activity
20192022
most citedA Method of Rapid Quantification of Patient-Specific Organ Dose for CT Using Coupled Deep-Learning based Multi-Organ Segmentation and GPU-accelerated Monte Carlo Dose Computing

82 citations · 115 across the 4 of their papers we have counts for

collaborators

4 papers

physics.med-ph2022★ 1 cited

OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

Aaron Babier, Rafid Mahmood, Binghao Zhang +56

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients w…

physics.med-ph2021★ 20 cited

Development of a GPU-accelerated Monte Carlo dose calculation module for nuclear medicine, ARCHER-NM: Demonstration for a PET/CT imaging procedure

Zhao Peng, Yu Lu, Yao Xu +8

This paper describes the development and validation of a Monte Carlo (MC) dose computing module dedicated to organ dose calculations of patients undergoing nuclear medicine (NM) in…

physics.med-ph2019★ 12 cited

Deep learning for accelerating Monte Carlo radiation transport simulation in intensity-modulated radiation therapy

Zhao Peng, Hongming Shan, Tianyu Liu +4

Cancer is a primary cause of morbidity and mortality worldwide. The radiotherapy plays a more and more important role in cancer treatment. In the radiotherapy, the dose distributio…

physics.med-ph2019★ 82 cited

A Method of Rapid Quantification of Patient-Specific Organ Dose for CT Using Coupled Deep-Learning based Multi-Organ Segmentation and GPU-accelerated Monte Carlo Dose Computing

Zhao Peng, Xi Fang, Pingkun Yan +7

Purpose: This paper describes a new method to apply deep-learning algorithms for automatic segmentation of radiosensitive organs from 3D tomographic CT images before computing orga…