Deep Learning Models for Fast Ultrasound Localization Microscopy

Abstract

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In vivo whole brain microvascular imaging in mice using transcranial 3D Ultrasound Localization Microscopy

“…Recently, deep learning based algorithms showed a great potential, shortening the MB localization processing time and performances. 39 , 40 As it helps to distinguish overlapping PSFs centroids, higher MB concentrations could be handled, thus shortening the time of acquisition. Second, the current spatial resolution, estimated at 20 × 20 × 20 μm 3 could be further improved by using matrix arrays at higher central frequency but at the expense of a lower Signal to Noise Ratio (SNR) because of the attenuation and ultrasonic wave aberrations due to the skull bone.…”

Evaluating a 3D Ultrasound Imaging Resolution of Single Transmitter/Receiver with Coding Mask by Extracting Phase Information

“…At present, it is performed by using an array system, which requires a number of sensors to be properly arranged and applies a general beamforming method. For accurate diagnosis, it should have a high resolution and high signal-to-noise ratio (SNR), and therefore researchers have intensively studied many methods to obtain a better image quality [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ].…”

Evaluating a 3D Ultrasound Imaging Resolution of Single Transmitter/Receiver with Coding Mask by Extracting Phase Information

“…At present, it is carried out by using an array system, which requires a number of sensors arranged properly and applies a general beamforming method. For accurate diagnosis, it should have high resolution and high signal to noise ratio (SNR), and therefore researchers have intensively studied many methods to have a better image quality [1][2][3][4][5][6][7][8][9][10].…”