ArticleAJNR. American journal of neuroradiology2022
Accelerated Synthetic MRI with Deep Learning-Based Reconstruction for Pediatric Neuroimaging.
Article in AJNR. American journal of neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- The scientific evidence of commercial AI products for MRI acceleration: a systematic review.European radiology · 2025Pooled it
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- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- Innovations in pediatric imaging: a scoping review of the past decade with case illustrations.World journal of pediatrics : WJP · 2026Article
- 3D-QALAS synthetic MRI with Zero-DeepSub in children: initial experience including post-contrast imaging feasibility.Pediatric radiology · 2026Article
- FOCUS-DWI improves prostate cancer detection through deep learning reconstruction with IQMR technology.Abdominal radiology (New York) · 2026Article
- Two-Minute Deep Learning-Powered Brain Quantitative Mapping: Accelerating Clinical Imaging With Synthetic Magnetic Resonance Imaging.JMIR medical informatics · 2026Article
- Impact of AIR™ Recon DL on magnetic resonance imaging-based quantitative brain structure measurements.Psychoradiology · 2026Article
- Accelerated magnetic resonance imaging of hippocampal sclerosis in pediatric patients with deep learning-based reconstruction: comparison of image quality and diagnostic performance with conventional reconstruction.Pediatric radiology · 2026Article
- Whole-tumor histogram analysis of synthetic MRI for the differentiation of benign and malignant soft-tissue tumors: a preliminary study.European radiology · 2025Article
- Deep learning reconstruction for accelerating synthetic magnetic resonance imaging of the breast: a comparative analysis of accelerated and standard protocols.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning MRI halves scan time and preserves image quality across routine neuroradiologic examinations.Radiology advances · 2025Article
- Accelerated brain magnetic resonance imaging with deep learning reconstruction: a comparative study on image quality in pediatric neuroimaging.Pediatric radiology · 2025Article
- Enhancing Lesion Detection in Inflammatory Myelopathies: A Deep Learning-Reconstructed Double Inversion Recovery MRI Approach.AJNR. American journal of neuroradiology · 2025Observational
- Neurological disability and brain grey matter atrophy in primary progressive multiple sclerosis are determined by microstructural lesional changes, but not by lesion load.Journal of neurology · 2025Article
- Synthetic MR: Clinical applications in neuroradiology.Neuroradiology · 2025Review
- Super-resolution synthetic MRI using deep learning reconstruction for accurate diagnosis of knee osteoarthritis.Insights into imaging · 2025Article
- Generative AI cybersecurity and resilience.Frontiers in artificial intelligence · 2025Review
- Predictive and Explainable Artificial Intelligence for Neuroimaging Applications.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeSynthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learning-based reconstruction in pediatric neuroimaging and to investigate the impact of deep learning-based reconstruction on image quality and quantitative values in synthetic MR imaging. MATERIALS AND
methodsThis study included 47 children 2.3-14.7 years of age who underwent both standard and accelerated synthetic MR imaging at 3T. The accelerated synthetic MR imaging was reconstructed using a deep learning pipeline. The image quality, lesion detectability, tissue values, and brain volumetry were compared among accelerated deep learning and accelerated and standard synthetic data sets.
resultsThe use of deep learning-based reconstruction in the accelerated synthetic scans significantly improved image quality for all contrast weightings (
conclusionsThe use of deep learning-based reconstruction in synthetic MR imaging can reduce scan time by 42% while maintaining image quality and lesion detectability and providing consistent quantitative values. The accelerated deep learning synthetic MR imaging can replace standard synthetic MR imaging in both contrast-weighted and quantitative imaging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.