10 citations · 12 across the 8 of their papers we have counts for
8 papers
An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM
Luke Lozenski, Michael T. McCann, Brendt Wohlberg
This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization pro…
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…
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…
Technical Note: PDE-constrained Optimization Formulation for Tumor Growth Model Calibration
Baoshan Liang, Luke Lozenski, Umberto Villa +1
We discuss solution algorithms for calibrating a tumor growth model using imaging data posed as a deterministic inverse problem. The forward model consists of a nonlinear and time-…
Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth
Baoshan Liang, Jingye Tan, Luke Lozenski +4
Reliably predicting the future spread of brain tumors using imaging data and on a subject-specific basis requires quantifying uncertainties in data, biophysical models of tumor gro…
A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields
Luke Lozenski, Mark A. Anastasio, Umberto Villa
Dynamic imaging is essential for analyzing various biological systems and behaviors but faces two main challenges: data incompleteness and computational burden. For many imaging sy…
Consensus ADMM for Inverse Problems Governed by Multiple PDE Models
Luke Lozenski, Umberto Villa
The Alternating Direction Method of Multipliers (ADMM) provides a natural way of solving inverse problems with multiple partial differential equations (PDE) forward models and nons…