Multi-Layer CT Detector Architecture for Photon Pile-Up Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Photon counting CT (PCCT) systems face issues with photon pile-up at high x-ray flux rates, leading to reduced image quality, increased power consumption, and data size due to smaller detector pixels, and inefficiencies in photon detection and pile-up correction.
Innovation Solution
A multi-layer CT detector system is introduced, comprising a layer of energy integrating detectors (EID) and a layer of photon counting (PC) sensors, where the PC sensors are arranged between the EID detectors and the x-ray source, with the EID detectors positioned below the PC sensors to capture photons that miss the PC sensors, and a deep learning convolutional neural network is used to correct and enhance image resolution using both types of detector data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If smaller detector pixels are introduced to reduce the pile-up effect, then image quality is improved, but power consumption and data size increase
Solution Approach 1:
The detector system is divided into two distinct layers: a first layer with smaller photon-counting detector pixels for high-resolution imaging, and a second layer with larger energy-integrating detector pixels for capturing photons that pass through the first layer. This segmentation allows each layer to perform its specialized function optimally, reducing the need for excessive small pixels that would increase power consumption.
Solution Approach 2:
The invention transitions from a single-layer detector to a multi-layer detector architecture, adding the dimension of depth. The first layer processes photons at a higher spatial resolution level, while the second layer captures remaining photons at a lower resolution level, effectively utilizing the third dimension (depth) to resolve the contradiction between pixel size and power consumption.
2Measurement precision
If smaller detector pixels are introduced to reduce the pile-up effect, then image quality is improved, but data size increases
Solution Approach 1:
The detector system segments the detection task across two layers with different pixel densities. The first layer uses smaller pixels for detailed imaging where needed, while the second layer uses larger pixels for capturing photons that pass through, reducing the total number of channels and thereby reducing data size while maintaining image quality.
3Measurement precision
If a layer of photon counting detectors is placed before energy integrating detectors, then photon pile-up is reduced, but gaps between detector modules become more significant
Solution Approach 1:
The detector system is segmented into two layers with different pixel densities. The first layer has smaller pixels for high-resolution detection, while the second layer has larger pixels that can effectively cover and compensate for gaps between detector modules, ensuring comprehensive photon capture.
4Measurement precision
If deeper silicon depth is used in PC sensors to ensure good dose efficiency, then detection efficiency is improved, but cost and data size increase
Solution Approach 1:
The detection function is segmented across two layers: the first layer (photon-counting) uses shallower silicon depth optimized for photon counting and spectral information, while the second layer (energy-integrating) uses deeper silicon depth optimized for capturing photons that pass through the first layer. This segmentation allows each layer to use optimized silicon depths for their specific functions, reducing the total silicon material quantity while maintaining overall detection efficiency.
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 configuration reduces photon pile-up, increases image resolution, decreases noise, and lowers power consumption and data size by leveraging the advantages of both EID and PC detectors, while allowing for accurate pile-up correction and improved beam-hardening performance.
Implementation Method 1
an electron beam generated by a cathode is directed towards a target within an x-ray tube. A fan-shaped or cone-shaped beam of x-rays produced by electrons colliding with the target is directed towards a subject
Implementation Method 2
the radiation detectors are photon-counting detectors, and photons are counted to provide spectral information
Implementation Method 3
an image processing unit configured to correct PC data using EID data, and denoise and increase a resolution of an image reconstructed from EID data and PC data using a deep learning convolutional neural network (CNN)
Data Source
AI summary
Systems and methods are provided for increasing a quality of computed tomography (CT) images. In one embodiment, a computed tomography (CT) detector system comprises a layer of energy integrating detectors (EID) arranged below a layer of photon counting (PC) sensors with respect to an incoming x-ray, where a number of the PC sensors exceeds a number of the EID detectors; and an image processing unit configured to correct PC data using EID data, and denoise and increase a resolution of an image reconstructed from EID data and PC data using a deep learning convolutional neural network (CNN) trained on pairs of images, each pair of images including a target image reconstructed from a first signal from the layer of PC sensors, and an input image reconstructed from a second signal from the layer of EID detectors, the EID data and PC data acquired concurrently from a same patient ray path.