Dynamic Acceptance Window for TOF-PET Image Reconstruction
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Solution Overview
Problem
Conventional TOF-PET systems suffer from reduced image quality due to detected events that are not true coincidence events, such as scattered radiation and randoms, which are only removed after reconstruction, consuming processing cycles and decreasing system performance.
Innovation Solution
A method that dynamically adjusts the acceptance window based on a subject's profile to filter out extraneous radiation events prior to reconstruction, using a profile generator to define a dynamic window that localizes positron-electron annihilation events within the region of interest, thereby discarding scatter, randoms, and other extraneous data before image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a constant acceptance window based on bore radius is used in conventional TOF-PET systems, then the system maintains simple operation and consistent processing, but extraneous radiation events (scatter, randoms) are not effectively filtered, leading to reduced image quality and increased processing burden
Solution Approach 1:
The patent applies dynamics by transitioning from a static, constant acceptance window to a dynamic window that adapts to each detected event. The window size and position are calculated in real-time based on the event's location relative to the subject profile, allowing the system to optimize filtering for each event while maintaining operational simplicity through automated calculations.
Solution Approach 2:
The patent implements local quality by applying different acceptance criteria to different spatial locations. Instead of a uniform window across the entire bore, the system calculates customized windows for each detected event based on its specific position relative to the subject profile, ensuring that filtering is optimized locally for each measurement point.
2Measurement precision
If post-reconstruction algorithms are used to remove artifacts, then artifact removal is performed, but processing cycles are consumed and system performance decreases
Solution Approach 1:
The patent applies preliminary action by performing artifact filtering before image reconstruction rather than after. The dynamic acceptance window filters out scatter and random events during data acquisition and preprocessing, so that only clean coincidence events are passed to the reconstruction algorithm, eliminating the need for post-reconstruction artifact removal and preserving system performance.
Solution Approach 2:
The patent extracts harmful elements (scatter and random events) from the dataset before reconstruction by using the dynamic acceptance window to identify and discard extraneous events based on their spatial relationship to the subject profile, leaving only true coincidence events for image reconstruction.
3Quantity of substance
If a larger acceptance window is used to capture more true coincidence events, then counting statistics improve, but more extraneous events are included, reducing image contrast resolution
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the acceptance window parameters (size and position) based on the detected event's location. The window size is calculated as a function of the distance from the event to the subject profile boundary, allowing the system to maximize the window size when few extraneous events are present and minimize it when extraneous events are likely, thereby optimizing both counting statistics and contrast resolution for each event.
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 accelerates the reconstruction process, reduces artifact contamination in images, and simplifies artifact removal algorithms by filtering out unwanted data before reconstruction, leading to improved image quality and enhanced system performance.
Implementation Method 1
The radiation detectors are configured to detect gamma rays produced within the imaging region. Such gamma rays include gamma rays resulting from electron-positron annihilation events
Implementation Method 2
A time difference between the times of each event of each coincident pair is analyzed to localize the positron-electron annihilation event along a LOR between the detectors
Data Source
AI summary
A method of reconstructing time-of-flight (TOF) images includes obtaining a profile of a subject to be imaged in an examination region (14) of an imaging system (10), Events associated with radiation emitted from the subject are detected and converted to electronic data. Electronic data attributable to radiation events located outside the profile are removed and images are reconstructed from the remaining electronic data.