Medical Image Processing Device for Radiation Alignment Accuracy
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Solution Overview
Problem
Current radiation treatment methods rely on visual confirmation for aligning patient position, which is subjective and prone to errors due to the transparency of tumors in fluoroscopic images, leading to potential variations in treatment effectiveness based on the user's ability and difficulty in accurately confirming tumor positions.
Innovation Solution
A medical image processing device that acquires and processes fluoroscopic images to calculate differential statistical quantities, applying virtual perturbations to align patient positions accurately and quantify alignment errors, enabling precise movement control of the patient table for consistent radiation treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual confirmation method is used for aligning patient position, then the process is simple and easy to operate, but the measurement precision and reliability are low due to user-dependent variability and tumor transparency in fluoroscopic images
Solution Approach 1:
The patent replaces the manual visual confirmation method with an automated image processing system that calculates differential statistical quantities between fluoroscopic images and DRR images. This substitution eliminates human subjectivity and provides objective, quantitative alignment assessment through computer-based image collation and statistical analysis.
Solution Approach 2:
The patent introduces differential statistical quantity calculation as an intermediary measure between the fluoroscopic image and the final alignment determination. This intermediary provides a quantitative metric that objectively assesses alignment accuracy, serving as a bridge between the visual input and the alignment outcome.
2Loss of time
If fluoroscopic image is used for alignment confirmation, then the process is quick and simple, but the measurement precision is insufficient because tumors are transparent to X-rays and not clearly shown
Solution Approach 1:
The patent uses differential statistical quantity calculation as an intermediary that enables tumor position assessment without requiring direct visual confirmation. This intermediary metric allows the system to quantify alignment accuracy even when tumors are not clearly visible in the fluoroscopic image.
Solution Approach 2:
The patent implements a feedback mechanism where the calculated differential statistical quantity is used to assess alignment accuracy and determine whether re-alignment is necessary. This feedback loop provides objective criteria for treatment progression without requiring additional time-consuming visual confirmation steps.
3Measurement precision
If automated image collation is performed between CT images, then the measurement precision and reliability are improved, but the device complexity and calculation requirements increase
Solution Approach 1:
The patent extracts only the essential comparison metric (differential statistical quantity) from the full image collation process. By focusing on this single quantitative measure rather than comprehensive image analysis, the system achieves reliable alignment assessment with reduced computational complexity and simpler implementation.
Solution Approach 2:
The patent changes the parameter being measured from qualitative visual assessment to a quantitative differential statistical quantity. This parameter transformation enables automated processing while maintaining measurement precision, as the quantitative metric can be calculated and compared without complex image manipulation.
4Ease of operation
If user performs visual confirmation by superimposing images, then the process is simple, but the reliability varies depending on user ability and subjective judgment
Solution Approach 1:
The patent replaces the human visual confirmation process with an automated calculation system that objectively computes differential statistical quantities. This substitution eliminates variability in user ability and provides consistent, reliable alignment assessment across different operators.
Solution Approach 2:
The system performs self-assessment of alignment accuracy through automated calculation of differential statistical quantities, eliminating the need for human judgment. The system serves itself by objectively determining whether alignment requirements are met based on the calculated metric.
Data Source
AI summary
According to an embodiment, a medical image processing device includes a first image acquirer, a second image acquirer, a treatment error acquirer, a difference calculator, and a differential statistical quantity calculator. The first image acquirer acquires a first fluoroscopic image of a patient. The second image acquirer acquires a second fluoroscopic image photographed at a timing different from the first fluoroscopic image. The treatment error acquirer acquires a treatment error occurring when an alignment process is performed or a treatment error occurring in treatment. The difference calculator calculates a difference image between the second fluoroscopic image to which the virtual perturbation is applied at the position of the patient shown therein based on the treatment error and the first fluoroscopic image. The differential statistical quantity calculator calculates a statistical quantity of a difference between the first fluoroscopic image and the second fluoroscopic image based on the difference image.