A method of preventing damage to non-neoplastic, e.g. healthy cells, by irradiating the non-neoplastic cells with a low-dose radiation is provided. The method initiates a protective cellular response which prevents later damage to non-neoplastic cells by radiotherapy and an immune response against neoplastic cells. The method of preventing damage to non-neoplastic cells is provided where the low-dose radiation is interspersed with a high dose sessions which themselves are varied through the weekly schedule.

A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

A method and related system to adjust an existing treatment plan. A second optimization is run based on a dual objective function system that includes a first objective function used for the optimization in respect of the existing plan and a second, extended objective function that includes the said first objective function as a functional component.

Provided are a method and apparatus for verifying a radiation beam intensity modulator. The apparatus includes a scanner; and a verification system verifying the verification target radiation beam intensity modulator, wherein the verification system includes: a modulator structure reconstruction unit extracting thickness information of the verification target radiation beam intensity modulator based on the 3D structure information of the verification target radiation beam intensity modulator; an original modulator structure information receiver receiving structure information of the original radiation beam intensity modulator; a modulator matching unit matching the verification target radiation beam intensity modulator with the original radiation beam intensity modulator based on the thickness information; and a modulator verification unit verifying the verification target radiation beam intensity modulator matched with the original radiation beam intensity modulator.

A computer-implemented method for generating a radiation therapy treatment plan for a volume of a patient, the method comprising: receiving an image of the volume; receiving at least one dose-distribution-derived function configured to provide a value as an output based on, as input, at least part of a dose distribution defined relative to said image; receiving a first probability distribution and at least a second, different, probability distribution, the first and at least second probability distributions; defining a multi-criteria optimization problem comprising at least a first objective function based on the at least one dose-distribution-derived function, the first probability distribution and a loss function; and a second objective function based on the at least one dose-distribution-derived function, the second probability distribution and the loss function; and performing a multi-criteria optimization process based on said at least two objective functions to generate at least two output treatment plans.

Methods, apparatuses and systems are disclosed for dose-based optimization related to multi-leaf collimator (“MLC”) tracking during radiation therapy. In an example, a method includes calculating a planned radiation dose using an MLC plan in an unshifted dose volume, acquiring, using a radiation machine, a target position through motion tracking, and shifting the dose volume by the target position. The method also includes integrating a three-dimensional dose into a two-dimensional beam’s eye view grid and fitting, using the radiation machine for each leaf track, an MLC aperture by minimizing a cost function. The method further includes calculating and accumulating a delivered dose based on the fitted leaf positions of the MLC and updating a gantry position and MLC leaves to update a next planned dose.

Information associated with a radiation treatment plan includes, for example, values of dose per voxel in a target volume, values of dose rate per voxel in the target volume, and values of parameters used when generating the values of dose per voxel and the values of dose rate per voxel. Renderings that include, for example, a rendering of an image of or including the target volume, and a rendering of selected values of the radiation treatment plan, are displayed. When a selection of a region of one of the renderings is received, a displayed characteristic of another one of the renderings is changed based on the selection.

A radiation treatment plan for a particular patient is optimized that provides corresponding resultant radiation dosing information. Such optimization can include, by one approach, calculating a corresponding influence matrix. Upon detecting at least one manual edit to at least one spot weight that corresponds to the radiation treatment plan, these teachings can provide for responsively generating new radiation dosing information in at least near real-time as a function of a corresponding influence matrix.

These teachings provide for quickly yet accurately forming an influence matrix by generating the influence matrix via integration with a Monte Carlo particle transport simulation. The resultant influence matrix can then be utilized in an ordinary manner when optimizing a radiation treatment plan. By one approach, the foregoing comprises generating the influence matrix via integration with a Monte Carlo particle transport simulation on a particle-by-particle basis. For example, for each particle, these teachings can provide for identifying a spot to which the particle belongs and then adding a dose deposited by the particle during transport to an influence matrix element that corresponds to a spot to which the particle belongs and a voxel to where the dose was deposited.

Patient information for a particular patient, radiation treatment information, and information regarding a particular radiation treatment platform that includes at least one multi-leaf collimator are each accessed. These teachings then provide for optimizing a radiation treatment plan to dose at least one treatment volume in the particular patient using the particular radiation treatment platform as a function of the patient information, the radiation treatment information, and the information regarding the particular radiation treatment platform, wherein the optimizing does not include optimizing movement of any leaves of the multi-leaf collimator.