Background and objective. Range uncertainty is a major concern affecting the delivery
precision in proton therapy. The Compton camera (CC)-based prompt-gamma (PG) imaging …

Herein, we propose a novel dataset distillation method for constructing small informative
datasets that preserve the information of the large original datasets. The development of …

High-spatial resolution MRI produces abundant structural information, enabling highly
accurate clinical diagnosis and image-guided therapeutics. However, the acquisition of high …

Abstract Learned Primal-Dual (LPD) is a deep learning based method for composite
optimization problems that is based on unrolling/unfolding the primal-dual hybrid gradient …

Hyperthermia (HT) in combination with radio-and/or chemotherapy has become an accepted
cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously …

Computed tomography (CT) reconstruction faces difficulties in dealing with artifacts caused
by imperfect imaging processes. Deep learning-based CT reconstruction models have been …

X-ray tomography is a powerful volumetric imaging technique, but detailed 3-D imaging
requires the acquisition of a large number of individual X-ray images, which is time …

Sparse-view computed tomography (CT) significantly reduces radiation doses to the human
body, whereas its analytical reconstruction exhibits severe streak artifacts. Recently, deep …

Aiming at the problems of the complex background of colorectal cancer tissue cell images
and the difficulty of detection caused by the low differentiation of cancer cell regions, a deep …

In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be
applicable to cone beam projections and perform reconstructions of entire volumes instead …