325 citations · 606 across the 16 of their papers we have counts for
16 papers
Interactive Treatment Planning in High Dose-Rate Brachytherapy for Gynecological Cancer
Huan Liu, Chang M Ma, Xun Jia +3
High dose-rate brachytherapy (HDRBT) is widely used for gynecological cancer treatment. Although commercial treatment planning systems (TPSs) have inverse optimization modules, it…
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning
Arjun Krishna, Kedar Bartake, Chuang Niu +4
Deep learning has shown great promise for CT image reconstruction, in particular to enable low dose imaging and integrated diagnostics. These merits, however, stand at great odds w…
Noise Entangled GAN For Low-Dose CT Simulation
Chuang Niu, Ge Wang, Pingkun Yan +8
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image. First, we present two schemes to generate a clean CT…
A Feasibility Study on Deep Learning Based Individualized 3D Dose Distribution Prediction
Jianhui Ma, Dan Nguyen, Ti Bai +5
Purpose: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An optimal dose distribution based on a specific anatomy can be predicted by pre-t…
Experimental and numerical studies on kV scattered x-ray imaging for real-time image guidance in radiation therapy
Yanqi Huang, Kai Yang, Youfang Lai +7
Motion management is a critical component of image guidance radiotherapy for lung cancer. We previously proposed a scheme using kV scattered x-ray photons for marker-less real-time…
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy
Dan Nguyen, Rafe McBeth, Azar Sadeghnejad Barkousaraie +4
We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of dee…