15 citations · 28 across the 8 of their papers we have counts for
11 papers
Guidance-Coupled Nullspace Drifting for One-Step Medical Image Reconstruction
M. Tuna Alatan, Agah Server Tapan, Şaban Öztürk +4
Generative reconstruction can recover high-quality images from undersampled acquisitions, but state-of-the-art generative priors commonly rely on iterative sampling. Reducing the n…
PEEL-DDPM: Physics-Enabled Evidential Learning for the Denoising Diffusion Probabilistic Model
Ge Wang
Normal-inverse-gamma (NIG) regression is not identifiable from its marginal Student-t likelihood: three combinations of four NIG parameters are determined, leaving one degree of fr…
PEEL: Physics-Enabled Evidential Learning for Identifiable Uncertainty in CT Imaging
Ge Wang
Normal-inverse-gamma (NIG) regression is not uniquely identifiable from its marginal Student-t likelihood: the likelihood determines three combinations of four NIG parameters and i…
Noise suppression in photon-counting CT using unsupervised Poisson flow generative models
Dennis Hein, Staffan Holmin, Timothy Szczykutowicz +4
Deep learning has proven to be important for CT image denoising. However, such models are usually trained under supervision, requiring paired data that may be difficult to obtain i…
X-ray Dissectography Enables Stereotography to Improve Diagnostic Performance
Chuang Niu, Ge Wang
X-ray imaging is the most popular medical imaging technology. While x-ray radiography is rather cost-effective, tissue structures are superimposed along the x-ray paths. On the oth…
Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography
Hongming Shan, Rodrigo de Barros Vimieiro, Lucas Rodrigues Borges +2
Digital mammography is still the most common imaging tool for breast cancer screening. Although the benefits of using digital mammography for cancer screening outweigh the risks as…
Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach
Hongming Shan, Christopher Wiedeman, Ge Wang +1
Photoacoustic tomography seeks to reconstruct an acoustic initial pressure distribution from the measurement of the ultrasound waveforms. Conventional methods assume a-prior knowle…
A Synergized Pulsing-Imaging Network (SPIN)
Qing Lyu, Tao Xu, Hongming Shan +1
Currently, the deep neural network is the mainstream for machine learning, and being actively developed for biomedical imaging applications with an increasing emphasis on tomograph…
X-optogenetics and U-optogenetics: Feasibility and Possibilities
Rachel Berry, Matthew Getzin, Lars Gjesteby +1
To address these limitations of optogenetics, here we propose two new methods for optogenetic stimulation. The first is x-optogenetics, which uses visible light-emitting nanophosph…
Physical Foundation for General Interior Tomography
Ge Wang
Gauge invariability guarantees the same form of the Maxwell equations in different coordinate systems, and is instrumental for electromagnetic cloaking to hide a region of interest…
First CT-MRI Scanner for Multi-dimensional Synchrony and Multi-physical Coupling
Ge Wang
We propose to prototype the first CT-MRI scanner for radiation therapy and basic research, demonstrate its transformative biomedical potential, and initiate a paradigm shift in mul…