ArticleEuropean radiology2022
Highly accelerated 3D MPRAGE using deep neural network-based reconstruction for brain imaging in children and young adults.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- The scientific evidence of commercial AI products for MRI acceleration: a systematic review.European radiology · 2025Pooled it
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study.Scientific reports · 2023Trial
- Accelerated brain magnetic resonance imaging with deep learning reconstruction: a comparative study on image quality in pediatric neuroimaging.Pediatric radiology · 2025Article
- Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T.Neuroradiology · 2025Article
- Deep Learning-Based Reconstruction for Accelerated Cervical Spine MRI: Utility in the Evaluation of Myelopathy and Degenerative Diseases.AJNR. American journal of neuroradiology · 2025Article
- Evaluation of Image Quality and Scan Time Efficiency in Accelerated 3D T1-Weighted Pediatric Brain MRI Using Deep Learning-Based Reconstruction.Korean journal of radiology · 2025Article
- Reliability of brain volume measures of accelerated 3D T1-weighted images with deep learning-based reconstruction.Neuroradiology · 2025Article
- Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.European radiology · 2023Article
- Pediatric magnetic resonance imaging: faster is better.Pediatric radiology · 2023Review
- [Applications of Artificial Intelligence in MR Image Acquisition and Reconstruction].Journal of the Korean Society of Radiology · 2022Review
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
objectivesThis study aimed to accelerate the 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence for brain imaging through the deep neural network (DNN).
methodsThis 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.
resultsAccelerated 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 (R
conclusionsDNN-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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