ArticleIEEE transactions on medical imaging2022
Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Bayesian-enhanced closed-loop optimization of ultrasound protocols for targeted and precise neuromodulation.bioRxiv : the preprint server for biology · 2026Article
- Computational design of foldable origami-based compressive ultrasound sensing.Scientific reports · 2026Article
- Imaging the brain by traversing the skull with light and sound.Nature biomedical engineering · 2025Review
- Sex dependence of opioid-mediated responses to subanesthetic ketamine in rats.Nature communications · 2024Article
- Unveiling precision: a data-driven approach to enhance photoacoustic imaging with sparse data.Biomedical optics express · 2024Article
- Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals.Life (Basel, Switzerland) · 2023Review
- Mu Opioid Receptor Activation Mediates (S)-ketamine Reinforcement in Rats: Implications for Abuse Liability.Biological psychiatry · 2023Article
- Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation.Cancers · 2022Article
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2 authors.
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Abstract
Functional ultrasound (fUS) is a rapidly emerging modality that enables whole-brain imaging of neural activity in awake and mobile rodents. To achieve sufficient blood flow sensitivity in the brain microvasculature, fUS relies on long ultrasound data acquisitions at high frame rates, posing high demands on the sampling and processing hardware. Here we develop an image reconstruction method based on deep learning that significantly reduces the amount of data necessary while retaining imaging performance. We trained convolutional neural networks to learn the power Doppler reconstruction function from sparse sequences of ultrasound data with compression factors of up to 95%. High-quality images from in vivo acquisitions in rats were used for training and performance evaluation. We demonstrate that time series of power Doppler images can be reconstructed with sufficient accuracy to detect the small changes in cerebral blood volume (~10%) characteristic of task-evoked cortical activation, even though the network was not formally trained to reconstruct such image series. The proposed platform may facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or in clinical scanners.
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