Scan Preparation System Using Physiological Motion Prediction
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Solution Overview
Problem
Current medical scanning technologies face challenges in simulating the effect of contrast agent injection on physiological motion and optimizing scan parameters for individual patients, leading to increased radiation exposure and dependence on user experience.
Innovation Solution
A system that uses a processor to predict scan parameters based on pre-scanned physiological motion data and health information, utilizing estimation and recommendation models to automate the optimization of scan settings, reducing radiation dose and improving scan efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scan parameters are set manually by user, then scan preparation can be customized, but it requires extensive user experience and time
Solution Approach 1:
The system automatically determines scan parameters using machine learning models that process patient physiological data, eliminating the need for manual user configuration and extensive user experience while maintaining high accuracy in parameter selection
Solution Approach 2:
The manual expert-based parameter setting process is replaced by an automated machine learning system that uses algorithms to analyze physiological motion data and recommend optimal scan parameters, substituting human expertise with computational intelligence
2Reliability
If scan range and scan times are increased to improve scan success rate, then more comprehensive data is collected, but patient is exposed to unnecessary radiation
Solution Approach 1:
The system dynamically adjusts scan parameters including scan range, scan time, and radiation dose based on predicted physiological motion characteristics from machine learning models, optimizing the balance between scan success rate and radiation exposure by selecting the minimum necessary parameters to achieve diagnostic quality
Solution Approach 2:
The system performs preliminary analysis of patient physiological data using machine learning models to predict motion characteristics before the actual scan, allowing pre-optimization of scan parameters to ensure success while minimizing radiation exposure from the outset rather than requiring increased exposure during the scan
Data Source
AI summary
Systems and methods for scan preparation are provided. The systems may obtain a first parameter set of a subject to be scanned by a medical device acquired before a scan is performed on the subject. The first parameter set may relate to a physiological motion of the subject acquired before the scan is performed on the subject. The systems may predict, based on the first parameter set and an estimation model, a second parameter set of the subject. The second parameter set may relate to the physiological motion of the subject. The systems may determine at least one scan parameter for the scan based at least in part on the second parameter set. The systems may cause the medical device to perform the scan on the subject based on the at least one scan parameter.