Automatic Patient Fixation Device Registration in CT Imaging
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Solution Overview
Problem
Current radiation treatment planning methods are inefficient and prone to human error, particularly when using patient fixation devices, as they require manual segmentation and optimization, which can lead to overly complex workflows and limited options for patients, and often do not account for collision avoidance.
Innovation Solution
The development of an automatically-registered patient fixation image apparatus and method that uses 3D models of patient fixation devices to segment CT images accurately and efficiently, employing deep learning and non-deep learning algorithms to optimize radiation treatment plans, including collision-free geometry and virtual dry run simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual segmentation and optimization methods are used for radiation treatment planning, then the planning process can be performed with existing tools, but the workflow becomes overly complex and time-consuming
Solution Approach 1:
The system enables automatic segmentation of anatomical structures and fixation devices from CT images using machine learning algorithms, eliminating the need for manual segmentation by physicians. The treatment plan optimization is also automated through iterative computational processes that independently adjust parameters to achieve dose objectives, reducing workflow complexity and improving planning efficiency.
Solution Approach 2:
The patent replaces manual mechanical segmentation processes with automated image processing and machine learning algorithms. The optimization process substitutes iterative manual adjustments with computational algorithms that automatically modify treatment parameters, thereby reducing workflow complexity while maintaining or improving planning quality.
2Measurement precision
If manual segmentation is performed by physicians, then anatomical structures can be identified, but the process is time-consuming and prone to observer variability
Solution Approach 1:
The system performs automatic segmentation of anatomical structures and fixation devices using trained machine learning models that analyze CT images independently, eliminating dependence on manual physician segmentation. This reduces both the time required and observer variability while maintaining consistent accuracy across different cases.
Solution Approach 2:
The patent uses trained machine learning models that have been taught through training datasets to replicate expert segmentation performance. The models create digital copies of anatomical structures and fixation devices from CT images, achieving consistent accuracy without manual intervention and significantly reducing segmentation time.
3Adaptability or versatility
If simple 3-D planning is used, then the planning process is less complex, but the treatment options available to patients are limited
Solution Approach 1:
The automated optimization system independently explores multiple treatment plan configurations by iteratively adjusting parameters such as beam angles, energies, and intensities. This computational self-service capability generates numerous optimized treatment options without requiring complex manual planning procedures, thereby increasing adaptability while keeping the user interface simple.
Solution Approach 2:
The patent implements dynamic optimization where treatment parameters are continuously adjusted through iterative computational processes. The system adaptively modifies beam parameters and treatment configurations based on dose distribution feedback, enabling generation of multiple versatile treatment options without requiring complex static planning methodologies.
4Manufacturing precision
If automated optimization with multiple parameters is used, then treatment plan quality improves, but the computational process becomes time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimal parameter ranges and constraints based on training data and clinical guidelines. During actual treatment planning, the automated optimization process leverages these pre-established parameters to rapidly converge on high-quality solutions, reducing computation time while maintaining optimization quality.
5Reliability
If collision avoidance is not considered in planning, then the optimization process is simpler, but the treatment may result in unsafe conditions
Solution Approach 1:
The system applies preliminary anti-action by pre-defining collision constraints and safety boundaries before optimization begins. The automated optimization process incorporates these pre-established safety constraints to prevent collisions between treatment devices and patient anatomy, ensuring reliable safe treatment delivery without requiring complex real-time monitoring during optimization.
Data Source
AI summary
A three-dimensional model for a patient fixation device that serves to immobilize at least a portion of a particular patient when capturing CT image information of that patient is accessed and then registered with the pixels that correspond to the patient fixation device in the CT image. The model can specify rules of movement for each of a plurality of structural elements that comprise the patient fixation device and that are capable of movement relative to one another. By one approach the aforementioned registration occurs on a part-by-part basis for each of the structural elements. Following registration, the CT image can be automatically segmented.