Varian Medical Systems International Ag

Siemens Healthineers International Ag

Marko Rusanen

Jarkko Yriana Peltola

Janne Iimari Nord

Esa Kuusela

Juha Kauppinen

Santtu Ollila

Mikko Vainio

Viljo Petäjä

Ville Pietila

Christopher Boylan

Shahab Basiri

Lasse Heikki Toimela

Janne Ilmari Nord

Sami P Siljamaki

Ramin Baghaie

Tuomas Tallinen

Viljo Petaja

Tuomas Erik Torsti

Ari Harju

Heini Hyvönen

Timo Ikonen

Petri Hiltunen

Petri Kokkonen

Antti Karjalainen

In a radiation treatment plan that includes a plurality of treatment fields of multiple treatment modalities, such as IMRT modality and dynamic treatment path modality (e.g., VMAT and conformal arc therapy), an optimized spatial point sequence may be determined that optimizes the total treatment time, which includes both the beam-on time (i.e., during the delivery of radiation dose) and the beam-off time (i.e., during transitions between consecutive treatment fields). The result is a time-ordered field trajectory that intermixes and interleaves different treatment fields. In one embodiment, a dynamic treatment path may be cut into a plurality of sections, and one or more IMRT fields may be inserted between the plurality of sections.

Generating time-efficient treatment field trajectories for external-beam radiation treatments

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.

Method and apparatus to optimize a radiation treatment plan

Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

Artificial intelligence modeling for radiation therapy dose distribution analysis

A memory has a fluence map that corresponds to a particular patient stored therein. This memory also has at least one deep learning model stored therein trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. A control circuit operably coupled to that memory iteratively optimizes a radiation treatment plan to administer therapeutic radiation to that patient by, at least in part, generating a leaf sequence as a function of the at least one deep learning model and the fluence map that corresponds to the patient.

Method and apparatus to facilitate generating a leaf sequence for a multi-leaf collimator

A method of calculating radiation dose includes dosimetric projection of a collimator geometry. The method includes defining a three-dimensional (3D) geometry of a collimating device which defines an aperture configured to allow a radiation beam passing through, projecting the collimating device along the radiation beam into a two-dimensional (2D) geometry in a plane, calculating dosimetric opacity values of the collimating device at locations adjacent to the aperture based on the 3D geometry of the collimating device, and calculating transport of the radiation beam through the collimating device based on the 2D geometry projected in the plane and using the dosimetric opacity values of the collimating device at the locations adjacent to the aperture.

Dosimetric projection of collimator geometry

An optimized radiation treatment plan may be developed in which the total monitor unit (MU) count is taken into account. A planner may specify a maximum treatment time. An optimization algorithm may convert the specified maximum treatment time to a maximum total MU count, which is then used as a constraint in the optimization process. A cost function for the optimization algorithm may include a term that penalizes any violation of the upper constraint for the MU count.

Optimization of radiation treatment plans for optimal treatment time in external-beam radiation treatments

These various embodiments access target information regarding a radiation-therapy treatment volume for a given patient as well as non-target information regarding at least one structure other than the radiation-therapy treatment volume that also comprises a part of the given patient. These embodiments then provide for accessing uncertainties information regarding spatial uncertainties as pertain to at least one of the target information and the non-target information and using that uncertainties information to characterize at least one radiation-therapy treatment plan optimization consideration with respect to a preference of usage to thereby provide preference considerations. These preference considerations are then used to influence a follow-on radiation-therapy treatment plan optimization process when developing a treatment plan for the radiation-therapy treatment volume.

Method and apparatus pertaining to radiation-therapy treatment-plan optimization

One accesses information regarding the functionality of portions of the patient's body and then uses that information to optimize a radiation-treatment plan to treat a target portion of the patient's body while minimizing at least some collateral radiation-based damage to non-targeted functional portions of the patient's body. By one approach, the aforementioned information can comprise a functionality model as pertains to at least some portions of the patient's body. As one example in these regards, this can comprise optimizing the radiation-treatment plan such that the planned radiation beams tend to pass through non-targeted less-functional portions of the patient's body rather than through non-targeted portions of the patient's body of greater functionality.

Method and apparatus to facilitate treating a target portion of a patient's body with radiation

A treatment planning method and system for optimizing a treatment plan used to irradiate a treatment volume including a target volume, such as a tumor, is disclosed. According to the method, two dose calculation algorithms are used to develop the optimized treatment plan. A first dose calculation algorithm is used to obtain substantially complete dose calculations and a second, incremental, dose calculation algorithm is used to make more limited calculations. The incremental calculations may be performed, for example, with less precision, less accuracy or less scope (e.g., focused on a specific subvolume within the treatment volume) in order to reduce the time required to achieve an optimized plan. Each of the dose calculation algorithms may be iterated a plurality of times, and different cutoff criteria can be used to limit the number of iterations in a given pass. A treatment planning system of the invention uses software for implementing the complete and incremental dose calculation algorithms. The method and system are especially useful for IMRT and arc therapy where treatment plan optimization is particularly challenging.

Growing community of inventors

Espoo, Finland

Marko Rusanen