Paper 13412-32
Paper 13412-32
Mask-enhanced deep-learning for prostate ultrasound tomography with narrow data acquisition aperture
20 February 2025 • 11:10 AM - 11:30 AM PST | Palm 2
Abstract
Prostate cancer is a leading cause of cancer-related deaths. Traditional ultrasound methods struggle with sensitivity and specificity due to narrow data acquisition apertures. This study uses a convolutional neural network (CNN) to enhance prostate imaging. Validated with synthetic phantoms and finite difference simulations, our CNN-based approach surpasses traditional full-waveform inversion (FWI) in accuracy and efficiency, swiftly reconstructing high-resolution speed of sound (SOS) maps. This method promises significant improvements in prostate cancer diagnosis and treatment planning, reducing the need for invasive procedures and enabling better clinical decisions.
Presenter
Hanchen Wang
Los Alamos National Lab. (United States)
Hanchen Wang received the B.S. degree in geo- physics from Tongji University, Shanghai, China, and the M.S. and Ph.D. degrees in geophysics from the King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, in 2016 and 2021, respectively. In 2022, he joined the Los Alamos National Laboratory, Los Alamos, NM, USA, as a Post- Doctoral Research Scholar. His research interests include wave simulation, computational imaging and inverse problems using physics-based and machine-learning-based methods, including full waveform inversion, ultrasound medical imaging, seismic imaging, data-driven seismic inversion, and physics-informed machine learning.