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20202024
most citedLearning-Based Synthetic Dual Energy CT Imaging from Single Energy CT for Stopping Power Ratio Calculation in Proton Radiation Therapy

3 citations · 4 across the 3 of their papers we have counts for

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physics.med-ph2024

Unsupervised Bayesian Generation of Synthetic CT from CBCT Using Patient-Specific Score-Based Prior

Junbo Peng, Yuan Gao, Chih-Wei Chang +7

Background: Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (…

physics.med-ph2023

Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning

Yang Lei, Yifu Ding, Richard L. J. Qiu +6

Background: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniq…

physics.med-ph20221 cited

Deep Learning-based Protoacoustic Signal Denoising for Proton Range Verification

Jing Wang, James J. Sohn, Yang Lei +5

Objective: Proton therapy offers an advantageous dose distribution compared to the photon therapy, since it deposits most of the energy at the end of range, namely the Bragg peak (…

physics.med-ph20203 cited

Learning-Based Synthetic Dual Energy CT Imaging from Single Energy CT for Stopping Power Ratio Calculation in Proton Radiation Therapy

Serdar Charyyev, Tonghe Wang, Yang Lei +7

Purpose: Dual-energy CT (DECT) has been shown to derive stopping power ratio (SPR) map with higher accuracy than conventional single energy CT (SECT) by obtaining the energy depend…

physics.med-ph2020

Learning-Based Stopping Power Mapping on Dual Energy CT for Proton Radiation Therapy

Tonghe Wang, Yang Lei, Joseph Harms +8

Purpose: Dual-energy CT (DECT) has been used to derive relative stopping power (RSP) map by obtaining the energy dependence of photon interactions. The DECT-derived RSP maps could…