Multi-Threshold Photon Counting Detector Linear Fitting
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Solution Overview
Problem
Current spectral X-ray imaging systems face challenges in accurately relating measured photon counts to X-ray energy due to pixel-to-pixel variance, photon counting paralysis at high rates, and the spread of photoelectron plumes over multiple pixels, leading to significant errors in energy assignment.
Innovation Solution
A method involving multi-threshold photon counting pixel-array detectors, where the detector response is modeled and fit on a pixel-by-pixel basis using analytical expressions for peak height distribution and fractional photon counting, allowing for the separation of composite images into individual contributions from different X-ray energies through fast linear fitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple threshold setting is used to detect X-ray photon energy, then the system is easy to operate, but measurement precision deteriorates due to pixel-to-pixel variance and photon counting paralysis
Solution Approach 1:
The patent transforms the simple threshold setting approach into a multi-parameter fitting approach. Instead of using a single threshold value, the system collects counts at multiple threshold levels and fits these data to a model that accounts for pixel-to-pixel variance and charge sharing effects. This changes the operational parameters from a single threshold to multiple thresholds with associated fitting parameters, thereby improving energy assignment accuracy while maintaining operational feasibility through automated processing.
Solution Approach 2:
The patent introduces an intermediate modeling step between threshold setting and energy detection. The multi-threshold counting data serves as an intermediary that captures the complex detector response characteristics. By fitting this intermediate data to a physical model that includes parameters for pixel variance and charge sharing, the system mediates between the simple threshold input and the accurate energy output, resolving the contradiction between operational simplicity and measurement precision.
2Area of moving object
If pixel size is reduced to improve spatial resolution, then area decreases, but measurement precision deteriorates due to increased photoelectron plume spread relative to pixel size
Solution Approach 1:
The patent explicitly models the photoelectron plume spread as a parameter in the fitting function. By including terms that account for charge sharing between adjacent pixels and the spatial distribution of photoelectrons, the system can accurately determine photon energy even when the plume spread is comparable to or larger than the pixel size. This parameterization allows small pixels to maintain energy detection accuracy despite increased relative plume spread.
Solution Approach 2:
The patent replaces the mechanical/geometric constraint of pixel size with a mathematical model of the detection process. Instead of relying on large pixel areas to contain photoelectron plumes, the system uses a computational model that accounts for plume spread and charge sharing effects. This substitution of physical constraints with mathematical corrections allows small pixels to achieve the same energy detection accuracy as larger pixels would provide through geometry alone.
3Loss of information
If multi-threshold measurements are collected to improve energy discrimination, then information content increases, but device complexity increases due to data processing requirements
Solution Approach 1:
The patent segments the complex multi-threshold data processing into distinct, manageable components. The fitting function is divided into separate terms: one for the ideal detector response and another for the charge sharing and pixel variance effects. This segmentation allows each physical effect to be modeled independently and then combined, simplifying the overall processing complexity while preserving all the information from the multi-threshold measurements.
Solution Approach 2:
The patent implements a self-calibrating approach where the multi-threshold counting data itself provides the information needed to characterize the detector response. By fitting the measured counts at multiple thresholds to the model, the system automatically determines the relevant parameters (pixel variance, charge sharing effects) without requiring external calibration measurements or complex preprocessing. The data serves its own purpose of characterizing the system response.
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 enables high dynamic range detection and accurate energy-specific imaging by accounting for fractional photon contributions, improving the accuracy of energy assignment and reducing errors, particularly beneficial for small pixel sizes and applications requiring precise energy discrimination.
Implementation Method 1
The detector response comprising counts detected upon absorption of photons by a sensor
Data Source
AI summary
The present disclosure provides a system and method for efficiently mining multi-threshold measurements acquired using photon counting pixel-array detectors for spectral imaging and diffraction analyses. Images of X-ray intensity as a function of X-ray energy were recorded on a 6 megapixel X-ray photon counting array detector through linear fitting of the measured counts recorded as a function of counting threshold. An analytical model is disclosed for describing the probability density of detected voltage, utilizing fractional photon counting to account for edge/corner effects from voltage plumes that spread across multiple pixels. Three-parameter fits to the model were independently performed for each pixel in the array for X-ray scattering images acquired for 13.5 keV and 15.0 keV X-ray energies. From the established pixel responses, multi-threshold composite images produced from the sum of 13.5 keV and 15.0 keV data can be analytically separated to recover the monochromatic images through simple linear fitting.