Abstract: The present disclosure relates to the use of machine learning for determining initial machine setting parameters for radiotherapy treatment planning. A machine-learning system is trained on data sets including a dose distribution and a set of machine parameter settings resulting from that dose distribution. The trained system can be used for determining machine parameter settings based on a desired dose distribution, which may be used as initial machine parameter settings for radiation treatment optimization.

Abstract: A method (100) for generating a treatment plan specifying an irradiation of a patient, the method comprising: a dose inference stage (112), including using a model to infer a spatial dose from patient data; a dose mimicking stage (116), including executing a robust optimization process to generate a deliverable treatment plan which is consistent with the inferred spatial dose, wherein the robust optimization considers a plurality of scenarios relating to patient data uncertainty.

Abstract: An estimated or predicted dose for radiotherapy treatment may be generated based on a partial dose map including dose information only for one or more regions of interest within a treatment site, by use of a properly trained machine learning system such as a U-Net or a V-Net. Said partial dose map typically set to fulfil clinical goals. A method of training such a machine learning system is also disclosed.

Abstract: An estimated or predicted dose for radiotherapy treatment may be generated based on a partial dose map including dose information only for one or more regions of interest within a treatment site, by use of a properly trained machine learning system such as a U-Net or a V-Net. Said partial dose map typically set to fulfil clinical goals. A method of training such a machine learning system is also disclosed.

Abstract: A method (100) for generating a treatment plan specifying an irradiation of a patient, the method comprising: a dose inference stage (112), including using a model to infer a spatial dose from patient data; a dose mimicking stage (116), including executing a robust optimization process to generate a deliverable treatment plan which is consistent with the inferred spatial dose, wherein the robust optimization considers a plurality of scenarios relating to patient data uncertainty.

Abstract: The present disclosure generally relates to the field of radiation treatment. More specifically, the present disclosure generally relates to methods and radiation treatment systems for generating a radiation treatment plan. According to one example embodiment, a method may comprise dividing at least one dose characteristic related to a dose distribution for a ROI into a plurality of subintervals. The method may further comprise partitioning the ROI into a plurality of different partitions based on the subintervals. All voxels of the ROI with values within the same subinterval are partitioned into the same partition. For each of the plurality of different partitions, the method may comprise establishing a weight and specifying an optimization function for an obtainable dose distribution based on the respective subinterval of dose characteristics. The method may further comprise generating the radiation treatment plan based on said established weights and specified optimization functions.

Abstract: The present disclosure generally relates to the field of radiation treatment. More specifically, the present disclosure generally relates to methods and radiation treatment systems for generating a radiation treatment plan. According to one example embodiment, a method may comprise dividing at least one dose characteristic related to a dose distribution for a ROI into a plurality of subintervals. The method may further comprise partitioning the ROI into a plurality of different partitions based on the subintervals. All voxels of the ROI with values within the same subinterval are partitioned into the same partition. For each of the plurality of different partitions, the method may comprise establishing a weight and specifying an optimization function for an obtainable dose distribution based on the respective subinterval of dose characteristics. The method may further comprise generating the radiation treatment plan based on said established weights and specified optimization functions.