Automated CED Treatment Plan Optimization Algorithm
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Solution Overview
Problem
Current methods for optimizing convection enhanced delivery (CED) treatment plans after tumor resection lack precision in targeting and distributing therapeutic agents within the brain tissue, often relying on general guidelines that do not account for individual anatomical variations, leading to suboptimal infusion setups and potential side effects.
Innovation Solution
A method and system that utilize algorithms for calculating optimal packing of spheres or cylinders to determine infusion parameters, such as flow rate, pressure, and catheter placement, based on convection and diffusion parameters, to ensure precise coverage of the target volume, incorporating patient-specific anatomical data and allowing for manual or automatic adjustment of parameters to optimize fluid distribution and minimize side effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional safety-range estimation methods are used to determine infusion target, then the treatment plan can be established with general guidelines, but the targeting precision is insufficient and does not account for individual anatomical variations
Solution Approach 1:
The system performs preliminary simulation of fluid distribution patterns before actual catheter placement. By calculating predicted diffusion and convection patterns in advance, the system allows physicians to optimize catheter positions and infusion parameters virtually, thereby improving targeting precision while reducing the complexity of the actual surgical procedure.
Solution Approach 2:
The system creates a virtual copy of the patient's anatomical structure using medical images, allowing simulation and optimization of treatment plans in this digital replica. This virtual model enables precise targeting calculations without directly complicating the physical surgical procedure, as optimizations are first validated in the simulated environment.
2Reliability
If multiple catheters are used to ensure adequate coverage of target volume, then the coverage completeness is improved, but the number of invasive procedures and treatment complexity increases
Solution Approach 1:
The system calculates the optimal number of catheters needed by simulating fluid distribution patterns, allowing for partial coverage with fewer catheters when sufficient, or excessive coverage with more catheters only when necessary. This optimization ensures adequate target coverage while minimizing the number of invasive catheter placements required.
Solution Approach 2:
The system determines that different regions of the target volume may require different levels of coverage intensity. By analyzing the anatomical structure and target characteristics, the system optimizes catheter placement to provide enhanced coverage in critical areas while maintaining adequate coverage elsewhere, thereby reducing the overall number of catheters needed while ensuring reliability.
3Reliability
If infusion parameters are optimized for maximum target coverage, then the therapeutic effectiveness is improved, but the risk of side effects from fluid distribution in non-target areas increases
Solution Approach 1:
The system optimizes infusion parameters such as flow rate, pressure, and duration by simulating different scenarios. By adjusting these parameters in the virtual model, the system identifies settings that maximize target area coverage while minimizing fluid extravasation into non-target regions, thereby improving therapeutic effectiveness while reducing side effects.
Solution Approach 2:
The simulation provides feedback on predicted fluid distribution patterns, allowing the system to identify potential side effect risks before actual treatment. This feedback mechanism enables refinement of catheter placement and infusion parameters to achieve better target coverage while avoiding harmful fluid distribution in adjacent structures.
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
This approach enables more precise and effective delivery of therapeutic agents, reducing the number of catheters needed and minimizing invasive procedures, while ensuring thorough coverage of the target area and minimizing risks by optimizing fluid distribution and catheter placement based on individual anatomical structures.
Implementation Method 1
Calculating the geometrical shape can include, for example, using at least one of flow rate, pressure head, infusion time, catheter radius, catheter type, tissue properties as the convention related parameters
Implementation Method 2
Calculating the geometrical shape can include using at least one of time, diffusivity, infusion time, concentration gradients, catheter radius, infusate properties, catheter type, catheter number, tissue properties as the diffusion related properties
Data Source
AI summary
A method of adjusting infusion parameters that provide coverage of a selected target volume for direct infusions of a fluid includes using an algorithm for calculation of optimal packing of spheres or cylinders in a selected volume to determine the coverage of the selected target volume.