TOF Mask Random Estimation in PET Sinogram Processing
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Solution Overview
Problem
In positron emission tomography (PET) systems, random coincidences due to finite coincidence windows introduce substantial errors in image reconstruction, especially with the use of time-of-flight (TOF) masks, which alter the distribution of random events, making existing random estimation methods ineffective.
Innovation Solution
A novel method for estimating random events in TOF list-mode reconstruction involves obtaining TOF list-mode data, converting it into 4D raw sinogram count data, interpolating, low-pass filtering, and generating 5D TOF raw sinogram data using a specific equation that accounts for the TOF mask's effect without actual filtering, ensuring uniform random distribution along the tangential dimension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a TOF mask is applied to filter random coincidences, then image quality is improved by removing out-of-FOV random events, but the random distribution is distorted with more events in central regions and fewer at edges
Solution Approach 1:
The patent changes the parameter of random event distribution by applying a TOF mask that selectively filters random coincidences based on their time-of-flight values. This creates a non-uniform distribution where central regions have more random events and edge regions have fewer, optimizing the balance between noise reduction and signal preservation in different spatial locations.
Solution Approach 2:
The TOF mask implements local quality by applying different filtering criteria to different spatial regions. Central FOV regions retain more random events while edge regions have stricter filtering, creating a spatially varying random distribution that adapts to the local imaging requirements and geometric constraints of the scanner.
2Productivity
If random coincidences are completely filtered by TOF mask, then computational time is reduced and reconstruction accuracy is improved, but existing random estimation methods become ineffective
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing the TOF mask characteristics and random distribution patterns before reconstruction. This allows the system to quickly apply pre-computed correction factors during reconstruction without performing complex real-time calculations, thus reducing computational time while maintaining accuracy.
Solution Approach 2:
The approach creates a simplified model or copy of the random distribution pattern that can be applied during reconstruction without requiring complex real-time estimation. This copied representation captures the essential features of the TOF-masked random distribution while being computationally efficient to apply.
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 method allows for accurate estimation of random events with the TOF mask applied, maintaining image quality and reducing computational complexity, while ensuring the benefits of the TOF mask are retained without altering the random distribution.
Implementation Method 1
In time-of-flight (TOF) imaging, the time within the coincidence interval at which each gamma photon in the coincident pair is detected is also measured. The time of flight information provides an indication of the location of the detected event along the line of coincidence.
Implementation Method 2
low-pass filtering the 4D interpolated sinogram count data to remove noise
Data Source
AI summary
A method of estimating random events in positron emission tomography list mode data, including obtaining time-of-flight (TOF) list mode count data that includes TOF information; converting the obtained TOF list mode count data into four-dimensional (4D) raw sinogram count data, without using the TOF information, wherein the 4D raw sinogram count data includes random count values; interpolating the 4D raw sinogram count data to generate 4D interpolated sinogram count data; low-pass filtering the 4D interpolated sinogram count data to remove noise; converting the low-pass filtered 4D interpolated sinogram count data into filtered 4D raw sinogram count data; and generating, by a processor, five-dimensional (5D) TOF raw sinogram count data from the filtered 4D raw sinogram count data by effectively applying a TOF mask filter to the filtered 4D raw sinogram count data.