Systems, devices, and methods for imaging-based calibration of radiation treatment couch position compensations.

Disclosed herein are methods for radiotherapy treatment planning and delivery that use sensor data from one or more target sensors. One variation of a radiotherapy treatment planning method comprises generating a sensor characterization image based on a sensor characterization probability density function (PDF) of a target sensor and calculating a set of firing filters that may be applied to sensor images generated from sensor data acquired during a radiation-delivery session. Additionally, a variation of a radiotherapy treatment planning method comprises generating multiple sensor characterization images based on multiple sensor characterization PDF of multiple target sensors and calculating multiple sets of firing filters for each of the multiple target sensors. The firing filters may be used with sensor images generated from target sensor data acquired from one or more target sensors during a radiation-delivery session to calculate a radiation fluence for delivering therapeutic radiation to a target region.

The invention comprises a method and apparatus for using a multi-layer multi-color scintillation based detector element to image a tumor of a patient using a process of determining residual energies of positively charged particles after passing through the patient, the process comprising the steps of: (1) transmitting the positively charged particles at known energies through the patient and into a multi-layer detector element; (2) detecting first and second secondary photons, resultant from passage of the positively charged particles, respectively from a first layer of a first scintillation material and a second layer of a second scintillation material at two respective layer depths, where the first wavelength range differs from the second wavelength range; (4) determining residual energies of the positively charged particles, using output from the step of detecting; and (5) relating the residual energies to body densities to generate an image.

Images that are associated with an identification of a tracking target of a patient to receive radiation treatment may be received. The images may be sorted into a sequence based on a motion of the patient. The sorted images may be provided via a graphical user interface. The sequence of the sorted images that are based on the motion of the patient may be provided.

A system and method for planning surgical procedure including a treatment zone setting view presenting at least one slice of a 3D reconstruction generated from CT image data including a target. The treatment zone setting view presenting a treatment zone marker defining a location and a size of a treatment zone and configured to adjust the treatment zone marker in response to a received user input. The system and method further including a volumetric view presenting a 3D volume derived from the 3D reconstruction and a 3D representation of the treatment zone marker relative to structures depicted in the 3D volume.

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).

A photon source emits a flattening filter-free photon beam. A control circuit operably couples to a multi-layer multi-leaf collimator that is disposed between the photon source and a treatment area of a patient. The control circuit automatically arranges operation of some, but not all, of the layers of the multi-layer multi-leaf collimator to serve as a virtual flattening filter with respect to the flattening filter-free photon beam emitted by the photon source. By one approach, another of the layers of the multi-layer multi-leaf collimator serves to form a treatment aperture corresponding to a shape of the treatment area of the patient. By one approach the control circuit comprises an integral part of a treatment platform (as versus a dedicated treatment planning platform) and can carry out most or even essentially all of the planning steps that lead to administration of the treatment to a patient.

A system and method for planning surgical procedure including a treatment zone setting view presenting at least one slice of a 3D reconstruction generated from CT image data including a target. The treatment zone setting view presenting a treatment zone marker defining a location and a size of a treatment zone and configured to adjust the treatment zone marker in response to a received user input. The system and method further including a volumetric view presenting a 3D volume derived from the 3D reconstruction and a 3D representation of the treatment zone marker relative to structures depicted in the 3D volume.

A method enables testing and evaluation of an expert human reviewer or an artificial intelligence (AI) error detection engine associated with a radiotherapy treatment planning process. Intentional errors are introduced into the output of a software module or AI engine that performs a certain step in the radiotherapy treatment planning process. The efficacy of the human or AI reviewer in detecting errors can then be evaluated or tested by determining whether the human or AI reviewer has detected the introduced error.

Disclosed herein are radiotherapy methods and systems that can display a workflow-oriented graphical user interface(s). In an embodiment, a method comprises presenting, by the server for display on a screen associated with a radiotherapy machine, a series of consecutive graphical user interfaces, wherein each graphical user interface displays one or more graphical components corresponding to one or more tasks of one or more stages of a patient's radiotherapy treatment; and when a user operating the pendant interacts with a first controller of the pendant, the radiotherapy machine adjusts at least one of its configurations; and when the user interacts with a second controller of the pendant, the server revises the graphical user interface corresponding to the user's input.