Normalization Correction for Multiple-Detection PET Imaging
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Solution Overview
Problem
Current PET scanners fail to effectively utilize and distinguish multiple-detection (MD) events involving three or more photons, leading to rejected or distorted data that limits image quality and sensitivity.
Innovation Solution
A system and method for emission tomography that identifies and processes MD events, applying normalization corrections to utilize these events for improved image reconstruction, without requiring additional detector elements, by separating and normalizing MD coincidence data alongside traditional prompt coincidence data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PET scanners only utilize traditional double coincidence events for image reconstruction, then the data processing is simpler, but the sensitivity and image quality are limited
Solution Approach 1:
The patent segments coincidence events into different categories (double coincidence events and multiple-detection coincidence events) and processes them separately through different normalization corrections. This segmentation allows the system to utilize complex MD events while maintaining organized data processing pipelines for each event type.
Solution Approach 2:
The patent applies preliminary normalization corrections to both double coincidence events and MD coincidence events before combining them for image reconstruction. By pre-processing each event type with appropriate normalization, the system prepares the data in advance, reducing the complexity of subsequent combined processing while improving overall image quality.
2Reliability
If PET scanners reject MD coincidence events involving three or more photons, then the data distortion is avoided, but the sensitivity and signal-to-noise ratio are reduced
Solution Approach 1:
The patent converts previously harmful or rejected MD coincidence events into beneficial data sources by applying specific normalization corrections. Instead of discarding these events as distorted data, the system uses MD-specific normalization to extract useful information, thereby improving signal-to-noise ratio while maintaining measurement precision through proper correction.
Solution Approach 2:
The patent changes the normalization parameters specifically for MD coincidence events, using different normalization corrections tailored to the unique characteristics of multiple-photon events. This parameter change allows the system to accurately process MD events that would otherwise be rejected, improving both sensitivity and data accuracy.
3Measurement precision
If PET scanners apply a single normalization correction to all coincidence events, then the processing is simpler, but the image reconstruction accuracy is compromised
Solution Approach 1:
The patent segments the normalization processing into distinct pathways: one for double coincidence events and another for multiple-detection coincidence events. Each segment receives appropriate normalization correction tailored to its specific characteristics, improving image reconstruction accuracy while maintaining organized, manageable processing complexity.
Solution Approach 2:
The patent applies different normalization corrections to different types of coincidence events based on their local characteristics. Double coincidence events receive one type of normalization while MD coincidence events receive a different normalization approach, ensuring each event type is processed with the quality appropriate to its specific properties.
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
Enhances the sensitivity and quality of PET scanner images by effectively incorporating MD events, increasing the signal-to-noise ratio and contrast-to-noise ratio, and allowing for better image reconstruction with existing clinical and preclinical systems.
Implementation Method 1
Block detectors 102 typically include a piece of scintillator material that converts the energy deposited by gamma rays into visible light
Implementation Method 2
The scintillator material is usually segmented into many scintillation crystal elements configured in an array, which is read out by a number of individual photo-detectors (typically, photo-multiplier tubes (PMTs), a position-sensitive photo-multiplier tube (PS-PMT), or silicon photo-multipliers (Si-PM)) that convert the light emitted by the scintillation material into electrical signals
Implementation Method 3
The positrons travel a very short distance before they encounter an electron and, when this occurs, the positrons are annihilated and converted into two high-energy photons, or gamma rays
Data Source
AI summary
A method and system for acquiring a series of medical images includes acquiring imaging data, identifying double coincidence events and multiple detection (MD) coincidence events from the imaging data, and storing the double coincidence events and the MD coincidence events in a first dataset and a second dataset, respectively. The method also includes applying a normalization correction to the first dataset and/or the second dataset using normalization values based on double coincidence events and/or MD coincidence events to obtain at least one normalized dataset, and reconstructing a series of medical images of the subject from the at least one normalized dataset.