An unsupervised U-Net trained on spot-count representations preselects energy layers for proton arc therapy, reducing energy switch time and improving nominal dosimetry versus SPArc_ps.
Medical physics 37(1), 154–163 (2010)
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Unsupervised deep learning model for fast energy layer pre-selection of delivery-efficient proton arc therapy plan optimization of nasopharyngeal carcinoma
An unsupervised U-Net trained on spot-count representations preselects energy layers for proton arc therapy, reducing energy switch time and improving nominal dosimetry versus SPArc_ps.