ArticleIEEE transactions on radiation and plasma medical sciences2024
Deep learning-based Fast Volumetric Image Generation for Image-guided Proton Radiotherapy.
Article in IEEE transactions on radiation and plasma medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Patient outcome prognosis for external beam radiation therapy using CBCT-based radiomics: a systematic review.Biomedical physics & engineering express · 2026Pooled it
- Anatomically informed deep learning framework for generating fast, low-dose synthetic CBCT for prostate radiotherapy.Scientific reports · 2025Article
- Real-time volumetric CBCT reconstruction using surface and X-ray imaging for image-guided radiotherapy.Medical image analysis · 2025Article
- An open-source deep learning framework for respiratory motion monitoring and volumetric imaging during radiation therapy.Medical physics · 2025Article
- Patient-Specific CBCT Synthesis for Real-time Tumor Tracking in Surface-guided Radiotherapy.ArXiv · 2024Article
- A retrospective study on the investigation of potential dosimetric benefits of online adaptive proton therapy for head and neck cancer.Journal of applied clinical medical physics · 2024Article
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Abstract
Very fast imaging techniques can enhance the precision of image-guided radiation therapy, which can be useful for external beam radiation therapy. This work aims to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for treating lung cancer patients with gating, and it is presented in the context of FLASH which leverages ultra-high dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. The proposed framework comprises four modules, including orthogonal kV x-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and proton water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a four-dimensional computed tomography (CT) dataset with ten respiratory phases. Considering all evaluation metrics, the kV projections with source angles of 135° and 225° yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were 75±22 HU, 19±3.7 dB, 0.938±0.044, and -1.3%±4.1%. The proposed framework can rapidly deliver volumetric images to potentially guide proton FLASH treatment delivery systems.
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