcoming limitations of NLS and DL alone.
  • The HDNLS framework offers tunable configurations to meet specific requirements for speed and performance in quantitative MRI.
  • HDNLS represents a significant advancement for efficient and robust parameter estimation in T1ρ imaging applications.