ArticleNeuroImage2021
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
What it found
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Who cites it
75 citing papers in PubMed, 8 citations in OpenAlex.
- Advancing the Volumetric Analysis of Ultra-Low-Field Brain MRI Using Image-to-Image Translation.Magnetic resonance in medicine · 2026Article
- MRI Characterization of Structural Brain Abnormalities in NGLY1 Deficiency.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Article
- Voice perception reflects graded sensitivity to acoustic cues along the auditory hierarchy.Current biology : CB · 2026Article
- Article
- Causal evidence from invasive recordings reveals distributed acoustic feature encoding underlying categorical voice perception.NeuroImage · 2026Article
- Portable Ultra-Low Field MRI Deep-Learning Algorithms for White Matter Lesion Segmentation Improve Accuracy and Reflect Clinical Disability in Multiple Sclerosis.medRxiv : the preprint server for health sciences · 2026Article
- Brain Morphology in Subacute Spinal Cord Injury: Insights Into Functional Recovery Across Brain Networks.Brain and behavior · 2026Article
- Diffusion Tensor MRI and Spherical-Deconvolution-Based Tractography on an Ultra-Low Field Portable MRI System.Human brain mapping · 2026Article
- AI-Based Brain Volumetry Without MPRAGE? Evaluation of Synthetic T1-MPRAGE from 2D T2/FLAIR.Diagnostics (Basel, Switzerland) · 2026Article
- The modular nature of long-association pathways in the human brain.bioRxiv : the preprint server for biology · 2026Article
- Repeatable tract-based diffusion metrics on a portable 64 mT MRI: a foundation for global population neuroscience.Frontiers in neuroimaging · 2026Article
- A modular and flexible pipeline for intraoperative electrode reconstruction and localization in patients with brain lesions.Frontiers in neural circuits · 2026Article
- Brain age trajectories and cognition after stroke in two longitudinal cohorts.Brain communications · 2026Article
- Targeting depression circuitry with H1 coil Transcranial Magnetic Stimulation: a retrospective circuit mapping study.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- RAVEN: Robust, generalizable, multi-resolution structural MRI upsampling using Autoencoders.bioRxiv : the preprint server for biology · 2025Article
- A Contrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation.Human brain mapping · 2025Article
- MPRAGEMagnetic resonance in medicine · 2025Article
- Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits.IEEE signal processing magazine · 2025Article
- "Recon-all-clinical": Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI.Medical image analysis · 2025Article
- Morphological Brain Analysis Using Ultra Low-Field MRI.Human brain mapping · 2025Article
15 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
11 authors at 4 institutions in 2 countries.
Funding
Abstract
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well-typically requiring T1-weighted images (e.g., MP-RAGE scans). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing in clinical settings every year. In turn, the inability to quantitatively analyze these scans hinders the adoption of quantitative neuro imaging in healthcare, and also precludes research studies that could attain huge sample sizes and hence greatly improve our understanding of the human brain. Recent advances in convolutional neural networks (CNNs) are producing outstanding results in super-resolution and contrast synthesis of MRI. However, these approaches are very sensitive to the specific combination of contrast, resolution and orientation of the input images, and thus do not generalize to diverse clinical acquisition protocols - even within sites. In this article, we present SynthSR, a method to train a CNN that receives one or more scans with spaced slices, acquired with different contrast, resolution and orientation, and produces an isotropic scan of canonical contrast (typically a 1 mm MP-RAGE). The presented method does not require any preprocessing, beyond rigid coregistration of the input scans. Crucially, SynthSR trains on synthetic input images generated from 3D segmentations, and can thus be used to train CNNs for any combination of contrasts, resolutions and orientations without high-resolution real images of the input contrasts. We test the images generated with SynthSR in an array of common downstream analyses, and show that they can be reliably used for subcortical segmentation and volumetry, image registration (e.g., for tensor-based morphometry), and, if some image quality requirements are met, even cortical thickness morphometry. The source code is publicly available at https://github.com/BBillot/SynthSR.
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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.