Patient Dwell Estimation in Diagnostic Imaging
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Solution Overview
Problem
Diagnostic examination devices, such as MRI machines, face challenges in determining a patient's tolerable examination duration, leading to reduced image quality or aborted examinations due to insufficient dwell capability, resulting in additional costs and time for re-examinations.
Innovation Solution
A method involving an observation phase to ascertain measurement parameters like heart rate, respiratory rate, and movement patterns, using sensors and algorithms, including artificial neural networks, to estimate the tolerable examination duration, allowing for adaptive adjustment of examination parameters to ensure optimal image quality within the patient's limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the examination duration is extended to improve image quality, then the signal-to-noise ratio and diagnostic content increase, but the patient's dwell capability is exceeded leading to motion artifacts and examination abortion
Solution Approach 1:
The system performs preliminary monitoring of vital signs (heart rate, respiratory rate, movement patterns) during an observation phase before the actual examination to estimate the patient's dwell capability in advance. This allows the examination protocol to be adapted to match the patient's actual tolerance, preventing motion artifacts while maximizing image quality within the tolerable duration.
Solution Approach 2:
The examination duration and protocol are dynamically adjusted based on real-time monitoring of the patient's vital signs and estimated dwell capability. The system continuously adapts the examination parameters to match the patient's physiological state, allowing extension when the patient can tolerate longer durations and reduction when motion artifacts begin to appear.
2Reliability
If the backup strategy is applied to record a lower quality version first, then the examination can be completed, but valuable dwell capability time is wasted that could have been used for higher resolution recording
Solution Approach 1:
The system performs preliminary monitoring of vital signs (heart rate, respiratory rate, movement patterns) during an observation phase before the actual examination to estimate the patient's dwell capability in advance. This allows the examination protocol to be adapted to match the patient's actual tolerance, preventing motion artifacts while maximizing image quality within the tolerable duration.
Solution Approach 2:
The examination protocol parameters (duration, resolution, number of contrasts) are changed and optimized based on the estimated dwell capability. Instead of using a fixed backup strategy, the system adjusts the examination parameters to match the patient's actual tolerance, ensuring both completion and optimal quality without wasting dwell capability time.
3Ease of manufacture
If the examination protocol is fixed in advance, then the planning is simple, but it cannot adapt to individual patient variations in dwell capability leading to suboptimal image quality or aborted examinations
Solution Approach 1:
The system performs preliminary monitoring of vital signs (heart rate, respiratory rate, movement patterns) during an observation phase before the actual examination to estimate the patient's dwell capability in advance. This allows the examination protocol to be adapted to match the patient's actual tolerance, preventing motion artifacts while maximizing image quality within the tolerable duration.
Solution Approach 2:
The system uses feedback from real-time monitoring of vital signs during the examination to continuously adjust the protocol. The monitoring data provides feedback about the patient's actual dwell capability, allowing dynamic adaptation of examination parameters to maintain optimal image quality throughout the procedure.
Data Source
AI summary
In a method and apparatus for determining an examination duration tolerable by a patient in and/or on a diagnostic examination device, the patient to be examined is observed at least in a preliminary stage of the examination concerned, during which measurement parameters are ascertained. From the measurement parameters, an algorithm determines a statement about the dwell capability of the patient in the examination device. The algorithm can be an artificial neural network.

