Image Registration Qualification Controller
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Solution Overview
Problem
Current interventional medical procedures face challenges in accurately aligning multi-modal images, such as MRI and ultrasound, due to the lack of effective quality assessment and feedback mechanisms, leading to potential misalignment and poor clinical outcomes.
Innovation Solution
A controller-based system that automatically analyzes the quality of image registration between different modalities using deep learning techniques, providing feedback to users and allowing for adjustments to ensure accurate alignment, thereby reducing operator error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated quality assessment is implemented, then measurement precision of registration quality is improved, but device complexity increases
Solution Approach 1:
The system implements automated feedback by analyzing image registration quality metrics and providing real-time assessments to users. The controller receives registration parameters, computes quality metrics, and feeds back results to guide manual adjustment, creating a closed-loop system that improves measurement precision without requiring complete automation.
Solution Approach 2:
The patent introduces an intermediary quality assessment module that sits between the image registration process and the final fused image output. This intermediary layer analyzes registration accuracy through computed metrics and provides intermediate feedback, allowing the system to maintain complexity at a manageable level while improving measurement precision through dedicated quality evaluation.
2Manufacturing precision
If manual adjustment is required for poor registration, then manufacturing precision can be maintained through expert control, but productivity decreases
Solution Approach 1:
The system provides real-time feedback on registration quality through computed metrics, allowing operators to quickly identify when manual adjustment is needed and how to adjust. This feedback mechanism maintains high precision by guiding expert manual correction while improving productivity by eliminating unnecessary manual adjustments through automated quality assessment.
Solution Approach 2:
The patent performs preliminary quality assessment before the final registration is completed. By evaluating registration metrics in advance and providing feedback, the system allows operators to make targeted adjustments only when necessary, rather than requiring complete manual re-do, thus maintaining precision while improving overall productivity.
3Ease of operation
If no quality feedback is provided, then ease of operation is maintained, but reliability of clinical outcomes deteriorates
Solution Approach 1:
The system implements feedback mechanisms that provide quality metrics and assessments to users during the registration process. This feedback maintains ease of operation by automatically evaluating quality and presenting results in user-friendly formats, while simultaneously improving reliability by ensuring that only adequately registered images proceed to clinical use.
Solution Approach 2:
The patent enables the system to self-assess registration quality through automated quality metrics computation. The system serves itself by evaluating its own output and providing feedback, maintaining operational simplicity for users while ensuring reliable clinical outcomes through built-in quality verification without requiring external expertise for each assessment.
Data Source
AI summary
A controller for qualifying image registration includes a memory that stores instructions; and a processor that executes the instructions. When executed by the processor, the instructions cause the controller to execute a process that includes receiving first imagery of a first modality and receiving second imagery of a second modality. The process executed by the controller also includes registering the first imagery of the first modality to the second imagery of the second modality to obtain an image registration. The image registration is subjected to an automated analysis as a qualifying process to qualify the image registration. The image registration is variably qualified when the image registration passes the qualifying process, and is not qualified when the image registration does not pass the qualifying process.