Abstract: Embodiments described herein derive improvements to a collimator model of a multi-leaf collimator (MLC) for a dose calculation source model. The MLC has movable leaves formed of an attenuating material for a beam of radiation. The collimation model includes a 3D geometric model of the MLC. The system determines a plurality of attenuation lengths at leaf of the MLC in attenuating the beam of radiation. The beam of radiation includes a plurality of beam segments, and the plurality of attenuation lengths at leaf may be based on distance that the beamlet travels in leaf along respective beam segments. The system retrieves pre-calculated beam spectrum data for the beam of radiation, and may receive data representing the input spectrum for the beam of radiation. The system applies these data to calculate adjustments to the dose calculation source model to take into account beam hardening along the MLC geometry.

Abstract: Embodiments described herein provide for training a local neural network model that receives a first data set corresponding to parameters of a radiation therapy delivery system for delivering a radiation field to a phantom. The neural network model has been trained to determine the quantity of radiation delivered to a plurality of volume elements of the phantom based on the parameters of the first data set. The local neural network model receives local dose calculation parameters, such as total energy released per mass (TERMA) values and density values, for each voxel grid element of a high resolution voxel grid of the phantom. In an embodiment, each voxel grid element includes a central voxel and a plurality of neighboring voxels. The processor applies the neural network model to the TERMA values and the density values of the neighboring voxels to determine the quantity of radiation delivered to the central voxel.

Abstract: An image acquisition apparatus includes: a positioner controller communicatively coupled to a positioner, wherein the positioner controller is configured to generate a control signal to cause the positioner to rectilinearly translate a patient support relative to an imager, and/or to rectilinearly translate the imager relative to the patient support; an imaging controller configured to operate the imager to generate a first plurality of two-dimensional images for a patient while the patient is supported by the patient support, and while the positioner rectilinearly translates the patient support and/or the imager; and an image processing unit configured to obtain the first plurality of two-dimensional images and arrange the two-dimensional images relative to each other to obtain a first composite image.

Abstract: A radiation treatment system includes a radiation delivery system including a rotatable gantry that is coupled to a static portion of the radiation treatment system and a radiation source that directs treatment radiation to a target volume and is mounted on the rotatable gantry for rotation about the target volume. The radiation treatment system further includes a computed tomography (CT) imaging system that generates portions of a CT scan of a region of patient anatomy that includes the target volume, wherein the CT imaging system includes an arcuate array of x-ray detectors, and an array of x-ray sources positioned around the target volume, wherein each x-ray source in the array of x-ray sources is oriented to direct imaging x-rays towards a different portion of the arcuate array of x-ray detectors.

Abstract: A computer-implemented method of reducing scatter in an X-ray projection image of an object comprises: generating an initial X-ray projection image with an imaging beam and an X-ray detector; based on a first position in a detector array of the X-ray detector, selecting a first kernel for convolution of a first portion of the initial projection image, wherein the first position corresponds to the first portion of the initial projection image; based on a second position in the detector array of the X-ray detector, selecting a second kernel for convolution of a second portion of the initial projection image, wherein the second position corresponds to the second portion of the initial projection image; convolving the first portion with the first kernel and the second portion with the second kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.

Abstract: A computer-implemented method of reducing scatter in an X-ray projection image of an object, the method comprising: generating an initial X-ray projection image of an object with an imaging beam produced by an imaging system; based on a first transmission indicator for the object and on a second transmission indicator for at least one element of the imaging system, selecting a kernel for convolution of the initial projection image; convolving the initial X-ray projection image with the kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.

Abstract: A virtual beam's-eye view of a planning target volume is generated based on volumetric image data acquired immediately prior to radiation therapy by a radiation therapy system. The virtual beam's-eye view can then be displayed to confirm that, with the patient disposed in the current position, the planned beam-delivered treatment extends beyond the surface of the skin. In some embodiments, the virtual beam's-eye view can be displayed in conjunction with a beam's-eye view that is generated based on volumetric image data acquired during treatment planning, to create a blended beam's-eye view. In some embodiments, a field outline of a treatment beam can be superimposed on the blended beam's-eye view, thereby illustrating whether the planned beam-delivered treatment extends beyond the surface of the skin of the patient. The blended beam's-eye view can facilitate a manual confirmation process that verifies the planned beam-delivered treatment extends beyond the surface of the skin.

Abstract: A crystalline structure modeling methodology that is conventionally used to model crystalline matter down to the atomic level is instead used to determine spot placement for radiation treatment. The cross-sectional shape of a treatment target is specified; locations (peaks) in a density field inside the shape are determined using the crystalline structure model; locations of spots in the treatment target for spot scanning are determined, where the locations correspond to the locations (peaks) inside the shape determined using the crystalline structure model; and the locations of the spots are stored as candidates for potential inclusion in a radiation treatment plan.

Abstract: Disclosed herein are systems and methods for identifying radiation therapy treatment data for patients. A processor accesses a neural network trained based on a first set of data generated from characteristic values of a first set of patients that received treatment at one or more first radiotherapy machines. The processor executes the neural network using a second set of data comprising characteristic values of a second set of patients receiving treatment at one or more second radiotherapy machines. The processor executes a calibration model using an output of the neural network based on the second set of data to output a calibration value. The processor executes the neural network using a set of characteristics of a first patient to output a first confidence score associated with a first treatment attribute. The processor then adjusts the first confidence score according to the calibration value to predict the first treatment attribute.

Abstract: A computer-implemented method of performing radiation therapy on a target volume within an anatomical region of a patient includes: acquiring a cone-beam computed tomography (CBCT) image of the region while the region is positioned in a first preliminary treatment location; reconstructing a current digital volume of the region based on the CBCT image of the region; generating a first beam's-eye-view of the region based on the current digital volume and an offset between the first preliminary treatment location and a reference treatment location; modifying the first beam's-eye-view of the region with one or more visual cues associated with a treatment field for the target volume; and displaying the first beam's-eye-view with the one or more visual cues.

Abstract: Disclosed herein are methods and systems for calculating radiation therapy treatment plan (RTTP) including receiving radiation therapy treatment planning data associated with a radiation therapy treatment of a patient; executing a first computer model to identify one or more attributes of a treatable sector for the radiation therapy treatment of the patient, the first computer model configured to ingest radiation therapy treatment planning data and clinical objectives to generate the one or more attributes of the treatable sector; and executing, by the processor, a second computer model to identify a radiation therapy treatment plan for the patient using the received radiation therapy treatment planning data associated with a patient, wherein the processor limits a search space used by the second computer model using the one or more attributes of the treatable sector identified by the first computer model.

Abstract: 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.

Abstract: Nominal values of parameters, and perturbations of the nominal values, that are associated with previously defined radiation treatment plans are accessed. For each treatment field of the treatment plans, a field-specific planning target volume (fsPTV) is determined based on those perturbations. At least one clinical target volume (CTV) and at least one organ-at-risk (OAR) volume are also delineated. Each OAR includes at least one sub-volume that is delineated based on spatial relationships between each OAR and the CTV and the fsPTV for each treatment field. Dose distributions for the sub-volumes are determined based on the nominal values and the perturbations. One or more dose prediction models are generated for each sub-volume. The dose prediction model(s) are trained using the dose distributions.

Abstract: An imager includes: an array of imager elements configured to generate image signals based on radiation received by the imager; and circuit configured to perform readout of image signals, wherein the circuit is configured to be radiation hard. An imager includes: an array of imager elements configured to generate image signals based on the radiation received by the imager; and readout and control circuit coupled to the array of imager elements, wherein the readout and control circuit is configured to perform signal readout in synchronization with an operation of a treatment beam source.

Abstract: A computing system comprising a central processing unit (CPU), and memory coupled to the CPU and having stored therein instructions that, when executed by the computing system, cause the computing system to execute operations to generate a radiation treatment plan. The operations include accessing a minimum prescribed dose to be delivered into and across the target, determining a number of beams and directions of the beams, and determining a beam energy for each of the beams, wherein the number of beams, the directions of the beams, and the beam energy for each of the beams are determined such that the entire target receives the minimum prescribed dose. The operations further include prescribing a dose rate and optimizing dose rate constraints for FLASH therapy, and displaying a dose rate map of the FLASH therapy.

Abstract: Embodiments described herein provide for determining a probability distribution of a three-dimensional point in a template feature map matching a three-dimensional point in space. A dual-domain target structure tracking end-to-end system receives projection data in one dimension or two dimensions and a three-dimensional simulation image. The end-to-end system extracts a template feature map from the simulation image using segmentation. The end-to-end system extracts features from the projection data, transforms the features of the projection data into three-dimensional space, and sequences the three-dimensional space to generate a three-dimensional feature map. The end-to-end system compares the template feature map to the generated three-dimensional feature map, determining an instantaneous probability distribution of the template feature map occurring in the three-dimensional feature map.

Abstract: 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.

Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: Embodiments of the present invention provide methods and systems for proton therapy planning that maximize the dose rate for different target sizes for FLASH therapy treatment are disclosed herein according to embodiments of the present invention. According to embodiments, non-standard scanning patterns can be generated, for example, using a TPS optimizer, to maximize dose rate and the overall FLASH effect for specific volumes at risk. The novel scanning patterns can include scanning subfields of a field that are scanned independently or spiral-shaped patterns, for example. In general, spot locations and beam paths between spots are optimized to substantially achieve a desired dose rate in defined regions of the patient's body for FLASH therapy treatment.

Abstract: A computing system comprising a central processing unit (CPU), and memory coupled to the CPU and having stored therein instructions that, when executed by the computing system, cause the computing system to execute operations to generate a radiation treatment plan. The operations include accessing a minimum prescribed dose to be delivered into and across the target, determining a number of beams and directions of the beams, and determining a beam energy for each of the beams, wherein the number of beams, the directions of the beams, and the beam energy for each of the beams are determined such that the entire target receives the minimum prescribed dose. The operations further include prescribing a dose rate and optimizing dose rate constraints for FLASH therapy, and displaying a dose rate map of the FLASH therapy.