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European Radiology

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22.03.2022 | Magnetic Resonance

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

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

Erschienen in: European Radiology | Ausgabe 8/2022

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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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Titel: Highly accelerated 3D MPRAGE using deep neural network–based reconstruction for brain imaging in children and young adults
verfasst von: Woojin Jung
JeeYoung Kim
Jingyu Ko
Geunu Jeong
Hyun Gi Kim
Publikationsdatum: 22.03.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 8/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08687-6

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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

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Abstract

Objectives

Methods

Results

Conclusions

Key Points

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