Monte Carlo Material Maps for Proton Therapy Implants
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Solution Overview
Problem
Current proton therapy treatment planning systems inaccurately characterize metal implants due to CT image artifacts, leading to incorrect dose calculations and exclusion of patients with implants from treatment.
Innovation Solution
A system and method using Monte Carlo simulations to determine the material composition, density, and geometry of implants by analyzing particle counting data and representation data, generating a material map for accurate dose calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CT images are used to calculate dose in proton therapy treatment planning, then dose calculation can be performed, but metal implants cause severe artifacts leading to inaccurate characterization of the implant and surrounding tissues
Solution Approach 1:
The patent introduces an intermediary material map generation process that acts as a mediator between the CT images and dose calculation. This material map, generated through Monte Carlo simulations, translates the artifact-prone CT data into accurate material composition and density information, enabling precise dose calculation without being affected by metal artifacts
Solution Approach 2:
The patent replaces the conventional direct CT-to-dose calculation mechanism with a Monte Carlo simulation-based material map generation process. This substitution uses probabilistic particle interaction modeling to overcome the deterministic limitations of traditional CT-based dose calculation in the presence of metal implants
2Measurement precision
If manual override of Monte Carlo dose calculation is performed to address implant inaccuracies, then dose accuracy may be improved, but the process becomes time-consuming and often inaccurate or not feasible due to lack of detailed implant information
Solution Approach 1:
The system performs self-service by automatically generating material maps through Monte Carlo simulations using available imaging data. The process autonomously characterizes implant materials and surrounding tissues without requiring manual intervention or external implant specifications, thereby maintaining high accuracy while minimizing time loss
Solution Approach 2:
The material map generation is performed as a preliminary step before dose calculation. By pre-characterizing the implant and surrounding tissues through simulation, the system prepares accurate input data in advance, eliminating the need for time-consuming manual overrides during the dosimetry process
3Reliability
If metal implant patients are excluded from general guidelines to avoid poor clinical outcomes, then patient safety is improved, but fewer patients can receive proton therapy
Solution Approach 1:
The patent fundamentally changes the input parameters for dose calculation by using material maps derived from Monte Carlo simulations instead of conventional CT-based parameters. This parameter transformation enables accurate dosimetry for implant patients, allowing the treatment system to adapt to previously excluded patient populations while maintaining clinical reliability
Solution Approach 2:
The material map generation approach serves multiple functions: it characterizes implants, characterizes surrounding tissues, and enables dose calculation for diverse patient populations including those with metal implants. This universal method replaces the need for separate handling protocols, expanding treatment availability across all patient types
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate dose calculation for patients with implants, allowing more patients to receive proton therapy by overcoming CT artifact inaccuracies.
Implementation Method 1
Proposed solutions, such as overriding techniques and various metal artifact reduction algorithms, to address these inaccuracies have been time-consuming as well as ineffective in addressing the artifacts. For example, the detailed dimension and material properties of implants are generally not described in surgical notes or otherwise available, so a manual override of Monte Carlo (MC) dose calculation in treatment planning systems could be inaccurate or not feasible.
Implementation Method 2
The method may include receiving particle counting data and representation data for a volume of interest of a patient for one or more sessions. The method may further include determining most probable energy (MPE) using particle energy determined from the particle counting data.
Data Source
AI summary
The disclosure relates to systems and methods for accurate generation of material maps of volume of interests, for example, that include implants, for proton radiation therapy. In one implementation, the method may include determining most probable energy (MPE) using particle energy determined from particle counting data. The method may include generating a plurality of simulations that simulate interactions using different combination of properties for the volume of interest determined from representation data and/or from a database to determine most probable energy (MPE) for each simulation. The method may include comparing the MPE determined using the particle counting data to the MPE determined from each simulation. The method may further include selecting one simulation of the plurality of simulations based on the comparing. The method may also include generating a material map for the volume of interest using the one or more properties corresponding to the one simulation.