Four-dimensional computational ultrasound imaging of brain hemodynamics
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http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.w0vt4b8z8
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资源简介:
Four-dimensional ultrasound imaging of complex biological systems such as the brain is technically challenging because of the spatiotemporal sampling requirements. We present computational ultrasound imaging (cUSi), an imaging method that uses complex ultrasound fields that can be generated with simple hardware and a physical wave prediction model to alleviate the sampling constraints. cUSi allows for high-resolution four-dimensional imaging of brain haemodynamics in awake and anesthetized mice.
Methods
In this study, we assessed cUSi via in-vivo imaging experiments in both anesthetized (n=1) and awake (n=1) adult C57BL6/J mice (8–10 weeks old). The national authority (Centrale Commissie Dierproeven, The Hague, The Netherlands; license: AVD1010020197846) granted ethical approval prior to the experiments, which were performed according to the institutional, national, and European Union guidelines. Data from both mice was collected using a custom-built 64-element matrix probe with an output modified by a plastic encoding mask. The probe was driven using a Hadamard encoded synthetic aperture transmission scheme transmitting continuously for 60 seconds at a rate of 32 kHz to acquire two data ensembles.
This upload contains the reconstructed Power and Colour Doppler Volumes from the data ensembles of both the awake and anesthetized mice. These were formed by spatio-temporally filtering using a singular value decomposition the 60-second acquisitions to remove signal components originating from soft-tissue. Next individual B-mode volumes were reconstructed by coherently compounding sets of 64 sequential transmissions from the filtered dataset with an 8 transmission lag. The reconstruction was performed with a model-based approach using a calibrated acoustic model of the transmitted matrix field. Finally, the Colour and Power Doppler volumes were generated by evaluating the lag-1 autocorrelation or the average power respectively for each voxel.
创建时间:
2024-01-17



