49 citations · 79 across the 17 of their papers we have counts for
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Learned Correction Methods for Ultrasound Computed Tomography Imaging Using Simplified Physics Models
Luke Lozenski, Hanchen Wang, Fu Li +4
Ultrasound computed tomography (USCT) is an emerging modality for breast imaging. Image reconstruction methods that incorporate accurate wave physics produce high resolution quanti…
ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction
Luke Lozenski, Refik Mert Cam, Mark D. Pagel +2
Accurate spatiotemporal image reconstruction methods are needed for a wide range of biomedical research areas but face challenges due to data incompleteness and computational burde…
Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography
Gangwon Jeong, Fu Li, Trevor M. Mitcham +3
Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accu…
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
Luke Lozenski, Hanchen Wang, Fu Li +4
Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods…
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems
Sayantan Bhadra, Umberto Villa, Mark A. Anastasio
Tomographic imaging is in general an ill-posed inverse problem. Typically, a single regularized image estimate of the sought-after object is obtained from tomographic measurements.…