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1.发明授权公开(公告)号：US11931598B2
公开(公告)日：2024-03-19
申请号：US17212324
申请日：2021-03-25
发明人： Elena Czeizler , Esa Kuusela , Mikko Hakala , Shahab Basiri
CPC分类号： A61N5/103 , G06T7/11 , G06T2207/20081 , G06T2207/20084 , G06T2207/30096
摘要： Image information regarding a particular patient is provided, which image information includes, at least in part, a tumor to be irradiated. These teachings can also include providing non-image clinical information that corresponds to the particular patient. A control circuit accesses the foregoing image information and non-image clinical information and automatically generates a clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information. The control circuit can then generate a corresponding radiation treatment plan based upon that clinical target volume, which plan can be utilized to irradiate the clinical target volume.
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公开(公告)号：US20230095485A1
公开(公告)日：2023-03-30
申请号：US17485794
申请日：2021-09-27
发明人： Elena Czeizler , Mikko Hakala , Shahab Basiri , Hannu Laaksonen , Maria Isabel Cordero Marcos , Christopher Boylan , Jarkko Y. Peltola , Ville Pietilä , Esa Kuusela
摘要： A radiation treatment plan three-dimensional dose prediction machine learning model is trained using a training corpus that includes a plurality of radiation treatment plans that are not specific to a particular patient and wherein the training corpus includes some, but not all, possible patient volumes of interest. Information regarding the patient (including information regarding at least one volume of interest for the patient that was not represented in the training corpus) is input to the radiation treatment plan three-dimensional dose prediction machine model. The latter generates predicted three-dimensional dose distributions that include a predicted three-dimensional dose distribution for the at least one volume of interest that was not represented in the training corpus.
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公开(公告)号：US20220415472A1
公开(公告)日：2022-12-29
申请号：US17361085
申请日：2021-06-28
发明人： Mikko Hakala , Esa Kuusela , Elena Czeizler , Shahab Basiri
摘要： Embodiments described herein provide for recommending radiotherapy treatment attributes. A machine learning model predicts the preference of a medical professional and provides relevant suggestions (or recommendations) of radiotherapy treatment attributes for various categories of radiotherapy treatment. Specifically, the machine learning model predicts field geometry attributes from various field geometry attribute options for various field geometry attribute categories. The machine learning model is conditioned on patient data such as medical images and patient information. The machine learning model is trained in response to cumulative reward information associated with a medical professional accepting the provided/displayed recommendations.
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4.发明申请公开(公告)号：US20220409929A1
公开(公告)日：2022-12-29
申请号：US17361817
申请日：2021-06-29
摘要： A memory has a fluence map that corresponds to a particular patient stored therein. This memory also has at least one deep learning model stored therein trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. A control circuit operably coupled to that memory iteratively optimizes a radiation treatment plan to administer therapeutic radiation to that patient by, at least in part, generating a leaf sequence as a function of the at least one deep learning model and the fluence map that corresponds to the patient.
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公开(公告)号：US20220088418A1
公开(公告)日：2022-03-24
申请号：US17031063
申请日：2020-09-24
发明人： Esa Kuusela , Mikko Hakala , Shahab Basiri , Elena Czeizler
IPC分类号： A61N5/10
摘要： These teachings serve to facilitate radiating a treatment target in a patient during a radiation treatment session with a radiation treatment platform having a moving source of radiation and using an optimized radiation treatment plan. These teachings in particular provide for configuring the radiation treatment platform in a half-fan trajectory arrangement. These teachings then provide for beginning the radiation treatment session with the source of radiation in a first location and an isocenter for the treatment target in a first position. Then, during the radiation treatment session, these teachings provide for moving the source of radiation from that first location in synchronization with moving the isocenter from the aforementioned first position.
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公开(公告)号：US11612761B2
公开(公告)日：2023-03-28
申请号：US17124249
申请日：2020-12-16
发明人： Shahab Basiri , Mikko Hakala , Esa Kuusela , Elena Czeizler
摘要： Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques. With each iteration, a training agent applies a random radiation therapy treatment attribute corresponding to the radiation therapy treatment attribute associated with previously performed radiation therapy treatments when an epsilon value indicative of a likelihood of exploration and exploitation training of the artificial intelligence model satisfies a threshold. When the epsilon value does not satisfy the threshold, the agent generates, using an existing policy, a first predicted radiation therapy treatment attribute, and generates, using a predefined model, a second predicted radiation therapy treatment attribute. The agent applies one of the first predicted radiation therapy treatment attribute or the second predicted radiation therapy treatment attribute that is associated with a higher reward. The agent iteratively repeats training the artificial intelligence model until the existing policy satisfies an accuracy threshold.
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公开(公告)号：US20210387018A1
公开(公告)日：2021-12-16
申请号：US16898608
申请日：2020-06-11
发明人： Mikko Hakala , Esa Kuusela , Elena Czeizler , Shahab Basiri
IPC分类号： A61N5/10
摘要： A control circuit access information corresponding to patient geometry information for a particular patient. The control circuit then provides that information, along with at least one variable that is unrelated to that particular patient, as input to a field geometry generator. The field geometry generator can comprise a neural network trained in a conditional generative adversarial networks (GAN) framework as a function of previously-developed field geometry solutions for a plurality of different patients. In such a case the information corresponding to the patient geometry information for the particular patient can serve as conditional input to the neural network. So configured, the control circuit can then process the foregoing input using the field geometry generator to thereby generate the therapeutic radiation delivery field geometry for the particular patient.
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公开(公告)号：US11590367B2
公开(公告)日：2023-02-28
申请号：US17124223
申请日：2020-12-16
发明人： Mikko Hakala , Esa Kuusela , Elena Czeizler , Shahab Basiri
摘要： Disclosed herein are systems and methods for identifying radiation therapy treatment data for patients. A processor accesses a neural network trained based on a first set of data generated from characteristic values of a first set of patients that received treatment at one or more first radiotherapy machines. The processor executes the neural network using a second set of data comprising characteristic values of a second set of patients receiving treatment at one or more second radiotherapy machines. The processor executes a calibration model using an output of the neural network based on the second set of data to output a calibration value. The processor executes the neural network using a set of characteristics of a first patient to output a first confidence score associated with a first treatment attribute. The processor then adjusts the first confidence score according to the calibration value to predict the first treatment attribute.
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9.发明申请公开(公告)号：US20220305285A1
公开(公告)日：2022-09-29
申请号：US17212324
申请日：2021-03-25
发明人： Elena Czeizler , Esa Kuusela , Mikko Hakala , Shahab Basiri
摘要： Image information regarding a particular patient is provided, which image information includes, at least in part, a tumor to be irradiated. These teachings can also include providing non-image clinical information that corresponds to the particular patient. A control circuit accesses the foregoing image information and non-image clinical information and automatically generates a clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information. The control circuit can then generate a corresponding radiation treatment plan based upon that clinical target volume, which plan can be utilized to irradiate the clinical target volume.
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公开(公告)号：US20210387017A1
公开(公告)日：2021-12-16
申请号：US16898643
申请日：2020-06-11
发明人： Elena Czeizler , Esa Kuusela , Mikko Hakala , Shahab Basiri
摘要： A control circuit accesses patient image content as well as field geometry information regarding a particular radiation treatment platform. The control circuit then generates a predicted three-dimensional dose map for the radiation treatment plan as a function of both the patient image content and the field geometry information.
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