CT Scout Scan High Contrast Object Detection
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Solution Overview
Problem
Current CT imaging systems lack a method to detect high contrast or highly attenuating objects during scout scans, which can lead to artifacts in reconstructed images and are difficult to identify, especially in busy medical centers.
Innovation Solution
A computer-implemented method using a processor to generate images from CT scanner data, perform object segmentation based on pixel connectivity, characterize segmented regions, and determine the presence of high contrast objects, displaying alerts and recommendations for operators to address detected objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scout scan is performed to identify region of interest, then scan preparation is improved, but high contrast objects cannot be detected leading to artifacts in reconstructed images
Solution Approach 1:
The system performs a preliminary scout scan before the actual diagnostic scan to detect high contrast objects. This preliminary action identifies potential artifact sources in advance, allowing operators to take corrective measures before the main scan, thus preventing harmful artifacts while maintaining reliable scan preparation
Solution Approach 2:
The scout scan acts as an intermediary step between patient preparation and the main diagnostic scan. It serves as a mediator that detects high contrast objects without being the primary diagnostic tool, enabling artifact prevention while preserving the reliability of the main scanning process
2Ease of operation
If manual inspection of scout scan is performed, then high contrast objects may be identified, but small contrast drops are difficult to detect and procedures are difficult to follow in busy medical centers
Solution Approach 1:
The system replaces manual visual inspection with an automated computer-based detection algorithm. This substitution of mechanical/manual detection with an automated image processing system improves both ease of operation (operators simply initiate the automated process) and measurement precision (the algorithm can detect small contrast drops that are difficult for human operators to identify)
Solution Approach 2:
The scout scan detection system performs self-service by automatically analyzing the scout scan images to identify high contrast objects. The system independently executes the detection process without requiring manual intervention, making the procedure easier to operate while maintaining high detection accuracy through automated image analysis algorithms
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
The method effectively identifies and alerts operators to high contrast objects, ensuring accurate scans by preventing artifacts and improving clinical workflow robustness by integrating hardware and software for efficient detection and analysis.
Implementation Method 1
In computed tomography (CT), X-ray radiation spans an object or a subject of interest being scanned
Implementation Method 2
In digital X-ray systems a photodetector produces signals representative of the amount or intensity of radiation impacting discrete pixel regions of a detector surface
Implementation Method 3
In CT imaging systems a detector array, including a series of detector elements or sensors, produces similar signals through various positions as a gantry is displaced around a subject or object being imaged, allowing volumetric image reconstructions to be obtained
Data Source
AI summary
A computer-implemented method includes generating an image from scan data acquired during a non-diagnostic scan utilizing a computed tomography scanner. The computer-implemented method also includes performing object segmentation based on pixel connectivity on the image to generate segmented regions. The computer-implemented method further includes characterizing pixel connectivity properties of the segmented regions. The computer-implemented method still further includes obtaining respective bounding shape coordinates and area for bounding shapes of the segmented regions.