CT Scanner Noise Map Dose Optimization
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Solution Overview
Problem
Current CT imaging protocols fail to provide patient-specific and application-dependent image quality optimization, leading to inaccurate radiation dose delivery and suboptimal image quality due to inadequate consideration of noise in tissues and varying organ exposure during CT scans.
Innovation Solution
A method that uses a pre-scan to obtain noise and organ-specific data to adjust the initial radiation dose, generating adjusted exposure factors through AEC systems, incorporating noise maps and organ masks to optimize image quality and radiation distribution during CT scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If radiation dose is reduced to improve radiation safety, then patient exposure to ionizing radiation decreases, but image quality deteriorates due to increased pixel noise and quantum noise
Solution Approach 1:
The system performs a pre-scan to obtain noise maps and organ masks before the actual CT scan. These preliminary data are used to calculate patient-specific AEC correction factors that predict image quality and optimize radiation dose distribution in advance, allowing dose reduction without compromising image quality
Solution Approach 2:
The patent applies spatially-varying AEC correction factors to different regions of the patient's body based on local noise characteristics and organ sensitivity. Each region receives a customized radiation dose adjustment, allowing dose reduction in areas where image quality is less critical while maintaining adequate dose in regions requiring high image quality
Solution Approach 3:
The system dynamically adjusts scan acquisition parameters including tube current, tube voltage, and pitch based on patient-specific anatomy and the predicted image quality requirements for different regions. These parameter changes optimize the radiation dose-image quality balance for each patient and imaging task
2Ease of operation
If generalized imaging protocols are used to simplify workflow, then ease of operation improves, but adaptability to patient-specific and application-dependent needs deteriorates
Solution Approach 1:
The system automatically performs patient-specific protocol optimization without requiring manual clinician intervention. The AEC system self-adjusts imaging parameters based on pre-scan data, patient anatomy, and task-specific requirements, eliminating the need for manual protocol customization while achieving personalized imaging
Solution Approach 2:
The pre-scan phase automatically collects patient-specific anatomical information and generates noise maps and organ masks before the main scan. This preliminary characterization enables the system to automatically select and customize imaging protocols without requiring manual intervention during the actual scanning process
3Ease of operation
If AEC is used to automatically optimize exposure, then ease of operation improves by simplifying workflow, but measurement precision deteriorates due to inaccurate determination of AEC leading to inaccurate dose delivery
Solution Approach 1:
The system uses pre-scan data to provide feedback about patient-specific noise characteristics and organ locations. This feedback loop enables the AEC system to accurately predict image quality and adjust radiation dose parameters accordingly, improving dose accuracy while maintaining automatic operation
Solution Approach 2:
The AEC system dynamically changes multiple scan parameters including tube current, tube voltage, and pitch based on patient-specific measurements from the pre-scan. These coordinated parameter changes optimize the radiation dose delivery accuracy for each patient's unique anatomy and imaging requirements
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
This approach allows for precise control of radiation dose and improved image quality by accounting for spatially-distributed noise and organ sensitivity, reducing patient exposure while maintaining clinically acceptable image quality.
Implementation Method 1
a transmitter configured to emit X-rays during CT scans
Data Source
AI summary
A method, apparatus, and computer-readable storage medium for controlling exposure/irradiation during a main three-dimensional X-ray imaging scan using at least one spatially-distributed characteristic of a pre-scan/scout scan preceding the main scan. The at least one spatially-distributed characteristic includes (1) a spatially-distributed noise characteristic of the pre-scan and/or (2) a spatially-distributed identification of exposure-sensitive tissue types. The at least one spatially-distributed characteristic can be calculated from images reconstructed from sinogram/projection data and/or from sinogram/projection directly using a neural network.