Radiation Tomographic Imaging Motion Artifact Detection
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Solution Overview
Problem
Radiation tomographic imaging is hindered by rapid and uncontrollable motions, such as peristaltic motion of intestines or movement of bubbles and liquids within the intestinal tract, which result in difficult-to-detect artifacts, often requiring re-imaging and can go unnoticed during initial image checks.
Innovation Solution
A method and apparatus for radiation tomographic imaging that involves a helical scan data collection, reconstructing images at multiple times using weighted data, fragmenting images into local-region images, identifying differences between these images, and notifying operators of significant motion, allowing for re-scanning and improved image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If helical scan is used for radiation tomographic imaging, then imaging speed and productivity are improved, but motion artifacts from rapid intestinal peristalsis and liquid/bubble movement become more problematic and harder to detect
Solution Approach 1:
The patent divides the imaging process into multiple temporal phases by reconstructing images at different time points during the helical scan. This segmentation allows identification of motion artifacts by comparing images from different time points, thereby maintaining high imaging speed while improving reliability through temporal comparison.
Solution Approach 2:
The patent performs preliminary image reconstruction at multiple time points during data collection, before final image production. This preliminary action enables detection of motion artifacts early in the process, allowing for corrective measures to be taken before final imaging completion, thus maintaining both speed and quality.
2Measurement precision
If multiple images are reconstructed at different times using weighted data, then motion detection capability is improved, but data processing complexity and device complexity increase
Solution Approach 1:
The patent applies different weighting factors to different data points based on their temporal proximity to the reference time point. This local quality approach optimizes motion detection by emphasizing relevant temporal information while minimizing processing of less relevant data, thus improving detection accuracy without proportionally increasing complexity.
Solution Approach 2:
The patent reconstructs images at multiple time points with different weighting schemes, performing more processing than a single static image would require. This partial action approach focuses computational resources on critical temporal comparisons, achieving superior motion detection while managing complexity through selective processing.
3Measurement precision
If local-region image fragmentation is performed for motion detection, then artifact detection capability is improved, but image processing time and loss of time increase
Solution Approach 1:
The patent fragments images into local regions to enable targeted motion detection in specific anatomical areas. This segmentation allows parallel processing of different regions and focuses computational effort only where motion artifacts are likely to occur, improving detection accuracy while minimizing overall processing time through efficient resource allocation.
4Reliability
If difference identification among local-region images is performed, then motion artifact detection is improved, but computational load and device complexity increase
Solution Approach 1:
The patent performs difference identification specifically on local-region images rather than entire images. This local quality approach concentrates computational resources on regions where motion artifacts are most likely to occur, improving motion detection reliability while reducing overall computational load by avoiding unnecessary processing of static regions.
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
Effectively detects local rapid motion during imaging, reduces artifacts, and facilitates efficient re-imaging by identifying motion-related issues and providing protocols for improved image acquisition, thereby enhancing diagnostic accuracy.
Implementation Method 1
said first reconstructing step applies said weighting using a weight function that has a relatively small weight for a region with a relatively large cone angle of a radiation path and a relatively large weight for a region with a relatively small cone angle of the radiation path
Implementation Method 2
a controlling step of controlling a data collection subsystem comprising a multi-slice detector to collect data for a subject by performing a helical scan
Data Source
AI summary
To detect motion in a subject, particularly, local rapid motion, during radiation tomographic imaging. There is provided a radiation tomographic imaging method causing a computer to execute: a controlling step of controlling a data collection subsystem comprising a multi-slice detector to collect data by performing a helical scan on a subject; a first reconstructing step of reconstructing a plurality of tomographic images at an identical slice position and at different times using data obtained by applying weighting to the collected data according to the slice position, the reconstruction being performed for a plurality of slice positions; a fragmenting step of fragmenting each of the plurality of tomographic images at each of the plurality of slice positions into a respective plurality of local-region images; and a difference identifying step of, for each combination of a plurality of local-region images at the same position and at different times, identifying a difference among local-region images in the combination.