A system and method of using machine learning to predict the pharmacokinetics of a therapeutic radiopharmaceutical on a subject patient using the biodistribution data of the patient in order to dynamically treat the patient using the radiopharmaceutical.

A radiation monitoring system includes a patient support apparatus. A radiation sensor assembly is operably coupled to the patient support apparatus. The radiation sensor assembly includes a radiation sensor and a first controller. The radiation sensor senses radiation data corresponding to a radiation dose received by a patient. A management system includes a second controller that stores a patient profile database. The second controller is communicatively coupled with the first controller. The first controller communicates the radiation data to the second controller for storage in the patient profile database to monitor the radiation dose received by the patient.

An x-ray imaging apparatus and associated methods are provided to receive measured projection data in a primary region and measured scatter data in asymmetrical shadow regions and determine an estimated scatter in the primary region based on the measured scatter data in the shadow region(s). The asymmetric shadow regions can be controlled by adjusting the position of the beam aperture center on the readout area of the detector. Penumbra data may also be used to estimate scatter in the primary region.

The current invention concerns a single-use patch for appliance on a skin of a subject, comprising at least two and preferably three hollow sheaths suitable for temporarily housing a fiber optic, wherein said sheaths comprise two open ends, wherein said sheaths are positioned to form a therapeutic surface within said patch by providing a fixed end of the fiber optic ends; wherein said patch is preferably combined with a topical cover layer and an adhesive supporting layer suitable to be attached to the skin of a subject.

A radiation therapy device includes an electron beam source (EBS) for generating an electron beam and a steering device for directing the electron beam. A target is disposed a predetermined distance from the EBS and is positioned to intercept the electron beam. The target element generates x-ray photons upon the impact of electrons with the target. A focusing lens is coupled to and spaced from the target by no more than 10 mm, and is positioned to receive x-ray photons generated by the target. The focusing lens focuses the x-ray photons to a focal point. The radiation therapy device can also include targets configured to generate x-ray beams for tomosynthesis. A method for performing radiation therapy is also disclosed.

Measuring and tracking energy emitted by a radiation source. A system includes an image sensor for sensing electromagnetic radiation and a scintillator. The scintillator absorbs energy emitted by a radiation source and scintillates the absorbed energy. The system is such that the image sensor senses an image frame depicting at least a portion of the scintillator when the radiation source emits the energy. The image frame comprises an indication of where the energy is absorbed by the scintillator.

In a particle beam irradiation system, upon receipt of a signal to stop irradiation of a charged particle beam, the signal outputted from a scanning controller, an accelerator and transport system controller stops emission of the charged particle beam from a charged particle beam generation unit to the irradiation unit, the scanning controller determines, according to an irradiation dose of the charged particle beam at one of a plurality of spots that has been irradiated with the charged particle beam until immediately before the accelerator and transport system controller stops the emission, the irradiation dose measured by the irradiation dose monitor from when the signal to stop the irradiation is outputted, whether or not to skip the irradiation of the charged particle beam at another one of the plurality of spots subsequent to the one of the plurality of spots, so as to control the accelerator and transport system controller.

A radiation treatment session is initiated to deliver a therapeutic radiation beam from a therapeutic radiation source to a target. One or more X-ray radiation sources are caused to deliver an imaging radiation beam from the one or more X-ray radiation sources through the target to one or more X-ray detectors to acquire imaging data associated with the target during therapeutic radiation beam delivery. One or more volumetric images are constructed using the acquired imaging data.

A method of monitoring a radiation dose includes impinging an electrode with radiation and measuring a current through the electrode. Emission of secondary electrons emitted from the electrode provides a majority of the current.

By using the Al module, the method of the present invention calculates, i.e. predicts, the dependency C.sub.i (p.sub.i) of a radiotherapy (RT) quality criterion C, from an adjustment of such a radiotherapy planning parameter p.sub.i. In this way, the decision making process in RT treatment plan optimization is streamlined by prediction of promising settings of one or more radiotherapy planning parameters p, before the actual time intensive iterative optimization process is carried out. This is achieved by applying an Al module, which has been trained to predict the specific behaviour of the dose optimization algorithm, i.e. the optimizer, with respect to geometric patient data, dose prescription and treatment indication data. Thus, a computer-implemented medical method of predicting a dependency C.sub.i (p.sub.i) of a radiotherapy (RT) quality criterion C.sub.i from an adjustment of a radiotherapy planning parameter p, is presented. The method comprises the following steps of providing geometric patient data geometrically describing an area of a patient, which is to be irradiated according to a radiotherapy treatment plan (step S1), providing dose prescription data and treatment indication data for said patient (step S2), and predicting with a trained Artificial Intelligence (Al) module the dependency C.sub.i (p.sub.i) of the radiotherapy quality criterion C.sub.i from the radiotherapy planning parameter p, when adjusting said radiotherapy planning parameter p.sub.i, thereby using the geometric patient data, the dose prescription data and the treatment indication data as input for the Al module (step S3).