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Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop a real-time abdominal T2 mapping method without requiring breath-holding or respiratory-gating.

Methods

The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T2 mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T2 mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set.

Results

The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T2 mapping. The average NMSE and R2 values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson’s correlation coefficient between our predicted T2 and reference T2 in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T2 value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T2 values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test–retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively.

Conclusions

Free-breathing abdominal T2 mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T2 mapping in the abdomen.

Key Points

• MOLED achieves free-breathing abdominal T 2 mapping in about 100 ms, enabling real-time capture of T 2 value changes due to CA injection in abdominal organs.

• Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.

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Data Availability

Data will be made available from the corresponding author on reasonable request.

Abbreviations

AC-LORAKS:

Auto-calibrated low-rank modeling of local k-space neighborhoods

CA:

Contrast agent

CV:

Coefficient of variation

DL:

Deep learning

EPI:

Echo-planar imaging

FLL:

Focal liver lesion

FM:

Feature map

GAN:

Generative adversarial network

MAE:

Mean absolute error

MOLED:

Multiple overlapping-echo detachment

NMSE:

Normalized root-mean-squared error

PD:

Proton density

PDw:

Proton density-weighted

RF:

Radio frequency

SE:

Spin-echo

SNR:

Signal-to-noise ratio

T2w:

T2-weighted

TCGA-LIHC:

The Cancer Genome Atlas Liver Hepatocellular Carcinoma

TSE:

Turbo spin-echo

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Funding

This work was supported by the National Natural Science Foundation of China under grant numbers 82071913, 22161142024, 11775184, and U1805261.

Author information

Author notes

  1. Xi Lin and Lixing Dai contributed equally to this work.

Authors and Affiliations

  1. Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China

    Xi Lin, Lixing Dai, Qinqin Yang, Qizhi Yang, Lingceng Ma, Congbo Cai, Zhong Chen & Shuhui Cai

  2. Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, 310027, Zhejiang, China

    Hongjian He & Jianhui Zhong

  3. Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China

    Jingjing Liu, Jingliang Cheng & Jianfeng Bao

  4. Department of Imaging Sciences, University of Rochester, Rochester, NY, USA

    Jianhui Zhong

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  1. Xi Lin
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Correspondence to Congbo Cai or Jianfeng Bao.

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The scientific guarantor of this publication is Professor Congbo Cai.

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Lin, X., Dai, L., Yang, Q. et al. Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction. Eur Radiol 33, 4938–4948 (2023). https://doi.org/10.1007/s00330-023-09417-2

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