PET Scanner TOF Offset Calibration Using Background Radiation
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Solution Overview
Problem
Conventional PET scanner calibration is resource-intensive and requires frequent recalibration to maintain consistent photon detection times, which can be inefficient and time-consuming.
Innovation Solution
The method estimates TOF offsets associated with annihilation radiation based on TOF offsets associated with background radiation, using a trained machine learning model to quickly estimate annihilation radiation-based TOF offsets, thereby reducing the need for frequent recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration is performed frequently to maintain consistent photon detection times, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent creates a digital twin or model of the PET scanner's detection system that can simulate and predict TOF offset behavior. Instead of physically recalibrating the entire system, the model copies the essential calibration characteristics and allows virtual calibration operations, significantly reducing the time and resources needed for actual recalibration while maintaining measurement precision
Solution Approach 2:
The patent performs preliminary calibration operations and stores calibration data in advance. By pre-computing TOF offset corrections and maintaining a library of calibration states, the system can quickly switch between pre-calibrated configurations rather than performing full calibration procedures each time, thus reducing recurring calibration time while preserving detection accuracy
2Reliability
If conventional calibration is performed frequently to maintain consistent photon detection times, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The patent uses a computational model to copy and simulate calibration outcomes, allowing the system to maintain reliable photon detection time consistency through virtual calibration adjustments rather than frequent physical recalibration. This approach preserves detection reliability while minimizing interruptions to PET data acquisition productivity
Solution Approach 2:
The system implements self-calibration capabilities where the PET scanner automatically adjusts TOF offsets based on real-time performance monitoring and stored calibration models. This self-service approach maintains detection reliability without requiring external calibration operations, thereby preserving continuous productivity
3Productivity
If machine learning estimation is used to determine TOF offsets, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw detection data and TOF offset determination. This intermediary layer processes calibration information more efficiently than conventional methods, dramatically improving calibration productivity. While the model adds computational complexity, it is implemented as software rather than hardware modifications, keeping the physical device complexity manageable
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 allows for efficient determination of whether system response has changed, enabling timely recalibration and reducing resource consumption, while maintaining accurate PET data acquisition and image reconstruction.
Implementation Method 1
Crystals of a scintillator receive the gamma photons and emit light photons in response
Implementation Method 2
The electrical transducers, or photosensors, convert these light photons to electrical signals
Implementation Method 3
Time-of-flight (TOF) PET additionally measures the difference between the detection times of the two photons arising from the annihilation
Data Source
AI summary
Systems and methods include determination of a first time-of-flight offset for each of a plurality of crystals based on first annihilation radiation received by the plurality of crystals, determination of a second time-of-flight offset for each of the plurality of crystals based on radiation emitted by the plurality of crystals, determination, based on the second time-of-flight offsets, of a third time-of-flight offset for each of the plurality of crystals and associated with a response of the plurality of crystals to annihilation radiation, determination of whether the third time-of-flight offsets exceed a threshold, and, in response to a determination that the third time-of-flight offsets exceed the threshold, determine a fourth time-of-flight offset for each of the plurality of crystals based on second annihilation radiation received by the plurality of crystals.