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Quantitative ultrasound brain imaging with multiscale deconvolutional waveform inversion


 High-resolution images of human brain are critical for monitoring the neurological conditions in a portable and safe manner. Sound speed mapping of brain tissues provides unique information for such purpose. In addition, it is particularly important for building digital human acoustic models, which form a reference for future ultrasound research. Conventional ultrasound modalities can hardly image the human brain at high spatial resolution inside the skull, due to the strong impedance contrast between hard and soft tissues. We carry out numerical experiments to illustrate that the time-domain waveform inversion technique, originating from the geophysics community, is promising to deliver quantitative images of human brains with-in the skull at the sub-millimeter level using ultra-sound signals. The success implementation of such approach to brain imaging requires the following: signals of sub-megahertz frequencies transmitting across the inside of skull, an accurate numerical wave equation solver simulating the wave propagation, and well-designed inversion schemes to reconstruct the physical parameters of targeted model based on the optimization theory. Here we propose an innovative modality of multiscale deconvolutional waveform inversion that improves ultrasound imaging resolution, by evaluating the similarity of synthetic and observed data using limited length wiener filter. We perform the proposed approach to iteratively update the para-metric models of the human brain. As a quantitative imaging method, it paves the way for building the accurate acoustic brain model to diagnose associated diseases, in a potentially more portable, more dynamic and safer way compared to magnetic resonance imaging and X-ray computed tomography.