DOSEVOLUME HISTOGRAM AND DOSE DISTRIBUTION BASED AUTOPLANNING

    公开(公告)号:US20200075151A1

    公开(公告)日:2020-03-05

    申请号:US16558698

    申请日:2019-09-03

    Abstract: A method and system for generating a voxel-based quadratic penalty model for automatic intensity modulated radiation therapy (IMRT) treatment planning are disclosed herein. A computing system generates an initial assignment of threshold values to a penalty function for IMRT treatment planning The computing system receives an update to a dose value associated with the IMRT treatment planning The computing system dynamically updates the threshold values based on the updated dose value. The computing system continues to iterate the threshold values based on further updated dose values.

    RADIATION THERAPY SYSTEMS THAT INCLUDE PRIMARY RADIATION SHIELDING, AND MODULAR SECONDARY RADIATION SHIELDS

    公开(公告)号:US20190365337A1

    公开(公告)日:2019-12-05

    申请号:US16443419

    申请日:2019-06-17

    Abstract: Radiation therapy systems and their components, including secondary radiation shields. At least some versions of the disclosed systems combine a radiation delivery device, a primary radiation shielding device, and a secondary shielding layer into an integrated, modular unit. This is accomplished by using a small direct beam shield capable of blocking a primary beam from a radiation delivery device. In turn, a thinner shielding layer can be used to surround the radiation delivery device and primary shielding device, enabling a single modular unit to be delivered to an installation site. In some embodiments, a bed may be disposed within the secondary shielding layer. In some embodiments, the system is configured to provide up to 4-pi (4π) steradians of radiation coverage to the bed from the radiation delivery device.

    Deep learning based dosed prediction for treatment planning and quality assurance in radiation therapy

    公开(公告)号:US11615873B2

    公开(公告)日:2023-03-28

    申请号:US16558681

    申请日:2019-09-03

    Abstract: A method and system for generating a treatment plan are disclosed herein. A computing system receives a plurality of dose volume histograms for a plurality of patients and a plurality of volumetric dose distributions corresponding to the plurality of dose volume histograms. The computing system generates a volumetric dose prediction model using a neural network by learning, by the neural network, a relationship between a plurality of dose volume histograms for the plurality of patients and the corresponding plurality of volumetric dose distributions. The computing system receives a candidate dose volume histogram for a target patient. The computing system infers, via the volumetric dose prediction module, a volumetric dose prediction distribution matching the candidate dose volume histogram. The computing system generates a recommendation based on the inferred volumetric dose prediction distribution.

    DEEP LEARNING BASED DOSED PREDICTION FOR TREATMENT PLANNING AND QUALITY ASSURANCE IN RADIATION THERAPY

    公开(公告)号:US20200075148A1

    公开(公告)日:2020-03-05

    申请号:US16558681

    申请日:2019-09-03

    Abstract: A method and system for generating a treatment plan are disclosed herein. A computing system receives a plurality of dose volume histograms for a plurality of patients and a plurality of volumetric dose distributions corresponding to the plurality of dose volume histograms. The computing system generates a volumetric dose prediction model using a neural network by learning, by the neural network, a relationship between a plurality of dose volume histograms for the plurality of patients and the corresponding plurality of volumetric dose distributions. The computing system receives a candidate dose volume histogram for a target patient. The computing system infers, via the volumetric dose prediction module, a volumetric dose prediction distribution matching the candidate dose volume histogram. The computing system generates a recommendation based on the inferred volumetric dose prediction distribution.

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