Breathing Compliance Monitoring for Real-Time Imaging Control

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Solution Overview

Problem

Existing medical imaging protocols face challenges in monitoring and controlling patient compliance with desired breathing and/or breath-holding patterns, leading to motion artifacts and inefficient use of imaging devices due to delayed detection of non-compliance.

Innovation Solution

A computer-implemented method using trained machine learning models to automatically select a compliance class based on breathing information, allowing real-time adjustment or abortion of imaging sequences, and providing non-compliance information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual evaluation of breathing information is used to determine patient compliance, then compliance assessment can be performed, but it is time-consuming and only allows determination after the imaging procedure is finished

Engineering Contradiction:

Improvecompliance assessment accuracyVSAvoidtime delay in compliance detection

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the mechanical/manual evaluation system with an automated computer-implemented system using machine learning models. The trained machine learning model automatically analyzes breathing information (e.g., breathing rate, depth, pattern) in real-time during the imaging procedure, eliminating the need for time-consuming manual assessment by medical personnel and enabling immediate compliance determination.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-monitoring of patient compliance through automated detection. The machine learning model continuously processes breathing data from sensors and autonomously determines compliance status without requiring external manual intervention, allowing the system to serve itself in monitoring patient adherence to breathing instructions throughout the procedure.

Inventive Principle:

Principle #25Self-service

2Reliability

If motion artifacts are detected only in final image data analysis, then compliance issues can be identified, but it leads to extended imaging time and lower device utilization

Engineering Contradiction:

Improveimage quality assuranceVSAvoidimaging device utilization

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent performs preliminary compliance monitoring during the imaging procedure itself rather than waiting for post-processing analysis. The machine learning model continuously evaluates breathing information in real-time, enabling early detection of compliance issues before they degrade image quality, so that corrective actions can be taken immediately during the procedure.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements real-time feedback by continuously monitoring breathing parameters and providing immediate compliance status information to medical personnel. This feedback loop allows for instantaneous detection of breathing pattern deviations and enables prompt intervention (such as pausing or repeating sequences) to maintain image quality without extending overall imaging time.

Inventive Principle:

Principle #23Feedback

3Manufacturing precision

If repeat imaging sequences are performed due to non-compliance detection, then satisfactory image quality can be achieved, but it increases total imaging time and patient burden

Engineering Contradiction:

Improveimaging qualityVSAvoidtotal imaging procedure time

Core Design Contradiction:

Manufacturing precisionVSDuration of action of moving object

Solution Approach 1:

The system performs preliminary compliance verification before initiating imaging sequences and during acquisition. By ensuring patient compliance is confirmed in advance through real-time breathing monitoring, the system minimizes the need for repeat sequences, thereby reducing total imaging time while maintaining image quality standards.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Real-time feedback on compliance status enables immediate corrective actions during the procedure. When non-compliance is detected, the system can prompt the patient to adjust breathing or pause the sequence, preventing degradation of image quality and avoiding the need for time-consuming repeat acquisitions after the procedure has concluded.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250261921A1Method for monitoring and/or controlling a medical imaging procedure and methods for providing trained machine learning models

Publication Date: 2025.08.21 SIEMENS HEALTHINEERS AG

AI summary

A computer-implemented method for monitoring and/or controlling a medical imaging procedure on a patient includes receiving breathing information concerning a breathing pattern of the patient and selecting a compliance class from at least two possible compliance classes based on the breathing information, wherein at least one of the possible compliance classes corresponds to a compliance of the acquired breathing information with a given desired breathing and/or breath-holding pattern. The method further includes (1) controlling the medical imaging procedure depending on the selected compliance class and/or (2) outputting non-compliance information to a user and/or storing the non-compliance information with an acquired medical image data when the selected compliance class does not indicate a compliance of the acquired breathing information with the given desired breathing and/or breath-holding pattern.