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QUALITY ASSESSMENT
FRAMEWORK FOR PHOTON COUNTING CT

QAF is a web-based framework designed to support image quality assessment for photon-counting detector (PCD) computed tomography in both clinical and research settings. The framework addresses core analytical requirements by providing standardized, reproducible, and scalable evaluation tools. It is designed and developed to facilitate integration into clinical workflows while also supporting advanced research applications. QAF offers comprehensive functionality for systematic image quality evaluation across a wide range of scanner configurations and acquisition protocols.

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Photon Counting CT Scanner

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About QAF Information Contact Team Publications

Analysis Capabilities

This application is feasible for clinical PCD (Photon Counting Detector) image quality assessment as well as for research purposes to fulfill researchers' basic analysis needs. The platform supports both clinical workflow integration and advanced research applications.

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01 Photon Counting CT MARS microlab & bench top systems −
MARS SPCCT Scanner
Spectral photon-counting CT (MARS microlab (5×120) installed at KU's medical imaging lab)
Bench Top System
Spectral photon-counting CT bench top system for 2D and 3D imaging
This application is feasible for clinical PCD (Photon Counting Detector) image quality assessment as well as for research purposes to fulfill researchers' basic analysis needs. The platform supports both clinical workflow integration and advanced research applications, providing comprehensive tools for image quality evaluation across different scanner configurations.
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02 Energy Images Multi-energy spectral bins +
Energy Images
Energy bin separation
Multi-energy photon counting images provide spectral information by separating X-ray photons into different energy bins, enabling material decomposition and enhanced contrast visualization.
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03 Linear Regression Response Material concentration vs CT values +
Linear Regression
Spectral linearity analysis
Evaluates the relationship between material concentrations and measured CT values across energy bins, ensuring accurate quantification and verifying linear response characteristics.
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04 Resolution of SPCCT Images Modulation Transfer Function +
MTF Resolution
Spatial resolution (MTF)
The MTF quantifies how well the system preserves contrast at different spatial frequencies, critical for evaluating image sharpness and detail visibility in photon counting CT.
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05 2D Noise Power Spectrum (NPS) Spatial frequency noise distribution +
2D NPS
2D Noise Power Spectrum (NPS)
The 2D Noise Power Spectrum (NPS) characterizes the spatial frequency content of noise in a medical image across two dimensions. It quantifies how noise is distributed across different spatial frequencies in the x-y plane, providing essential information for image quality assessment and system optimization through comprehensive noise analysis.
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06 3D Noise Power Spectrum (NPS) Volumetric noise characterization +
3D NPS
3D Noise Power Spectrum (NPS)
The 3D Noise Power Spectrum (NPS) provides complete three-dimensional noise characterization showing noise power distribution in all three spatial dimensions (x, y, z). This comprehensive analysis offers the most detailed noise assessment, enabling thorough evaluation of system performance across the entire image volume.
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07 Signal-to-Noise Ratio (SNR) Signal strength vs noise level +
SNR
Signal-to-Noise Ratio (SNR)
Signal-to-Noise Ratio (SNR) is a fundamental image quality metric that quantifies the ratio of signal strength to noise level in an image. Higher SNR values indicate better image quality with less noise relative to the signal, making it essential for evaluating diagnostic image quality and system performance.
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08 Material Decomposition Images Material-specific basis images +
Material Decomposition
Material Decomposition Images
Material decomposition utilizes spectral CT data to separate multi-energy images into material-specific basis images (e.g., water, iodine, calcium). This advanced technique enables precise material identification and quantification, supporting enhanced diagnostic capabilities and research applications in spectral photon counting CT imaging.
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Platform Features

Image Quality
SNR, NPS, MTF, and linearity analysis for complete assessment
Material ID
Advanced material identification and quantification from spectral data
Database
Store and manage DICOM data with multi-energy bin support
Analysis Tools
Histogram, Line Profile, HU Conversion, and visualization