ArticleMedical image analysis2020
Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 83 citations in OpenAlex.
- Development and validation of a deep learning model for radiographic classification of pediatric femoral neck fractures.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2026Article
- [A vessel-enhanced consistency-preserving diffusion model for 3T-to-7T susceptibility weighted image generation].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- LiteMamba-Synth: lightweight state space models for efficient 3T-to-7T MRI translation.Frontiers in neuroanatomy · 2026Article
- A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations.Scientific data · 2025Article
- Semi-supervised synthesis of 7T MRI from 3T using 3D FR-U-Net with anatomical segmentation consistency assessment.PloS one · 2025Article
- Voxel-Wise Fusion of 3T and 7T Diffusion MRI Data to Extract more Accurate Fiber Orientations.Brain topography · 2024Article
- High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model.Medical physics · 2024Article
- Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images.Scientific reports · 2023Article
- A paired dataset of T1- and T2-weighted MRI at 3 Tesla and 7 Tesla.Scientific data · 2023Article
- Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis.Nature communications · 2023Article
- SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry.Science advances · 2023Article
- 7.0T cardiac magnetic resonance imaging of right ventricular function in rats with high-altitude deacclimatization.Annals of translational medicine · 2023Article
- Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives.Biomedicines · 2023Review
- Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound.Frontiers in oncology · 2023Review
- Machine learning in neuro-oncology: toward novel development fields.Journal of neuro-oncology · 2022Review
- Generative adversarial networks with adaptive normalization for synthesizing T2-weighted magnetic resonance images from diffusion-weighted images.Frontiers in neuroscience · 2022Article
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Toward Studying Cognition in a Dish.Trends in cognitive sciences · 2021Review
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
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 2 institutions in 3 countries.
Funding
Abstract
Ultra-high field 7T MRI scanners, while producing images with exceptional anatomical details, are cost prohibitive and hence highly inaccessible. In this paper, we introduce a novel deep learning network that fuses complementary information from spatial and wavelet domains to synthesize 7T T1-weighted images from their 3T counterparts. Our deep learning network leverages wavelet transformation to facilitate effective multi-scale reconstruction, taking into account both low-frequency tissue contrast and high-frequency anatomical details. Our network utilizes a novel wavelet-based affine transformation (WAT) layer, which modulates feature maps from the spatial domain with information from the wavelet domain. Extensive experimental results demonstrate the capability of the proposed method in synthesizing high-quality 7T images with better tissue contrast and greater details, outperforming state-of-the-art methods.
Indexed as
Identifiers
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.