CT Reconstruction Quality Control Through Contrast-Block Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
CT scanners face challenges in accurately recognizing when calibration is needed due to factors altering physical component alignment, leading to distorted or blurry reconstructed images, especially in automated systems with limited user oversight.
Innovation Solution
A computer-implemented method for reconstruction quality control that determines air and material densities from a reconstructed volume, subdivides it into blocks, identifies contrast blocks, and assesses reconstruction quality based on the number of contrast blocks, sending alerts if the quality is below a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated CT scanning is used to improve productivity, then scanning efficiency increases, but the ability to detect calibration issues deteriorates
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing reconstructed volumes to detect calibration issues. The quality control method evaluates density distributions and contrast block counts without requiring external intervention, enabling the system to monitor its own calibration status and trigger alerts when degradation is detected.
Solution Approach 2:
The system implements continuous feedback by analyzing reconstructed volumes and providing real-time quality assessments. The method calculates statistical parameters from density distributions and compares them against threshold values, automatically generating alerts when calibration degradation is detected, thus creating a closed-loop monitoring system.
2Manufacturing precision
If manual quality control is performed to ensure image quality, then reconstruction accuracy improves, but operational complexity and time consumption increase
Solution Approach 1:
The patent replaces manual visual inspection with automated computational analysis. The quality control method uses algorithms to calculate density distributions, identify contrast blocks, and assess reconstruction quality objectively, eliminating the need for operator judgment and reducing subjectivity in quality assessment.
Solution Approach 2:
The system transforms subjective quality assessment into objective parameter measurement. By converting image quality into quantifiable parameters such as mean density, standard deviation, contrast block count, and signal-to-noise ratio, the method enables automated decision-making and removes variability associated with manual evaluation.
3Manufacturing precision
If calibration is performed frequently to maintain image quality, then reconstruction accuracy improves, but time consumption and productivity decrease
Solution Approach 1:
The system performs preliminary quality assessment on reconstructed volumes before they are used for diagnosis or analysis. By evaluating density distributions and contrast characteristics immediately after reconstruction, the method identifies calibration issues early, allowing for timely recalibration without delaying patient scans or workflow.
Solution Approach 2:
The continuous quality monitoring system provides real-time feedback on calibration status through automated analysis of reconstructed volumes. This enables proactive calibration maintenance, where the system monitors calibration health continuously and triggers alerts or recalibration events only when degradation is detected, rather than requiring frequent preventive maintenance.
Data Source
AI summary
A computer-implemented method, system, and instructions can include receiving a reconstructed volume of an object; determining an air density and a material density from the reconstructed volume of the object; subdividing the reconstructed volume into one or more blocks, each block comprising one or more voxels; determining one or more contrast blocks within the reconstructed volume; and determining a reconstruction quality based on the number of contrast blocks in the reconstructed volume.