Systems and methods for shuttle mode radiation delivery are described herein. One method for radiation delivery comprises moving the patient platform through the patient treatment region multiple times during a treatment session. This may be referred to as patient platform or couch shuttling (i.e., couch shuttle mode). Another method for radiation delivery comprises moving the therapeutic radiation source jaw across a range of positions during a treatment session. The jaw may move across the same range of positions multiple times during a treatment session. This may be referred to as jaw shuttling (i.e., jaw shuttle mode). Some methods combine couch shuttle mode and jaw shuttle mode. Methods of dynamic or pipelined normalization are also described.

The disclosure provides systems and methods for adjusting a multi-leaf collimator (MLC). The MLC includes a plurality of cross-layer leaf pairs, each cross-layer leaf pair of the plurality of cross-layer leaf pairs includes a first leaf located in a first layer of leaves and a second leaf opposingly located in a second layer of leaves. For at least one cross-layer leaf pair, an effective cross-layer leaf gap to be formed between the first leaf and the second leaf may be determined; at least one of the first leaf or the second leaf may be caused to move to form the effective cross-layer leaf gap; and an in-layer leaf gap may be caused, based on the effective cross-layer leaf gap, to be formed between the first leaf and an opposing first leaf in the first layer. A size of the in-layer leaf gap may be no less than a threshold.

Methods and systems for configuring a plan optimizer model for radiotherapy treatment is presented herein in which a processor iteratively trains a machine learning model configured to predict a heuristic parameter, wherein with each iteration, an agent of the machine learning model identifies a test heuristic parameter; transmits the test heuristic parameter to the plan optimizer model configured to receive one or more radiotherapy treatment attributes and predict a treatment plan; and identifies a reward for the test heuristic parameter based on execution performance value of the plan optimizer model, wherein the processor iteratively trains a policy of the machine learning model until the policy satisfies an accuracy threshold based on maximizing the reward.

A compensating device for use in ion-based radiotherapy may comprise a disk with a number of protrusions may be placed in a radiation beam to affect the ions in the beam in different ways to create an irradiation field from a broad beam. This is particularly useful in FLASH therapy because of the limited time available or modulating the beam. A method of designing such a compensating device is proposed, comprising the steps of obtaining characteristics of an actual treatment plan comprising at least one beam, determining at least one parameter characteristic of the desired energy modulation of the actual plan by performing a dose calculation of the initial plan and, based on the at least one parameter, computing a shape for each of the plurality of elongated elements to modulate the dose of the delivery beam to mimic the dose of the initial plan per beam.

It is provided a method for determining arc costs. The method comprises the steps of: determining a plurality of beam orientations; evaluating a set of at least one cost function comprising an intermediate exposure cost function that is evaluated by performing the substeps of: projecting the at least one target volumes on a beam plane; determining an alignment angle based on a collimator angle value; finding any intermediate area in the beam plane along the alignment angle between areas of the at least one target volume projection; determining a value of the intermediate exposure cost function. The method further comprises the steps of: finding a plurality of arcs, wherein each arc comprises a sequence of a plurality of beam orientations; and calculating, for each arc in the plurality of arcs, at least one arc cost based on the cost function values of the beam orientations of the arc.

Better Pareto dose distributions for multi-criteria optimization of treatment plans can be obtained by obtaining at least one reference dose function designed to result in an acceptable reference dose distribution, defining a multi-criteria optimization problem including the at least one reference dose function as at least one optimization function, performing at least two optimization procedures based on the multi-criteria optimization problem to generate a set of at least two possible treatment plans, obtaining a treatment plan to be used for treating the patient, based on the set of possible treatment plans, by selecting one plan or by combining plans.

Systems, kits and methods for preparing an injection system and/or treating target lesions with a selective internal radiation therapy which includes a double-barrel syringe loaded with a two-component tissue glue and radioisotope loaded microspheres. The microspheres are loaded into the syringe based on the size of the target location and are administered with a needle or dual-lumen catheter. Dosing regimens for treating breast cancer lesions or surgical beds up to 130 mm in diameter and hepatocellular carcinoma lesions up to 50 mm are included.

Techniques for particle beam therapy include receiving a target region inside a subject for particle therapy, a minimum dose inside the target region, and a maximum dose inside the subject but outside target region. Multiple beam axis angles are determined, each involving a gantry angle and a couch position. Multiple spots within the target region are determined. For each beam axis angle a pristine particle scan beam (not coaxial with any other particle scan beam) is determined such that a Bragg Peak is directed to a spot, and repeated until every spot is subjected to a Bragg Peak or an intersection of two or more such pristine scan beams. Output data indicating the pristine beamlets is stored for operation of a particle beam therapy apparatus.

Deep learning approaches automatically segment at least some breast tissue images while non-deep learning approaches automatically segment organs-at-risk. Both three-dimensional CT imaging information and two-dimensional orthogonal topogram imaging information can be used to determine virtual-skin volume. The foregoing imaging information can also serve to automatically determine a body outline for at least a portion of the patient. That body outline, along with the virtual-skin volume and registration information can serve as inputs to automatically calculate radiation treatment platform trajectories, collision detection information, and virtual dry run information of treatment delivery per the optimized radiation treatment plan.

It is provided a method for determining a distribution of spots for use with ion beam therapy for providing the spots in a target volume, wherein each spot represents a collection of ions of a specific energy level and of a specific size at a specific lateral location. The method is performed in a treatment planning system and comprises the steps of: dividing the target volume in a plurality of target sections based on a user configuration comprising at least one spot size strategy defining a maximum spot size at the location of a Bragg peak; assigning a spot size strategy to each one of the target sections based on the location of the respective target section; and determining, within each target section, spots in accordance with its spot size strategy.