NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction

Xinrui Jiang, Yohan Jun, Jaejin Cho, Mengze Gao, Xingwang Yong +1 more
1/22/2024
eess.IVcs.LGeess.SP

No official code found. Best implementation: Xinrui-Jiang/NLCG-Net (3 stars, last commit 2025-12-25).

Abstract

Typical quantitative MRI (qMRI) methods estimate parameter maps after image reconstructing, which is prone to biases and error propagation. We propose a Nonlinear Conjugate Gradient (NLCG) optimizer for model-based T2/T1 estimation, which incorporates U-Net regularization trained in a scan-specific manner. This end-to-end method directly estimates qMRI maps from undersampled k-space data using mono-exponential signal modeling with zero-shot scan-specific neural network regularization to enable high fidelity T1 and T2 mapping. T2 and T1 mapping results demonstrate the ability of the proposed NLCG-Net to improve estimation quality compared to subspace reconstruction at high accelerations.

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Code Implementations(1)

Xinrui-Jiang/NLCG-NetCommunity100%
30PythonNov 14, 202310 months ago

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Cite this paper

@article{jiang2024nlcgnet,
  title  = {NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction},
  author = {Xinrui Jiang and Yohan Jun and Jaejin Cho and Mengze Gao and Xingwang Yong and Berkin Bilgic},
  year   = {2024},
  eprint = {2401.12004},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.12004v1}
}

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