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European Radiology

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22-03-2022 | Magnetic Resonance Imaging | Magnetic Resonance

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

Authors: Woojin Jung, JeeYoung Kim, Jingyu Ko, Geunu Jeong, Hyun Gi Kim

Published in: European Radiology | Issue 8/2022

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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Title: Highly accelerated 3D MPRAGE using deep neural network–based reconstruction for brain imaging in children and young adults
Authors: Woojin Jung
JeeYoung Kim
Jingyu Ko
Geunu Jeong
Hyun Gi Kim
Publication date: 22-03-2022
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Published in: European Radiology / Issue 8/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08687-6

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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