A radiation system is provided. The radiation system may include a bore accommodating an object, a rotary ring, a first radiation source and a second radiation source mounted on the rotary ring and a processor. The first radiation source may be configured to emit a first cone beam toward a first region of the object. The second radiation source may be configured to emit a second beam toward a second region of the object, the second region including at least a part of the first region. The processor may be configured to obtain a treatment plan of the object, the treatment plan including parameters associated with radiation segments. The processor may be further configured to control an emission of the first cone beam and/or the second beam based on the parameters associated with the radiation segments to perform a treatment and a 3-D imaging simultaneously.

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.

A system for dynamic localization of medical instruments includes an ultrasound imaging system (110) configured to image a volume where one or more medical instruments are deployed. A registration module (136) registers two images of the one or more medical instruments to compute a transform between the two images, the two images being separated in time. A planning module (142) is configured to have positions of the volume and the one or more medical instruments updated based on the transform and, in turn, update a treatment plan in accordance with the updated positions such that changes in the volume and positions of the one or more medical instruments are accounted for in the updated plan.

The present disclosure relates to systems and methods for pulse parameter modulation. The systems may perform the methods to obtain information related to a treatment plan; determine a backward window based on the information related to the treatment plan, within which one or more radiation pulses have been transmitted; determine backward information associated with the backward window based on the information related to the treatment plan and the backward window; determine a forward window based on the information related to the treatment plan, within which one or more radiation pulses are to be transmitted; determine forward information associated with the forward window based on the information related to the treatment plan, the backward information, and the forward window; and determine one or more pulse parameters of the forward window based on the forward information.

The present disclosure relates to systems and methods for radiotherapy planning. The systems may obtain a delineation of a region of interest (ROI) in an image of an object. The ROI may include at least one target region. The systems may obtain modified delineation of the ROI based on one or more modifications to the delineation of the ROI. The systems may determine a target radiotherapy plan of the object by performing a radiotherapy dose optimization on the ROI. The modifications of the delineation of the ROI and the radiotherapy dose optimization of the ROI may be performed at least partially overlap temporally.

A resource management system for better operation of a plurality of devices. The system comprises an input interface (IN) for receiving input data including one or more characteristics of at least one work object (P1-P3) and/or including context data. The at least one work object (P1-P3) can be processed by one or more processing devices (M.sub.ij). The said processing is specified in a respective work specification (S1-S3). A predictor component (PC) of the system is configured to predict, based on the input data, a change to the work specification. The system comprises an output interface (OUT) for providing output data that represents said predicted change.

Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.

The present invention relates to radiation therapy. In order to improve the accuracy in deformable image registration in radiation therapy planning, a method is provided that comprises a) receiving a dose distribution to be delivered during one or more treatment sessions according to a radiation therapy plan, b) receiving contours delineating at least one region of interest, ROI, of at least one of the medical images, c) receiving one or more treatment objectives associated with the at least one ROI that has delineated contours, d) determining one or more critical regions in at least one of the medical images, where a geometric error of the contours of the at least one ROI and/or an uncertainty in the electron density distribution leads to a violation of one or more treatment objectives with respect to the dose distribution, and e) improving the accuracy of a deformable image registration algorithm in the one or more critical regions for registering the at least two medical images, wherein the deformable image registration algorithm estimates a deformation between the at least two medical images.

Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

The present disclosure relates to systems and methods for pulse parameter modulation. The systems may perform the methods to obtain information related to a treatment plan; determine a backward window based on the information related to the treatment plan, within which one or more radiation pulses have been transmitted; determine backward information associated with the backward window based on the information related to the treatment plan and the backward window; determine a forward window based on the information related to the treatment plan, within which one or more radiation pulses are to be transmitted; determine forward information associated with the forward window based on the information related to the treatment plan, the backward information, and the forward window; and determine one or more pulse parameters of the forward window based on the forward information.