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20232026
most citedDeep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

24 citations · 47 across the 14 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

physics.med-ph2023

Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability

Lian Zhang, Jason M. Holmes, Zhengliang Liu +11

Purpose: Prior AI-based dose prediction studies in photon and proton therapy often neglect underlying physics, limiting their generalizability to handle outlier clinical cases, esp…

physics.med-ph2023★ 2 cited

Artificial Intelligence-Facilitated Online Adaptive Proton Therapy Using Pencil Beam Scanning Proton Therapy

Hongying Feng, Jie Shan, Carlos E. Vargas +10

We propose an oAPT workflow that incorporates all these functionalities and validate its clinical implementation feasibility with prostate patients. AI-based auto-segmentation tool…

physics.med-ph2023

Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report

Jason Holmes, Lian Zhang, Yuzhen Ding +7

Purpose: To introduce the concept of using large language models (LLMs) to re-label structure names in accordance with the American Association of Physicists in Medicine (AAPM) Tas…

physics.med-ph2023

Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy

Hongying Feng, Jason M. Holmes, Sujay A. Vora +8

Purpose: To enhance an in-house graphic-processing-unit (GPU) accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both do…

eess.IV2023★ 13 cited

Segment Anything Model (SAM) for Radiation Oncology

Lian Zhang, Zhengliang Liu, Lu Zhang +10

In this study, we evaluate the performance of the Segment Anything Model (SAM) in clinical radiotherapy. Our results indicate that SAM's 'segment anything' mode can achieve clinica…

cs.CV2023★ 24 cited

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

Yuzhen Ding, Hongying Feng, Yunze Yang +9

Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The v…