IMRT Dose Optimization Using Motion Probability Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current intensity modulated radiation therapy (IMRT) methods fail to accurately deliver the prescribed radiation dose due to patient motion, which can result in inadequate treatment of tumor areas or excessive exposure to surrounding tissues.
Innovation Solution
A system and method that generate intensity maps for IMRT by accounting for patient motion using a probability distribution function to adjust fluence values for each radiation ray, ensuring that the planned dose is delivered accurately despite movement during treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional IMRT methods are used with static treatment plans, then the treatment planning process is simple and fast, but the dose delivery accuracy deteriorates due to patient motion
Solution Approach 1:
The patent applies dynamics by transitioning from static treatment plans to dynamic plans that account for patient motion. The system incorporates motion information (respiratory cycles, tumor displacement) into the treatment planning process, allowing the radiation delivery system to adapt beam parameters dynamically during treatment to maintain accuracy despite patient movement.
Solution Approach 2:
The patent implements preliminary action by preparing motion compensation strategies in advance during the treatment planning phase. The system pre-calculates adjusted beam parameters and sequences based on predicted patient motion patterns, so that when treatment begins, the correct compensatory actions are already prepared and can be executed without real-time computational delays.
2Reliability
If treatment plans do not account for patient motion, then the planning process remains straightforward, but tumor treatment effectiveness deteriorates due to misplaced radiation
Solution Approach 1:
The patent applies feedback by incorporating real-time or near-real-time monitoring of patient motion into the treatment delivery system. Motion sensors detect actual tumor position deviations, and the system uses this feedback information to dynamically adjust beam parameters, ensuring the radiation remains accurately targeted at the tumor despite motion variations during treatment.
Solution Approach 2:
The patent implements parameter changes by modifying beam parameters (intensity, direction, timing) based on patient motion state. The system continuously adjusts these parameters in response to detected motion, transforming the static beam delivery into a dynamic process that adapts to maintain optimal tumor targeting and dose distribution.
3Measurement precision
If static treatment plans are used, then the treatment delivery system is simpler, but the dose distribution accuracy deteriorates due to motion-induced blurring
Solution Approach 1:
The patent applies segmentation by dividing the treatment into discrete temporal and spatial segments corresponding to specific motion phases. The treatment plan is segmented into multiple delivery steps, each optimized for a particular position or phase of patient motion, allowing precise dose delivery at each segment while managing overall complexity through structured organization.
Solution Approach 2:
The patent implements asymmetry by creating non-uniform intensity maps that compensate for motion-induced dose blurring. Rather than using symmetric or uniform dose distributions, the system calculates asymmetric intensity patterns that are specifically tailored to counteract predicted motion effects, ensuring accurate dose delivery despite the added complexity of motion compensation.
Data Source
AI summary
A computer-implemented method for optimizing a radiation treatment plan for a radiotherapy machine providing independently controlled radiation along a plurality of rays j directed toward a patient and configured to account for the effects of patient motion. The method includes generating a probability distribution function quantitatively expressing patient motion, identifying a prescribed total dose Dip at the voxels i in a treatment area, assigning a fluence value wj for each ray j based on an iterative function, calculating an actual total dose Did produced each voxel i within the assigned fluence values and calculating an expectation value of the dose per energy fluence, dij based on the actual total dose Did and the probability distribution function.