PET Noise Equivalent Count Estimation From Scatter Events
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The limited use of noise equivalent counts in positron emission tomography (PET) scans is due to the inability to estimate scatter coincidence events during the scan, which affects the signal-to-noise ratio and image quality.
Innovation Solution
A method and system for estimating noise equivalent counts on-the-fly using a PET scanner, involving a detector array, processor, and deep learning-based models to detect and differentiate between true, random, and scatter coincidence events, with the aid of pre-computed masks and attenuation correction factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise equivalent counts are to be estimated during the PET scan, then real-time image quality prediction is improved, but the complexity of the system increases due to the need for scatter estimation capabilities
Solution Approach 1:
The system performs preliminary actions by estimating scatter coincidence events during the scan using measured data and known physical models before the final image reconstruction. This allows noise equivalent counts to be calculated in real-time without waiting for post-processing, resolving the contradiction by preparing necessary estimates in advance during the acquisition phase
Solution Approach 2:
The patent introduces an intermediary computational layer that processes detected coincidence events to separate true, random, and scatter components. This intermediary processing step enables noise equivalent counts estimation without requiring fundamental changes to the PET scanner hardware, thus improving measurement precision while managing system complexity through software-based mediation
2Reliability
If scatter coincidence events are estimated during the scan, then image quality assessment is improved, but the scan time increases due to additional processing requirements
Solution Approach 1:
The system maintains continuity of useful action by performing scatter estimation and noise equivalent counts calculation continuously during the scan acquisition rather than as a separate post-processing step. The processor utilizes coincidence events as they are detected, converting raw data into quality metrics in real-time without interrupting or extending the scan duration
Solution Approach 2:
The patent implements self-service by having the system use its own measured data from the scan to perform scatter estimation and quality assessment. The PET scanner uses the coincidence events it detects to generate noise equivalent counts estimates, eliminating the need for external or additional measurement processes that would extend scan time
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
Enables real-time estimation of noise equivalent counts, improving image quality prediction and allowing for dynamic adjustment of scan protocols, thereby enhancing the efficiency and accuracy of PET imaging.
Implementation Method 1
When a positron interacts with an electron by annihilation, the entire mass of the positron-electron pair is converted into two 511 keV photons. The photons are emitted in opposite directions along a line of response.
Implementation Method 2
The annihilation photons (known as (2) singles) are detected by detectors that are placed along the line of response on a detector ring.
Implementation Method 3
estimating scatter coincidence events detected by the detector array
Data Source
AI summary
A method for estimating noise equivalent counts includes one or more times during a scan of an object with a positron emission tomography (PET) scanner, wherein a plurality of coincidence events are detected by a detector array of the PET scanner, performing the following actions. The actions include obtaining a total of the plurality of coincidence events, estimating random coincidence events, and estimating scatter coincidence events. The actions include estimating true coincidence events based on the total of the plurality of coincidence events, the estimated random coincidence events, and the estimated scatter coincidence events. The actions include determining a scatter fraction based on the estimated scatter events and true coincidence events. The actions include estimating the noise equivalent counts based at least on the scatter fraction, the total of the plurality of coincidence events, the estimated true coincidence events, the estimated scatter coincidence events, and the estimated random coincidence events.