Koninklijke Philips Corporation N.v.

Vmware, Inc.

General Electric Company

University of Texas System

Yahoo! Inc.

Arista Networks, Inc.

Excalibur Ip, LLC

Koninklijkle Philips N.v.

Prashant Kumar

Karl Antonin Bzdusek

Vaitheeswaran Ranganathan

Vineet Kumar Sinha

Prasenjit Sarkar

Anoop Shukla

Aman Teja

Sree Ranga Sudha T K

Vineeth Chandran

Sourav Chakraborty

Rajarshi Saha

Yogisha Mallya

Indrajit Mazumder

Sabarinath Devarajan

Matthew Palmer

Gipson Joe Anto

Michael Kantor

Daniel Bystrov

Heinrich Schulz

Nicole Schadewaldt

Torbjoern Vik

Todd R McNutt

Deepak Pitambar Mahajan

Sriharsha Jayanarayana

Seyfettin Can Gulen

Prasanna Parthasarathy

Matthieu Frédéric Bal

Yogish Mallya

Chandrasekhar Pushkaran

Sivaramakrishnan Krishnaiyer Raman

Sankara Hari Gopalakrishnan

Vivekanandhan Thiruvettipuram Madhivanan

Kumara Sanjaya

Souri Rajan Venkatesan

Hrishikesh Dalvi

Hemant Puri

In order to assess the achievability of treatment goals for a radiation therapy treatment, an estimation unit generates a treatment plan for the radiation therapy treatment only on the basis of treatment goals relating to a target structure to obtain a first dose distribution, (i) segments the region of the patient's body into a plurality of concentric shells surrounding the target structure and determines a mean radiation dose assigned to each shell in accordance with the first dose distribution, and (iii) checks whether a further dose distribution can be determined, which fulfills the treatment goals relating to at least one structure at risk and, which is configured such that the mean radiation dose assigned to each shell in accordance with the further dose distribution corresponds to the mean radiation dose assigned to the same shell in accordance with the first dose distribution.

Estimation of the achievability of treatment goals in radiation therapy

A radiation therapy planning system () includes a radiation therapy optimization unit (), which receives at least one target structure and at least one organ-at-risk (OAR) structure segmented from a volumetric image, and generates an optimized plan () based on at least one modified objective function. The optimized plan () includes a planned radiation dose for each voxel.

Radiation therapy optimization unit with global considerations

The invention relates to a system and method for generating a radiotherapy treatment plan on the basis of treatment goals comprising optimization objectives and/or constraints. A planning unit () generates a first treatment plan including treatment parameters for fulfilling first treatment goals in a first optimization cycle, and a decision unit () receives second treatment goals and compares the first and second treatment goals todetermine a modification of the treatment goals, assigns to the modification a category from a plurality of predetermined categories, wherein to each category a strategy from a plurality of predetermined strategies for treatment plan generation is allocated, and instructs the planning unit () to generate the second treatment plan in accordance with a strategy allocated to the determined category of modifications in a second optimization cycle.

Automatic selection of optimization strategy in iterative treatment planning

An achievability estimate is computed for an intensity modulated radiation therapy (IMRT) geometry () including a target volume, an organ at risk (OAR), and at least one radiation beam. Namely, a geometric complexity (GC) metric is computed for the IMRT geometry that compares a number NT of beamlets of the at least one radiation beam available in the IMRT geometry for irradiating the target volume and a number n of these beamlets that also pass through the OAR. A GC metric ratio is computed of the GC metric for the IMRT geometry and the GC metric for a reference IMRT geometry for which an IMRT plan is achievable. If the clinician is satisfied with this estimate then optimization () of an IMRT plan for the IMRT geometry () is performed. Alternatively, a reference IMRT geometry is selected by comparing the GC metric with GC metrics of past IMRT plans.

Pre-optimization method for quick prediction of achievability of clinical goals in intensity modulated radiation therapy

A method and apparatus of a network element that processes data by a network element with a data processing pipeline is described. In an exemplary embodiment, the network element receives network data and performs a policy-based routing lookup using one or more characteristics of the network data to retrieve a next hop identifier. In addition, the network element generates a key for the next hop identifier and performs a longest prefix match lookup to retrieve a forwarding result. The network element further determines a next hop interface based on the forwarding result.

System and method of a data processing pipeline with policy based routing

A method and related system to adjust an existing treatment plan. A second optimization is run based on a dual objective function system that includes a first objective function used for the optimization in respect of the existing plan and a second, extended objective function that includes the said first objective function as a functional component.

Method to reduce local hot/cold spots in DMPO-based IMRT planning

A system and method for automatically generating radiation therapy treatment plans including one or more processors configured to capture geometries of organs at risk and a target volume specific to a subject, and use a shape-based algorithm to mine () a knowledgebase () of previously constructed treatment plans for similar geometries to the subject. The system and method interfaces () dosimetric information from a plan with a similar geometry as a patient specific starting point for a progressive tuning optimization algorithm resulting in fewer iterations. The progressive tuning algorithm () generates an optimized treatment plan. The optimized plan is evaluated against treatment goals. Trade-off plans are generated () create alternative plans according to unmet treatment goals.

Shape based initialization and QA of progressive auto-planning

A therapy planning system and method generate an optimal treatment plan accounting for changes in anatomy. Therapy is delivered to the subject according to a first auto-planned optimal treatment plan based on a first image of a subject. A second image of the subject is received after a period of time. The second image is registered with the first image to generate a deformation map accounting for physiological changes. The second image is segmented into regions of interest using the deformation map. A mapped delivered dose is computed for each region of interest using the dose delivery goals and the deformation map. The first treatment plan is merged with the segmented regions of the second image and the mapped delivered dose during optimization.

Automatic plan optimization for changing patient anatomy in the presence of mapped delivered dose from delivered fractions

A method and system determines a utility of performing a re-planning on a patient during a radiation therapy planning. Quality improvements which may be made possible by re-planning the patient are automatically estimated, independent of clinician bias. This enables the clinician to make an informed decision regarding whether to re-plan the patient in a fast and efficient manner. This achieves more uniformity and predictability in the adaptive re-planning.

Method and system for automatic estimation of utility of adaptive radiation therapy re-planning

Techniques for managing session data in a computer system having a plurality of servers establishing sessions with clients. Communications from any particular client are forwarded to the server assigned to handle that client. A load balancer may determine that any particular session for any particular client should be moved between servers, based on load balancing criteria. In this case, the load balancer causes session data for the server assigned to the client to be transferred to a different server. Acknowledgment signals are sent between the servers and load balancer to indicate completion status. Load balancer maintains backup data at secondary servers which can be brought online when a primary server becomes unresponsive. A method, system, and computer-readable medium are provided.

Sticky session data migration

A method for reviewing a treatment plan () for delivering radiation therapy to a patient. The treatment plan () includes geometric analysis data, dose distribution analysis data, dose volume histogram data, parametric analysis data or deliverability analysis data of a patient. First, for the treatment plan (), a plurality of clinical and delivery goals are identified (). Next, goal data points are extracted () from the treatment plan (). Then, data points are correlated () to identify deficiencies in the treatment plan (). A report is generated () to display on a display () the correlated data points using visual markings () to highlight identified deficiencies. Text and audio notations can be attached to the report to explain the correlations and warn a user of plan deficiencies.

Audio-visual summarization system for RT plan evaluation

Systems and techniques are described for identifying test gaps. A described technique includes identifying production code paths for an application. Each production code path specifies a respective sequence of code of the application that was executed in a production environment. Test code paths are identified for the application. Each test code path specifies a respective sequence of the application that was tested in a test environment. The production code paths are compared to the test code paths to identify a set of test gaps for the application. Each test gap specifies a respective production code path that is not included in the test code paths. Test gap data specifying the test gaps for the application can be provided for presentation to a user.

Identifying test gaps using code execution paths

A planning image memory stores a volume diagnostic image. A user inputs data defining clinical objectives including organs-at-risk with a user interface device. An auto-planning module generates a candidate treatment plan. A trade-off module having a processor evaluates the treatment plan against the clinical objectives. When one or more objectives is not met, the trade-off module performs a trade-off analysis to determine an effect on other clinical objectives and generates at least one trade-off treatment plan which more closely meets the not met objectives. The candidate and at least one trade-off plan are displayed on a display device and/or analyzed by the processor and a final treatment plan is selected from the at least one trade-off or candidate treatment plan.

Efficient treatment plan trade-off analysis

A recovery management system and recovery method uses dependency information between clients running in a distributed computer system to automatically determine logical fault domains for the clients. The logical fault domains are then prioritized for a recovery process.

Automatic discovery and prioritization of fault domains

A method for dose-gradient based optimization of an intensity modulated radiation therapy plan. First, an optimizer () performs a first optimization () of the plan to generate dose distributions corresponding to the plan. Next, the optimizer () generates a beam specific dose gradient map () for each beam of the plan. Then, new dose gradients are specified () for the plan. Last, the optimizer () performs a final optimization () using the new dose gradients. The final optimization is given the new dose gradients as soft constraints into an objective function. The optimizer () applies a limiting factor to the objective function such that a first dose gradient is limited by the optimizer only if the first dose gradient exceeds the new dose gradient for a specific beamlet.

Growing community of inventors

Bengaluru, India

Prashant Kumar