Stabilized adaptive waveform inversion for enhanced robustness in Gaussian penalty matrix parameterization and transcranial ultrasound imaging

Abstract Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases, preoperative planning of craniotomies and intraoperative management during craniotomy procedures. Adaptive waveform inversion (AWI), a variant of full waveform inversion (FWI), has shown potential in intracranial ultrasound imaging. However, the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios. Conventional AWI struggles to produce accurate images in these cases, limiting its application in critical medical settings. To address these issues, we propose a stabilized adaptive waveform inversion (SAWI) method, which introduces a user-defined zero-lag position for the Wiener filter. Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails, perform successful transcranial imaging in configurations where AWI cannot, and maintain the same imaging accuracy as AWI. The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs, which helps to promote the application of AWI in medical fields, particularly in transcranial scenarios.