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

  • Woojin Jung
  • , Jee Young Kim
  • , Jingyu Ko
  • , Geunu Jeong
  • , Hyun Gi Kim

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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).

Original languageEnglish
Pages (from-to)5468-5479
Number of pages12
JournalEuropean Radiology
Volume32
Issue number8
DOIs
StatePublished - Aug 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to European Society of Radiology.

Keywords

  • Brain
  • Children
  • Magnetic resonance imaging
  • Neural networks, computer
  • Young adults

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