PET Image Reconstruction Using Dual Processor Error Correction
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Solution Overview
Problem
Current positron emission tomography (PET) reconstruction methods face inefficiencies in handling time-of-flight (TOF) data, particularly with histogram-based approaches that lose TOF information and require increased memory and processing time, while event-by-event techniques struggle with applying necessary corrections efficiently.
Innovation Solution
A dual processor system is employed, where one processor generates volumetric data using list-based reconstruction and another applies error corrections, including multiplicative and additive corrections, to efficiently reconstruct PET images, with the secondary processor handling rebinned data to provide proxy correction information in parallel, reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If histogram-based reconstruction algorithms are used to handle TOF data, then the reconstruction process can be simplified, but TOF information is lost and processing efficiency decreases
Solution Approach 1:
The patent segments the reconstruction process into two distinct paths: a primary list-mode reconstruction path that preserves TOF information and an auxiliary histogram-based path that generates correction data. This segmentation allows each path to be optimized for its specific purpose, resolving the contradiction between simplicity and efficiency.
Solution Approach 2:
The patent introduces an intermediary correction data generation process that uses histogram-based reconstruction on rebinned data to create correction factors. This intermediary process bridges the gap between simple histogram methods and complex list-mode methods, allowing the system to benefit from both approaches.
2Loss of information
If additional bins are created to account for TOF information in histogram-based approaches, then TOF information can be preserved, but memory requirements and processing time increase
Solution Approach 1:
The patent extracts the TOF information handling from the main reconstruction workflow by using list-mode reconstruction, while the auxiliary histogram path uses traditional binning without TOF bins. This extraction allows TOF information to be preserved in the primary path without the memory overhead in the auxiliary correction path.
Solution Approach 2:
The patent applies partial action by using histogram-based reconstruction only for generating correction data rather than for the full reconstruction. This partial application of histogram methods provides sufficient correction information without requiring the excessive memory and processing that would be needed for full histogram-based TOF reconstruction.
3Loss of information
If event-by-event reconstruction techniques are used, then TOF information can be accommodated, but applying necessary corrections becomes difficult and processing time increases
Solution Approach 1:
The patent segments the correction application process into two parts: multiplicative corrections applied during list-mode reconstruction and additive corrections applied after reconstruction using correction data from the auxiliary histogram path. This segmentation makes correction application more efficient while preserving TOF information.
Solution Approach 2:
The patent performs preliminary action by generating correction data in advance through the auxiliary histogram-based reconstruction path. This correction data is then applied to the primary list-mode reconstruction results, avoiding the need to apply corrections during the time-consuming event-by-event reconstruction process.
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 enhances processing efficiency by applying error corrections in image space rather than event-by-event, allowing for faster image reconstruction while maintaining high accuracy, and reduces the overall processing time by utilizing parallel processing paths.
Implementation Method 1
each of a plurality of positrons reacts with an electron in what is known as a positron annihilation event, thereby generating a coincident pair of 511 keV gamma rays
Implementation Method 2
In time of flight ('TOF') imaging, the time within the coincidence interval at which each gamma ray in the coincident pair is detected is measured. The time of flight information provides an indication of the location of the detected event along the line of coincidence.
Data Source
AI summary
A method and system for use in positron emission tomography, wherein a first processor element (234) is configured to reconstruct a plurality of positron annihilation events detected during a positron emission tomography scan using a list-based reconstruction technique to generate first volumetric data. A second reconstructor (226) is configured to reconstruct the plurality of events using a second reconstruction technique to generate second volumetric data for determining an error correction (228), the error correction applied to the first volumetric data to generate corrected volumetric data for generating a human-readable image (234). In one embodiment a multiplicative error correction is performed on the plurality of events, the first processor element (234) reconstructing the corrected plurality of events; and the second volumetric data error correction comprises an additive error correction.