A treatment planning system (106) for generating patient-specific treatment margins. The system (106) includes one or more processors (142). The processors (142) are programmed to receive a radiation treatment plan (RTP) for irradiating a target (122) over the course of one or more treatment fractions. The RTP including one or more treatment margins around the target (122) and a planned dose distribution for the target (122). The processors (142) are further programmed to receive motion data for at least one of the treatment fractions of the RTP from one or more target surrogates (124), calculate a motion-compensated dose distribution for the target (122) using the motion data and the planned dose distribution, compare the motion-compensated dose distribution to the planned dose distribution, and adjust the treatment margins based on dosimetric differences between the motion-compensated dose distribution and the planned dose distribution.

Methods and systems are disclosed for radiating a moving object. The method may comprise acquiring a plurality of indicators of the phase of a physiological cycle of a patient and a plurality of images of the patient that include a target. Each image may be taken at a different phase of the physiological cycle and may be registered to the phase at which the image was taken. The method may also include identifying the target in each of the plurality of images, calculating a dose of radiation required to treat the target, calculating the number, orientation, and dwell time of one or more radiation beams required to deliver the calculated required dose of radiation to the target, and calculating a position of each of the one or more radiation beams required to achieve the calculated orientation. Each position may be a function of the phase of the physiological cycle to which each of the plurality of images is registered.

System, method, and computer program product to select a desired portion of a subject to receive a radiation dose, including: determining a plurality of Pareto points on a Pareto surface (PS); selecting a first reference point (p.sub.1) on a back side of an assumed Pareto surface (PS′) and first direction (q.sub.1) emerging therefrom towards PS′ from behind; selecting a first starting adjustment (x.sub.10) and iteratively developing forward a minimum criterion in steps until a final adjustment (x.sub.11) is reached that still is implementable in the radiation apparatus; stopping the forward development, thereby determining x.sub.11 represented by a final front point (y.sub.11) as a real Pareto point of PS′; and along q.sub.1, dismissing undetermined portions of the objective space in front of and behind y.sub.11 as not containing parts of the PS, and continuing with other remaining more determined portions that are assumed to each contain a part of PS′.

Particle radiation therapy and planning utilizing magnetic resonance imaging (MRI) data. Radiation therapy prescription information and patient MRI data can be received and a radiation therapy treatment plan can be determined for use with a particle beam. The treatment plan can utilize the radiation therapy prescription information and the patient MRI data to account for interaction properties of soft tissues in the patient through which the particle beam passes. Patient MRI data may be received from a magnetic resonance imaging system integrated with the particle radiation therapy system. MRI data acquired during treatment may also be utilized to modify or optimize the particle radiation therapy treatment.

Disclosed herein are systems, methods, and computer-readable storage devices for manufacturing patient-specific bolus for use in targeted radiotherapy treatment. Based on dose calculations without a bolus and based on three-dimensional scan data of a patient, the example system generates a model of a bolus for targeting radiotherapy treatment to a planning target volume or target region within the patient. The system can perform several iterations to generate a resulting model for the bolus. Then, the system can generate instructions for controlling a three-dimensional printer to generate the bolus that conforms to the patient's skin surface while also specifically targeting the planning target volume for the radiotherapy treatment. In this way, the amount of radiotherapy treatment administered to other tissue is reduced, while the costs, time, and human involvement in creating the bolus are significantly reduced.

Systems and methods arc disclosed for generating radio-therapy treatment machine parameters based on projection images of a target anatomy. The systems and methods include receiving an image depicting an anatomy of a subject: generating a first projection image based on the received image that represents a view of the anatomy from a first gantry angle of tire radiotherapy treatment machine; applying a machine learning model to the first projection image to estimate a first graphical aperture image representation of multi-leaf collimator (MLC) leaf positions at the first gantry angle and the radiation intensity at that angle, the machine learning model being trained to establish a relationship between projection images representing different views of a patient anatomy and respective graphical aperture image representations of the MLC leaf positions at different gantry angles corresponding to the different views: and generating radiotherapy treatment machine parameters based on the first graphical aperture image representation.

An apparatus for developing an intensity-modulated radiation therapy treatment plan includes a memory that stores machine instructions and a processor that executes the machine instructions to receive a clinical goal associated with the treatment plan as a user input. The processor further executes the machine instructions to determine a plan objective based on the clinical goal, generate a cost function comprising a term based on the plan objective, and assign an initial value to a parameter associated with the term. The processor also executes the machine instructions to identify a microstate that results in a reduced value associated with the cost function, evaluate a fulfillment level associated with the clinical goal, and adjust the value of the parameter to improve the fulfillment level.

Method and apparatus (DMS) for dosage management in radiation therapy planning and/or delivery. Images of a region of interest ROI are acquired at different times. A registration transformation is computed that deforms one of the two images into the other. A magnitude of the transformation is then computed based on a suitable metric. If the computed magnitude is found to comply with a pre-defined criterion, the transformation is used to deform a dose distribution map and compute, based on the deformed dose map, therefrom a new fluence map.

A treatment planning apparatus 501 or a clinical decision support apparatus 701 calculates a dose distribution, calculates the damage to a normal tissue for a plurality of number of times of radiation irradiation based on the calculated dose distribution, and displays at least one or more calculated damages to the normal tissues on display apparatuses 603 and 703 or outputs the same to an outside of the apparatuses 501 and 701. Accordingly, since effects can be calculated for a plurality of number of times of irradiation, the optimum number of time of irradiation can be presented, and an operator and a doctor can be presented with a suitable decision material of a radiation irradiation planning.

A method of evaluating a radiation therapy (RT) treatment plan for a treatment volume, divided into sub-volumes and having a target volume and one or more organs at risk, OAR. It includes obtaining a RT treatment plan; calculating the linear energy transfer, LET, in each sub-volume; dividing the dose distribution into doses of a first category and a second category in each sub-volume, wherein the first category comprises doses with energy depositions with an LET below a first LET threshold and the second category comprises doses with energy depositions with an LET above a second LET threshold; determining amounts of doses of the first and of the second category in each sub-volume; and performing an analysis of the quality of the RT treatment plan by metrics based on the obtained distribution of doses of the first and of the second category in the target volume and in the OAR.