Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion.

Perfusion maps obtained from low-dose computed tomography (CT) data suffer from poor signal to noise ratio. To enhance the quality of the perfusion maps, several works rely on denoising the low-dose CT (LD-CT) images followed by conventional regularized deconvolution. Recent works employ deep neural networks (DNN) for learning a direct mapping between the noisy and the clean perfusion maps ignoring the convolution-based forward model.

Effects of surgery, chemotherapy, and radiation on hepatocellular carcinoma patients: A SEER-based study.

Background: Hepatocellular carcinoma (HCC) is a major global health issue, accounting for 75%-85% of primary liver cancer cases. HCC has huge molecular heterogeneity, and the treatment varies among the patients. The aim of this study is assess the effect of surgery, chemotherapy, and radiation on the mortality risk in hepatocellular carcinoma (HCC) patients.

Towards lower-dose PET using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data.

Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging.