Neural Born Series Operator for Biomedical Ultrasound Computed Tomography
This work addresses a computational limitation that hinders clinical adoption of USCT, making radiation-free high-resolution imaging more viable, though it appears incremental as it applies neural operators to an existing bottleneck.
The paper tackles the computational bottleneck of Full Waveform Inversion (FWI) in Ultrasound Computed Tomography (USCT) by introducing the Neural Born Series Operator (NBSO) to speed up wave simulations, enabling more efficient image reconstruction with validation on brain and breast datasets under experimental conditions.
Ultrasound Computed Tomography (USCT) provides a radiation-free option for high-resolution clinical imaging. Despite its potential, the computationally intensive Full Waveform Inversion (FWI) required for tissue property reconstruction limits its clinical utility. This paper introduces the Neural Born Series Operator (NBSO), a novel technique designed to speed up wave simulations, thereby facilitating a more efficient USCT image reconstruction process through an NBSO-based FWI pipeline. Thoroughly validated on comprehensive brain and breast datasets, simulated under experimental USCT conditions, the NBSO proves to be accurate and efficient in both forward simulation and image reconstruction. This advancement demonstrates the potential of neural operators in facilitating near real-time USCT reconstruction, making the clinical application of USCT increasingly viable and promising.