Medical Imaging Comparison Data for Early Anomaly Detection

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Solution Overview

Problem

Existing medical imaging technologies struggle to identify deviations or anomalies in measurement data comprehensively and efficiently, often requiring image reconstruction and additional computing resources, and are not universally applicable across various situations.

Innovation Solution

A method utilizing a trained function, such as a neural network, to generate expected measurement data based on output information, allowing for early and comprehensive identification of deviations by comparing expected and actual measurement data, and providing comparison information to adjust imaging parameters and protocols.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If image reconstruction is performed to identify deviations or anomalies, then measurement precision is improved, but loss of time and computing resources increase

Engineering Contradiction:

Improveidentification accuracy of deviationsVSAvoidtime for image reconstruction

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies preliminary action by generating expected measurement data before the actual measurement is completed. The trained function predicts what the measurement data should look like based on output information, allowing deviation detection to start before full image reconstruction is finished. This enables early identification of anomalies without waiting for complete reconstruction.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary approach by using a trained function as a mediator between the measurement data and the deviation detection process. Instead of directly reconstructing images and then detecting deviations, the trained function processes output information to generate expected measurement data, which serves as an intermediate representation for comparison and anomaly detection.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If specific methods are used to identify artifacts or system errors, then reliability is improved for specific types of deviations, but adaptability deteriorates

Engineering Contradiction:

Improvedetection accuracy for specific anomaliesVSAvoidapplicability across different situations

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent applies universality by developing a trained function that can handle multiple types of deviations and anomalies across different imaging situations. The function is trained on diverse data and can generate expected measurement data for various scenarios, making it adaptable to different anomaly types without requiring separate specialized methods for each situation.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent utilizes parameter changes by adjusting the trained function's input parameters (output information from the imaging apparatus) to adapt to different imaging scenarios. The function processes varying output information and generates corresponding expected measurement data, allowing the same underlying method to detect different types of deviations by changing input parameters rather than requiring different detection methods.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If comprehensive deviation detection is implemented, then reliability is improved, but device complexity increases

Engineering Contradiction:

Improvecomprehensive anomaly detectionVSAvoidcomputational complexity of deviation identification

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies mechanics substitution by replacing complex mechanical image reconstruction processes with a computational trained function. Instead of performing full physical image reconstruction and analysis, the system uses a trained function that processes output information directly to generate expected measurement data, substituting complex computational mechanics with a learned mathematical model that achieves comprehensive deviation detection more efficiently.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260080541A1Methods for providing an item of comparison information for a medical imaging apparatus

Publication Date: 2026.03.19 SIEMENS HEALTHINEERS AG

AI summary

One or more example embodiments relates to a method for providing an item of comparison information based on an item of output information for a medical imaging apparatus. In addition, one or more example embodiments relates to a computing unit, a medical imaging apparatus, a computer program product and a computer storage medium. The computer-implemented method for providing an item of comparison information based on an item of output information for a medical imaging apparatus, comprises generating expected measurement data via a trained function based on the item of output information, providing measurement data of a medical imaging examination based on the items of output information, ascertaining an item of comparison information based on the expected measurement data and the measurement data, and providing the item of comparison information.