Medical Image Evaluation Device for Time-Series Registration
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Solution Overview
Problem
Current methods for comparing medical images across multiple time points are error-prone and time-consuming, especially when dealing with lesions or tumors, as radiologists manually select and document changes, which is inefficient and prone to errors.
Innovation Solution
A method and system that retrieves relevant medical images from a database based on user-defined evaluation parameters, registers them to create a composite evaluation image dataset, and outputs this dataset for time-series analysis, facilitating efficient comparison and reporting without requiring changes to existing image databases or viewers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison of medical images across multiple time points is performed, then radiologists can assess medical conditions over time, but the process is error-prone and time-consuming
Solution Approach 1:
The system automatically retrieves and pre-processes historical images before the radiologist performs assessment. The evaluation device proactively compiles relevant images from different time points, applies registration algorithms to align them, and organizes them into composite datasets, eliminating the need for manual image collection and preparation while ensuring consistent alignment for accurate comparison
Solution Approach 2:
The evaluation device acts as an intermediary between the image database and the radiologist. It automatically retrieves images, performs registration to correct misalignments, generates composite evaluation datasets, and presents them in a standardized format. This intermediary processing ensures accurate temporal comparison while reducing radiologist workload and time investment
2Reliability
If manual selection and documentation of lesions across multiple time points is performed, then medical conditions can be assessed, but the process is error-prone
Solution Approach 1:
The evaluation device serves as an intermediary that automatically retrieves images, performs registration, and generates composite datasets with consistent formatting. This automated intermediary process eliminates manual errors in image selection and documentation while maintaining high productivity by delivering ready-to-assess datasets to radiologists
Solution Approach 2:
The system replaces the manual mechanical process of image selection, alignment, and documentation with automated computational processes. Image retrieval, registration algorithms, and dataset compilation are performed automatically by the evaluation device, eliminating human errors while maintaining efficient workflow and high productivity
3Adaptability or versatility
If images from different institutions and modalities are integrated, then comprehensive evaluation is possible, but system complexity increases
Solution Approach 1:
The evaluation device is designed with universal functionality to handle images from multiple institutions and imaging modalities. It implements standardized protocols for image retrieval, registration, and composite dataset generation that work across diverse data sources, enabling comprehensive evaluation without requiring separate systems for different institutions or modalities
Solution Approach 2:
The system employs parameter-based registration and normalization techniques that adapt to different imaging modalities and institutions. By adjusting registration parameters and transformation models based on image characteristics, the evaluation device achieves consistent integration across diverse data sources while maintaining manageable system complexity through standardized processing pipelines
Data Source
AI summary
A method for controlling an evaluation system for medical images of a patient is proposed. Medical images of the patient acquired at different time points are stored in an image database. Upon reception of a user command associated with a time-dependent evaluation command including at least one evaluation parameter describing an evaluation to be performed and identification information describing the patient, medical images of the patient fulfilling a relevancy criterion depending on the evaluation parameter and acquired at different time points are retrieved from the image database. The retrieved images are registered to each other in at least one registration process. Finally, a composite evaluation image data set including a time series of evaluation images is derived from the registered retrieved images and output to the user.