Radiotherapy Plan Modulation Control for Adaptive Dose Robustness

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Solution Overview

Problem

Existing radiotherapy planning systems face challenges in accurately adapting to anatomical changes during treatment due to physiological regression and patient movement, leading to underdosing of tumors and overdosing of surrounding organs, with traditional optimization methods being time-consuming and computationally intensive, and lacking flexibility in balancing dose conformity and field modulation.

Innovation Solution

A hybrid optimization framework integrating multi-field and single-field optimization techniques, with user-definable modulation parameters and interactive visualization, allows for dynamic control over field modulation and dose distribution, providing real-time feedback for iterative plan refinement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If frequent updates to radiotherapy treatment plan are performed to account for anatomical changes, then treatment accuracy is improved, but treatment time and workflow efficiency deteriorate due to complex replanning procedures

Engineering Contradiction:

Improvetreatment accuracyVSAvoidtreatment time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by establishing dosimetric parameters and optimization objectives before treatment delivery. The hybrid optimization framework pre-configures dose distribution parameters, field regularization factors, and plan regularization factors, allowing for rapid adaptation to anatomical changes without full replanning procedures

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a simplified model of the treatment plan optimization problem through the hybrid framework that copies essential dosimetric parameters and constraints. This model enables rapid evaluation and adjustment of treatment plans without requiring complete replanning, thus reducing treatment time while maintaining accuracy

Inventive Principle:

Principle #26Copying

2Manufacturing precision

If highly modulated dose distributions are used for individual fields to achieve precise dose delivery to target volume, then target coverage is improved, but sensitivity to patient movement increases leading to suboptimal dose delivery

Engineering Contradiction:

Improvedose conformityVSAvoidrobustness to movement

Core Design Contradiction:

Manufacturing precisionVSReliability

Solution Approach 1:

The system changes parameters by introducing field regularization factors that control the degree of modulation in individual fields. By adjusting these factors, the optimization framework balances dose conformity with robustness to patient movement, allowing the system to adapt to different clinical scenarios and patient conditions

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system implements dynamics by making the modulation level adjustable through user-defined regularization factors. The optimization framework dynamically balances between highly modulated distributions (for precision) and less modulated distributions (for robustness), allowing adaptation to actual patient positioning and movement patterns during treatment

Inventive Principle:

Principle #15Dynamics

3Ease of manufacture

If conventional plan optimizers use static dosimetric parameters hardcoded in software, then optimization is simplified, but flexibility to adapt to different clinical scenarios deteriorates and computational intensity increases

Engineering Contradiction:

Improveoptimization simplicityVSAvoidflexibility

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The system implements dynamics by transforming static, hardcoded dosimetric parameters into dynamic, user-configurable parameters. The hybrid optimization framework allows clinicians to adjust field regularization factors, plan regularization factors, and dosimetric objectives based on specific patient needs and clinical scenarios, providing both simplicity and flexibility

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system achieves universality by creating a unified optimization framework that handles both field-level and plan-level optimization with a single configurable system. The hybrid framework can adapt to different treatment types, anatomical regions, and clinical priorities through parameter adjustment rather than requiring separate optimization routines for each scenario

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260069889A1Systems and methods for modulation control in radiotherapy plans

Publication Date: 2026.03.12 SIEMENS HEALTHINEERS INTERNATIONAL AG

AI summary

Disclosed herein are methods and systems using artificial intelligence models to control the amount of modulation in radiotherapy plans. The system may receive parameters defining a plan objective for a radiotherapy treatment plan. The system may establish dose distribution parameters for each field of the plan objective. The system may initialize user-defined field regularization factors for each field of the plan objective. The system may initialize plan regularization factors. In response to applying the user-defined field regularization factor to a corresponding field of the plan objective and the plan regularization factor to the plan objective, the system may generate a treatment plan objective incorporating weighted components for the plan objective and each field. The system may transmit the treatment plan objective to a radiotherapy treatment planning computer model. The radiotherapy treatment planning computer model may determine the radiotherapy treatment plan based on the treatment plan objective.