Liver Perfusion Phase Classification via Plausibility Check
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for classifying medical image data of the liver based on perfusion phases are not robust or accurate enough, particularly in correctly assigning phases for diagnostic purposes, which is crucial for detecting and differentiating liver lesions.
Innovation Solution
A method involving the acquisition of medical image data sets within a short period, using a classification algorithm to correlate image data with defined perfusion phases, and a plausibility check to verify the classification based on metadata and logical order, ensuring accurate and robust phase assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current classification methods are used for medical image data, then the process is simple, but the accuracy and robustness of phase assignment is insufficient
Solution Approach 1:
The classification process is divided into distinct modules: a classification algorithm module that performs initial phase assignment, and a plausibility check module that verifies the results. This segmentation allows each module to specialize in specific tasks, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The plausibility check module provides feedback on the initial classification results by verifying logical consistency of phase assignments across multiple image data sets. This feedback mechanism enables correction of erroneous classifications and ensures robust phase assignment, directly addressing the accuracy requirement.
2Reliability
If multiple image data sets are analyzed to improve classification reliability, then the diagnostic accuracy improves, but the processing time and complexity increase
Solution Approach 1:
The system performs preliminary classification of multiple image data sets using the classification algorithm before conducting the plausibility check. This preliminary action on multiple data sets allows the system to gather sufficient information for reliable phase assignment while enabling subsequent efficient verification through the plausibility check module.
Solution Approach 2:
The system utilizes temporal parameters (time points of image acquisition) and contrast agent concentration parameters across multiple image data sets to perform classification. By analyzing parameter changes across the time series of images, the system achieves reliable phase determination while the plausibility check ensures consistency without requiring excessive processing time.
Data Source
AI summary
A method for classifying medical image data using a classification algorithm configured to make an image-based correlation between an image data set and a phase of a plurality of defined phases relative to a time of administration of a contrast agent with additional plausibility checking. The image data may include at least three image data sets in each case imaging the examination region in each case at a time of capture within a period of less than two hours.