Medical Imaging Parameter Control for Lower Radiation Exposure
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Solution Overview
Problem
Medical imaging devices emit harmful radiation, leading to excessive exposure for patients and nearby personnel, and inefficient power consumption due to iterative imaging and misconfiguration, which can cause health issues and device overheating.
Innovation Solution
A machine learning model is trained using patient-specific data and device parameters to optimize imaging settings, reducing radiation exposure and power consumption by improving image capture efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative manual imaging is used to achieve desired image quality, then image quality is improved, but radiation exposure increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring optimal imaging parameters based on patient data (height, weight, body habitus) and anatomical region information before actual image capture. This preliminary configuration eliminates the need for iterative manual adjustments, achieving desired image quality in fewer exposures and thereby reducing radiation exposure to both patients and personnel.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring imaging outcomes and adjusting parameters based on real-time performance data. The machine learning model learns from accumulated imaging results and patient responses, refining parameter recommendations to maintain optimal image quality while minimizing radiation exposure through data-driven adjustments.
2Measurement precision
If exposure time is extended to improve image quality, then measurement precision is improved, but harmful radiation exposure increases
Solution Approach 1:
The system applies parameter changes by optimizing multiple imaging parameters simultaneously (kVp, mAs, pulse width, frame rate) rather than relying on extended exposure time. The machine learning model determines the optimal combination of parameters based on patient-specific characteristics, achieving superior image quality through coordinated parameter adjustment while minimizing total radiation exposure by avoiding prolonged exposure times.
3Productivity
If medical imaging devices operate at high power to reduce scanning time, then productivity is improved, but heat generation and energy consumption increase
Solution Approach 1:
The system employs periodic action by using pulsed imaging modes with optimized pulse widths and frame rates instead of continuous high-power operation. The machine learning model determines optimal pulsing patterns that maintain high productivity by capturing necessary images at appropriate intervals while allowing cooling periods between pulses, thereby reducing cumulative heat generation and energy consumption while preserving scanning efficiency.
4Reliability
If multiple images are captured to ensure quality, then reliability is improved, but radiation exposure and power consumption increase
Solution Approach 1:
The system performs preliminary action by pre-calculating the optimal number and type of images needed based on patient characteristics and clinical requirements before scanning begins. This preliminary planning ensures that the minimum necessary images are captured with correct parameters from the start, eliminating redundant imaging and thereby reducing both power consumption and radiation exposure while maintaining reliability through targeted quality assurance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Significantly reduces radiation exposure and heat generation, enhancing device performance and longevity while maintaining image quality.
Implementation Method 1
A machine learning model is trained using patient-specific data and device parameters to optimize imaging settings, reducing radiation exposure and power consumption by improving image capture efficiency.
Data Source
AI summary
Methods, systems, and apparatuses are described herein for using processes and machine learning techniques to optimize medical imaging processes to reduce inadvertent exposure to harmful radiation. A machine learning model may be trained to output recommended medical imaging device operating parameter settings. Available operating parameters of a medical imaging device may be determined, and patient data may be received. The patient data and the available operating parameters may be used as input to the trained machine learning model, which might output recommended operating parameter settings. In turn, this output in addition to other calculations might be used to transmit, to the medical imaging device, data that causes modification of the operating parameters of the medical imaging device. Metadata corresponding to one or more images captured by the medical imaging device may be received, and the trained machine learning model might be further trained based on that metadata.