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Highly accelerated 3D MPRAGE using deep neural network–based reconstruction for brain imaging in children and young adults

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

Abstract

Objectives

This study aimed to accelerate the 3D magnetization–prepared rapid gradient-echo (MPRAGE) sequence for brain imaging through the deep neural network (DNN).

Methods

This retrospective study used the k-space data of 240 scans (160 for the training set, mean ± standard deviation age, 93 ± 80 months, 94 males; 80 for the test set, 106 ± 83 months, 44 males) of conventional MPRAGE (C-MPRAGE) and 102 scans (77 ± 74 months, 52 males) of both C-MPRAGE and accelerated MPRAGE. All scans were acquired with 3T scanners. DNN was developed with simulated-acceleration data generated by under-sampling. Quantitative error metrics were compared between images reconstructed with DNN, GRAPPA, and E-SPIRIT using the paired t-test. Qualitative image quality was compared between C-MPRAGE and accelerated MPRAGE reconstructed with DNN (DNN-MPRAGE) by two readers. Lesions were segmented and the agreement between C-MPRAGE and DNN-MPRAGE was assessed using linear regression.

Results

Accelerated MPRAGE reduced scan times by 38% compared to C-MPRAGE (142 s vs. 320 s). For quantitative error metrics, DNN showed better performance than GRAPPA and E-SPIRIT (p < 0.001). For qualitative evaluation, overall image quality of DNN-MPRAGE was comparable (p > 0.999) or better (p = 0.025) than C-MPRAGE, depending on the reader. Pixelation was reduced in DNN-MPRAGE (p < 0.001). Other qualitative parameters were comparable (p > 0.05). Lesions in C-MPRAGE and DNN-MPRAGE showed good agreement for the dice similarity coefficient (= 0.68) and linear regression (R2 = 0.97; p < 0.001).

Conclusions

DNN-MPRAGE reduced acquisition time by 38% and revealed comparable image quality to C-MPRAGE.

Key Points

• DNN-MPRAGE reduced acquisition times by 38%.

• DNN-MPRAGE outperformed conventional reconstruction on accelerated scans (SSIM of DNN-MPRAGE = 0.96, GRAPPA = 0.43, E-SPIRIT = 0.88; p < 0.001).

• Compared to C-MPRAGE scans, DNN-MPRAGE showed improved mean scores for overall image quality (2.46 vs. 2.52; p < 0.001) or comparable perceived SNR (2.56 vs. 2.58; p = 0.08).

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Abbreviations

2D:

Two-dimensional

3D:

Three-dimensional

ACS:

Autocalibrating signal

C-MPRAGE:

Conventional image reconstruction of magnetization-prepared rapid gradient-echo images from conventional scans

CNR:

Contrast-to-noise ratio

CS:

Compressed sensing

DNN:

Deep neural network

DNN-MPRAGE:

Deep neural network–based reconstruction of magnetization-prepared rapid gradient-echo images from accelerated scans

DSC:

Dice similarity coefficient

E-SPIRIT:

An eigenvalue approach to iterative self-consistent parallel imaging reconstruction

GRAPPA:

Generalized autocalibrating partially parallel acquisitions

GW:

Gray-white matter

MPRAGE:

Magnetization-prepared rapid gradient-echo

MRI:

Magnetic resonance imaging

NRMSE:

Normalized mean square errors

PSNR:

Peak signal-to-noise ratio

R :

Acceleration (or reduction) factor

RSS:

Root sum of squares

SENSE:

Sensitivity encoding

SNR:

Signal-to-noise ratio

SSIM:

Structural similarity index

WAVE-CAIPI:

Wave-controlled aliasing in parallel imaging

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Funding

This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: KMDF_PR_20200901_0062, 9991006735) and the National Research Foundation of Korea (NRF) grant funded by the Korean government (Project Number: NRF-2021R1A2C1007831).

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Authors and Affiliations

  1. AIRS Medical, Seoul, Republic of Korea

    Woojin Jung, Jingyu Ko & Geunu Jeong

  2. Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea, 03312

    JeeYoung Kim & Hyun Gi Kim

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  1. Woojin Jung
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Correspondence to Hyun Gi Kim.

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Guarantor

The scientific guarantor of this publication is Hyun Gi Kim.

Conflict of interest

Three of the authors of this manuscript declare relationships with AIRS Medical Inc.

Woojin Jung, Jingyu Ko, and Geunu Jeong are employees of AIRS Medical.

The other authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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• Retrospective

• Cross-sectional study

• Performed at one institution

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Jung, W., Kim, J., Ko, J. et al. Highly accelerated 3D MPRAGE using deep neural network–based reconstruction for brain imaging in children and young adults. Eur Radiol 32, 5468–5479 (2022). https://doi.org/10.1007/s00330-022-08687-6

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  • DOI: https://doi.org/10.1007/s00330-022-08687-6

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