https://www.selleckchem.com/pr....oducts/LY2228820.htm
The neural network trained with in vitro datasets followed by fine-tuning with in vivo datasets had the highest accuracy at 88.0%. The SR-US images produced with deep learning allowed visualization of vessels as small as 25 μm in diameter, which is below the diffraction limit (wavelength of 110 lm at 14 MHz). The performance of the 3DCNN was encouraging for real-time SR-US imaging with an average processing frame rate for in vivo data of 51 Hz with GPU acceleration.We report the time kinetics of fluorescently labelled microbubbles in
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