IEEE Transactions on Medical ImagingBioengineeringPeer-reviewed journal article
Microbubble Track-based Functional Ultrasound Localization Microscopy in Awake Mice
Yike WangiD, Matthew R. LowerisoniD, Zhe HuangiD, YiRang ShiniD, Bing‐Ze LiniD, Pengfei SongiD
- Citations
- 2
- FWCI
- 1.71
- Field percentile
- top 24%
Abstract
Functional neuroimaging with ultrafast ultrasound is an emerging neuroimaging tool for studying neural activities in the rodent brain. Existing methods, however, are challenged by the compromise between functional imaging sensitivity (i.e., sensitivity in detecting neural responses) and spatial resolution. For example, functional ultrasound (fUS) uses native red blood cells (RBCs) as imaging targets, which offers high functional imaging sensitivity but limited spatial resolution that is confined by the diffraction limit of ultrasound. On the other hand, functional ultrasound localization microscopy (fULM) employs intravenously injected microbubble (MB) as contrast agent to achieve super-resolved spatial resolution but at the cost of functional imaging sensitivity. This study aims to address this challenge by developing a novel, MB track-based hemodynamic activity estimation method to enhance the functional imaging sensitivity of fULM. Our approach involves conducting functional correlation analysis using the MB signals acquired from the entire MB movement track rather than individual MB centroid locations, which overcomes the signal sparsity issue in fULM. To further boost the functional sensitivity of fULM, we developed a novel approach based on indwelling jugular vein catheters to achieve fULM imaging in awake mice. The in vivo imaging results demonstrate that the proposed techniques successfully enhanced the functional imaging sensitivity of fULM without compromising its high spatial resolution. In the whisker stimulation experiments, the proposed technique enabled detection of significantly activated brain regions within fewer than five stimulation cycles (5 minutes of acquisition), reducing the required time by over 50% compared to conventional fULM.
- organism: mouse
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- approach: deep learning
- approach: experimental assay
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