Patent application number | Description | Published |
20110293071 | METHOD FOR CALCULATING HEAD SCATTER PHASE SPACE FOR RADIATION TREATMENT USING A MULTI-LEAF COLLIMATOR WITH DYNAMIC JAWS - A method is proposed for accurate and efficient modeling of head scatter phase space for treatments with dynamic jaws. Specifically, the method enables the efficient calculation of the head scatter phase space in case of a dynamic treatment where jaws and MLC leaves move during the delivery. In one embodiment, the invention can be used to calculate the head scatter contribution during final dose calculation of dynamic treatments. This novel method also enables an accurate calculation of the head scatter contribution from optimal fluence and from jaw positions without having to calculate the leaf sequence. In this embodiment, the invention can be used in optimization of large field IMRT treatments. | 12-01-2011 |
20120012763 | Method and Apparatus Pertaining to Use of Jaws During Radiation Treatment - These various embodiments are employed in conjunction with the use of both a multi-leaf collimator and jaws that are interposed between a source of radiation and a treatment target while sourcing radiation from the source of radiation towards the treatment target. Generally speaking, during some portion of the aforementioned treatment, these teachings provide for manipulating the jaws to more tightly constrain, in at least one dimension, a beam-shaping aperture as is formed by the multi-leaf collimator. In many cases, as when the leaves of the multi-leaf collimator move back and forth horizontally, the foregoing can comprise manipulating the jaws in a vertical dimension | 01-19-2012 |
20130187062 | METHOD AND APPARATUS PERTAINING TO RADIATION-TREATMENT PLAN OPTIMIZATION - A control circuit optimizes a radiation-treatment plan to provide an initially-optimized radiation-treatment plan and then modifies that initially-optimized radiation-treatment plan to reduce corresponding monitor units (MU's) to provide a radiation-treatment plan that is further optimized for monitor units. This modification can comprise, at least in part, imposing a stronger smoothing constraint with respect to fluence. Optimizing a radiation-treatment plan to provide an initially-optimized radiation-treatment plan can comprise identifying at least one particular leaf pair for a multi-leaf collimator that requires a longest amount of time to move into a position that achieves a particular desired fluence and then selectively smoothing position requirements of that particular leaf pair to reduce the amount of time associated with that particular leaf pair while not also smoothing position requirements for all leaf pairs as comprise that multi-leaf collimator. | 07-25-2013 |
20130324783 | Apparatus and Method Pertaining to Optimizing a Radiation-Treatment Plan Using Historical Information - A control circuit operably couples to a memory having historical information stored therein. This historical information comprises information regarding delivered radiation doses to non-targeted patient volumes for a plurality of different volume presentations. The control circuit iteratively optimizes a radiation-treatment plan for a specific plan using that historical information. The aforementioned historical information can comprise delivered-dose metrics as correspond to different relative distances within given patients. The control circuit can employ such information to determine, for example, an estimated dosage (including, if desired, a corresponding range of estimated dosages) for at least one volume within the specific patient at a specific distance from a specific point of reference. The control circuit can compare such historical information against radiation-treatment plan optimization results to qualitatively assess the radiation-treatment plan optimization results. | 12-05-2013 |
20140279725 | RADIATION THERAPY PLANING USING INTEGRATED MODEL - System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level. | 09-18-2014 |
20140350863 | SYSTEMS AND METHODS FOR AUTOMATIC CREATION OF DOSE PREDICTION MODELS AND THERAPY TREATMENT PLANS AS A CLOUD SERVICE - The present invention proposes a method for automatically creating a dose prediction model based on existing clinical knowledge that is accumulated from multiple sources without collaborators establishing communication links between each other. According to embodiments of the claimed subject matter, clinics can collaborate in creating a dose prediction model by submitting their treatment plans into a remote computer system (such as a cloud-based system) which aggregates information from various collaborators and produces a model that captures clinical information from all submitted treatment plans. According to further embodiments, the method may contain a step where all patient data submitted by a clinic is made anonymous or the relevant parameters are extracted and condensed prior to submitting them over the communications link in order to comply with local regulations. | 11-27-2014 |
20150094519 | PREDICTING ACHIEVABLE DOSE DISTRIBUTION USING 3D INFORMATION AS AN INPUT - Knowledge-based radiotherapy treatment planning is expanded to include spatial information from, for example, positron emission tomography (PET). Information that is specific to a patient is accessed. A prediction of a spatial dose distribution inside a target volume in the patient is determined using the patient-specific information as an input to a prediction model. The prediction model is established using training data that includes data resulting from applying other radiation treatment plans to other patients. The training data includes spatially distributed information indicating a level of activity in target volumes in the other patients (e.g., PET image data). A dose-volume histogram and associated three-dimensional dose distribution information based on the prediction are produced. The dose-volume histogram and the three-dimensional dose distribution information can be used to develop a radiation treatment plan for the patient. | 04-02-2015 |
20150095043 | AUTOMATIC CREATION AND SELECTION OF DOSE PREDICTION MODELS FOR TREATMENT PLANS - A dose prediction model can be determined for generating a dose distribution of a treatment plan for irradiating a target structure within a patient. Treatment plans from previous patients can be analyzed to determine D characteristic values to obtain a D dimensional point for each treatment plan. The treatment plans can be clustered based on the D dimensional points. The treatment plans of a cluster can then be used to determine a dose prediction model. A dose prediction model for patient can be selected from among multiple models. Characteristics about the patient can be used to determine a D dimensional point corresponding to the patient. The D-dimensional point can be used to select a model in comparison to D dimensional points of the models. | 04-02-2015 |
20150095044 | DECISION SUPPORT TOOL FOR CHOOSING TREATMENT PLANS - Patient data can be used to determine input values to different estimation functions for different treatment types. The estimation functions can each be used to estimate one or more outcome values for the respective treatment. A quality score can be determined using the outcome value(s). A first treatment plan having an optimal quality score can be identified, e.g., by displaying the treatment plans with the quality scores, which may correspond to the outcome values. | 04-02-2015 |