Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning

Abstract

Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles in the blood vessels, ultimately resulting in long acquisition times. Here, we present an alternative super-resolution approach, based on direct deconvolution of single-channel ultrasound radio-frequency (RF) signals with a one-dimensional dilated convolutional neural network (CNN). This work focuses on low-frequency ultrasound (1.7 MHz) for deep imaging (10 cm) of a dense cloud of monodisperse microbubbles (up to 1000 microbubbles in the measurement volume, corresponding to an average echo overlap of 94%). Data are generated with a simulator that uses a large range of acoustic pressures (5-250 kPa) and captures the full, nonlinear response of resonant, lipid-coated microbubbles. The network is trained with a novel dual-loss function, which features elements of both a classification loss and a regression loss and improves the detection-localization characteristics of the output. Whereas imposing a localization tolerance of 0 yields poor detection metrics, imposing a localization tolerance corresponding to 4% of the wavelength yields a precision and recall of both 0.90. Furthermore, the detection improves with increasing acoustic pressure and deteriorates with increasing microbubble density. The potential of the presented approach to super-resolution ultrasound imaging is demonstrated with a delay-and-sum reconstruction with deconvolved element data. The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data.

Super resolution ultrasound imaging Using deep learning based micro-bubbles localization

“…First we would like to point out unlike methods shown in [12], training data generated by simulations were not employed in our experiments for following consideration. It seems that oscillations of MBs are not considered in above mentioned paper and essentially, MBs and normal scatters suffer different underlying physical disciplines when insonified by ultrasound beam [13]. Therefore, we believe accurate simulations with oscillations of MBs needs to be investigated in our future work however it is beyond the scope of this manuscript.…”

Retrieving Pulsatility in Ultrasound Localization Microscopy

“…Each subpopulation was then individually processed by conventional ULM processing steps. Alternatively, machine learning-based approaches for MB localization were shown to be able to handle higher MB concentrations by disentangling the interference pattern of spatially overlapping MBs [20]- [22]. ULM can also be triggered with electrocardiograms to observe phenomenons independently from the cardiac cycle such as pulsatility [23], or avoid big motion [24].…”

Lyophilized Microfluidic Monodisperse Microbubbles as an Ultrasound Contrast Agent

“…where 𝐽 is the effective permeability, and 𝑟 21 is a critical radius value. Equation (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) basically shows that bubbles with a radius greater 𝑟 21 will grow and bubbles with 𝑟 < 𝑟 21 will shrink.…”

“…Equations (5)(6)(7)(8)(9)(10)(11)(12)(13)(14) and (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) are solved to obtain a solution for the bubble surface motion at:…”

“…Introducing Eq. (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) into the Eulers equations (Eq. 5-14) one obtains:…”