ArticleScientific reports2021
Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
What it found
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Who cites it
77 citing papers in PubMed, 140 citations in OpenAlex.
- Advancing the Volumetric Analysis of Ultra-Low-Field Brain MRI Using Image-to-Image Translation.Magnetic resonance in medicine · 2026Article
- MRpro: Open Framework for Model-Based, Learned, and Quantitative MR Imaging-Application to Low-Field MRI.NMR in biomedicine · 2026Article
- Fast Submillimeter Whole-Brain TMagnetic resonance in medicine · 2026Article
- Neuroimaging access in Chad: Infrastructure barriers and opportunities for accessible magnetic resonance imaging.medRxiv : the preprint server for health sciences · 2026Article
- A single-view-based electroacoustic tomography imaging using deep learning for electroporation monitoring.Medical physics · 2026Article
- Fast zero-shot deep learning-based denoising method for low-field MR images.Magma (New York, N.Y.) · 2026Article
- Open-Source Multinuclear Low-Field Preclinical MRI Scanner.NMR in biomedicine · 2026Article
- A generalizable foundation model for analysis of human brain MRI.Nature neuroscience · 2026Article
- A scoping review of portable ultra-low-field MRI studies in patients with acquired brain injury: Past, present, and future.Neuroimage. Reports · 2026Review
- Subject grounding to reduce electromagnetic interference for MRI scanners operating in unshielded environments.Magnetic resonance in medicine · 2026Article
- Band-limited implicit neural representations for diffusion-weighted imaging denoising.Physics in medicine and biology · 2026Article
- GLAPAL-H: Global, Local, and Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.IEEE transactions on bio-medical engineering · 2026Article
- Ultra-low-field MRI: a David versus Goliath challenge in modern imaging.La Radiologia medica · 2025Review
- Robust multi-coil MRI reconstruction via self-supervised denoising.Magnetic resonance in medicine · 2025Article
- In-silico comparison of a diffusion model with conventionally trained deep networks for translating 64mT to 3T brain FLAIR.Scientific reports · 2025Article
- HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.NPJ digital medicine · 2025Article
- Pediatric Neuroimaging: Advanced Techniques and Clinical Applications.Radiologic clinics of North America · 2025Review
- Open-source, customizable phantom for low-field magnetic resonance imaging.Magma (New York, N.Y.) · 2025Article
- Morphological Brain Analysis Using Ultra Low-Field MRI.Human brain mapping · 2025Article
- GLAPAL-H: Global, Local, And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.medRxiv : the preprint server for health sciences · 2025Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.
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What OpenQuestion holds
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.