DTI-MR fingerprinting for rapid high-resolution whole-brain T1, T2, proton density, ADC, and fractional anisotropy mapping

Xiaozhi Cao, Congyu Liao*, Zihan Zhou, Zheng Zhong, Zhitao Li, Erpeng Dai, Siddharth Srinivasan Iyer, Ariel J. Hannum, Mahmut Yurt, Sophie Schauman, Quan Chen, Nan Wang, Jintao Wei, Yifan Yan, Hongjian He, Stefan Skare, Jianhui Zhong, Adam Kerr, Kawin Setsompop

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Purpose: This study aims to develop a high-efficiency and high-resolution 3D imaging approach for simultaneous mapping of multiple key tissue parameters for routine brain imaging, including T1, T2, proton density (PD), ADC, and fractional anisotropy (FA). The proposed method is intended for pushing routine clinical brain imaging from weighted imaging to quantitative imaging and can also be particularly useful for diffusion-relaxometry studies, which typically suffer from lengthy acquisition time. Methods: To address challenges associated with diffusion weighting, such as shot-to-shot phase variation and low SNR, we integrated several innovative data acquisition and reconstruction techniques. Specifically, we used M1-compensated diffusion gradients, cardiac gating, and navigators to mitigate phase variations caused by cardiac motion. We also introduced a data-driven pre-pulse gradient to cancel out eddy currents induced by diffusion gradients. Additionally, to enhance image quality within a limited acquisition time, we proposed a data-sharing joint reconstruction approach coupled with a corresponding sequence design. Results: The phantom and in vivo studies indicated that the T1 and T2 values measured by the proposed method are consistent with a conventional MR fingerprinting sequence and the diffusion results (including diffusivity, ADC, and FA) are consistent with the spin-echo EPI DWI sequence. Conclusion: The proposed method can achieve whole-brain T1, T2, diffusivity, ADC, and FA maps at 1-mm isotropic resolution within 10 min, providing a powerful tool for investigating the microstructural properties of brain tissue, with potential applications in clinical and research settings.

Original languageEnglish (US)
Pages (from-to)987-1001
Number of pages15
JournalMagnetic resonance in medicine
Volume91
Issue number3
DOIs
StatePublished - Mar 2024

Funding

This work was supported in part by NIH (R01‐EB020613, R01‐EB019437, R01‐MH116173, P41EB030006, and U01‐EB025162) and GE Healthcare. In the preparation of this manuscript, the OpenAI's Large Language Model (LLM), specifically the GPT‐4 architecture, was used for grammar check.

Keywords

  • MRF
  • diffusion preparation
  • quantitative mapping

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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