Tensor‐decomposition regularized learning for fast and high‐fidelity multi‐parametric microstructural MR imaging

Wenxin Fan; Jian Cheng; Qiyuan Tian; Rongjie Wu; Juan Zou; Weixin Si; Zan Chen; Shanshan Wang

doi:10.1002/mp.70359

March 1, 2026Medical Physics

Tensor‐decomposition regularized learning for fast and high‐fidelity multi‐parametric microstructural MR imaging

View at Publisher

Ask AI

Bookmark

Share

Key Points

To develop DeepMpMRI for accurate microstructural imaging under sparse conditions.
Tensor-decomposition-based learning
Multi-parametric imaging techniques
Application under sparse sampling conditions
Achieves high fidelity in tissue characterization
Demonstrates robustness under sparse sampling
Strong potential for clinical applications in diffusion MRI

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

DeepMpMRI demonstrates efficient tissue characterization in multi-parametric imaging, indicating strong clinical potential.

Abstract

DeepMpMRI enables accurate and robust multi-parametric microstructural imaging under sparse sampling conditions, showing strong potential for clinical translation in efficient diffusion MRI-based tissue characterization.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Fan et al. (2026) studied this question.

synapsesocial.com/papers/69be38da6e48c4981c6799a5 https://doi.org/10.1002/mp.70359

View at Publisher

Ask AI

Bookmark

Share

Tensor‐decomposition regularized learning for fast and high‐fidelity multi‐parametric microstructural MR imaging

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Tensor‐decomposition regularized learning for fast and high‐fidelity multi‐parametric microstructural MR imaging

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading