3 papers
physics.med-ph2023
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…
eess.IV2019
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…
physics.med-ph2018
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…