Microbubble Backscattering Intensity Improves the Sensitivity of Three-dimensional (3D) Functional Ultrasound Localization Microscopy (fULM)
Microbubble Backscattering Intensity Improves the Sensitivity of Three-dimensional (3D) Functional Ultrasound Localization Microscopy (fULM)
You, Q.; Shin, Y.; Wang, Y.; Lowerison, M.; Lin, B.-Z.; Song, P.
AbstractFunctional ultrasound localization microscopy (fULM) is a new functional neuroimaging technique that maps brain-wide neural activities with a micron-scale spatial resolution. In practice, since fULM relies on measuring fluctuations in microbubble (MB) count to estimate variations in blood flow associated with neural activities, it is susceptible to various sources of noise, which hampers the functional sensitivity of fULM. This challenge exacerbates in 3D fULM where existing 3D ultrasound imaging techniques suffer from lower imaging quality than their 2D counterparts. In this paper, we systematically analyzed the functional sensitivity of fULM and its relationship with the spatial resolution of fULM, based on which we proposed a new method that combines MB backscattering intensity with MB count to improve the fidelity of fULM in vivo. The proposed method was first validated in a flow phantom study followed by an in vivo rat brain study. Our results indicate that MB backscattering intensity provides a more sensitive measure of the dynamic blood flow variations associated with neural activities. The functional imaging sensitivity in the rat brain study improved by 3.8dB in the rodent somatosensory barrel cortex (S1BF) region and 6.6dB in the ventral posteromedial thalamic nucleus (VPM) region. Fourier shell correlation analysis showed that the proposed method based on MB backscattering intensity achieved a 3D spatial resolution of 53 m, which was slightly lower than that of conventional fULM (46 m). These results suggest that with moderate compromise in spatial resolution, integrating MB backscattering intensity in fULM is beneficial for improving the robustness of fULM in functional neuroimaging.