Spectral analysis
Multi-energy bin assessment for photon-counting CT
Insight That Drives Decisions
Quantitative CT QA that keeps pace
with photon-counting
Intelligent imaging solutions that turn complex CT and spectral data into clarity — from artefact reduction to quantitative insight.
Next
Solutions
QAF by QuantiSight
Quality Assessment Framework is the first cloud-based, made-in-UAE quantitative image quality platform, purpose-built for multi-energy photon-counting computed tomography (PCCT).
NPS
Noise power spectrum across energy bins
MTF
Modulation transfer function for spatial resolution
SNR
Signal-to-noise ratio measurement
CNR
Contrast-to-noise ratio for detectability
Material identification
Classify materials from spectral data
Material quantification
Quantify material concentrations
Histogram
Distribution analysis and visualization
Line profiles
Spatial intensity profiles along ROIs
MetraCT by QuantiSight
Quantitative metal artifact assessment for conventional CT — powered by a deep learning MAR model trained on the AAPM clinical dataset, with evaluation metrics for image quality.
Deep learning MAR
AMUS-GAN based metal artefact reduction
AAPM clinical dataset
Validated against clinical reference data
Histogram
Pre/post MAR distribution comparison
Line profile
Intensity profiles across metal regions
Image sharpness
Edge preservation after artefact correction
PSNR
Peak signal-to-noise ratio metric
RMSE
Root mean square error quantification
SSIM
Structural similarity index comparison
Under development
SpectraMAR by QuantiSight
Physics-based deep learning model for multi-energy photon-counting CT — spectral metal artifact assessment built for PCCT workflows.
Under development
SpectraMat by QuantiSight
Material decomposition for photon-counting CT.
How CT QA actually works
Technically deep. Vendor-neutral. Useful.
Educate
What “image quality” actually means in CT
Three numbers that matter for specialists: noise, spatial resolution, and material accuracy — measured consistently, not guessed.
Request a Demo →On demand
Custom analysis on demand
Need a tailored CT quality or spectral workflow? We build custom analysis for research and clinical teams — scoped to your scanner, protocol, and questions.
Talk about a custom project →About us
Built for the next generation of CT
QuantiSight develops intelligent imaging solutions for medical physicists, researchers, and clinical teams working with multi-energy and photon-counting CT. From quantitative quality assessment to metal artefact reduction, we turn complex spectral data into metrics you can trust — vendor-neutral, technically deep, and ready for real workflows.
Vendor-neutralQuantitativePCCT-ready
QuantiSight
Insight that drives decisions
Quantitative imaging for photon-counting CT
Proof
Standardized QA means comparable results across our photon-counting systems. QuantiSight gives our physics team metrics we can trust — noise, resolution, and spectral performance in one workflow.
Dr. Aamir Younis Raja
Associate Professor · Medical Physics Lab · Khalifa University
Educate
How CT QA actually works
Clear explainers for medical physicists and imaging researchers — no hype, no jargon for its own sake.
Educate
What does image quality actually mean in CT? Noise, resolution, and material accuracy.
Read article →Educate
Conventional QA gives you one number. Multi-energy CT needs QA across every energy bin.
Read article →Educate
Material decomposition, explained simply: how spectral CT tells bone from iodine from calcium.
Read article →News
QuantiSight news
Product milestones, conference moments, and updates from the QuantiSight team. Drag to explore.
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2026
SpectraMAR abstracts accepted at RSNA 2026 and IEEE NSS-MIC / RTSD 2026
Two photon-counting CT metal artefact reduction studies accepted — spectral-consistency MAR and quantitative bone/soft-tissue material decomposition.
Read more →2026
QAF-MAR accepted at IEEE NSS-MIC / RTSD 2026
Web-based quantitative framework for standardized evaluation of metal artefact reduction in CT, connecting MetraCT correction to QAF metrics.
Read more →2025
QAF presented at RSNA 2025
Task-specific image quality assessment framework for spectral photon-counting CT — foundational work behind the QAF product.
Read more →2026
SpectraMAR abstracts accepted at RSNA 2026 and IEEE NSS-MIC / RTSD 2026
Two photon-counting CT metal artefact reduction studies accepted — spectral-consistency MAR and quantitative bone/soft-tissue material decomposition.
Read more →2026
QAF-MAR accepted at IEEE NSS-MIC / RTSD 2026
Web-based quantitative framework for standardized evaluation of metal artefact reduction in CT, connecting MetraCT correction to QAF metrics.
Read more →2025
QAF presented at RSNA 2025
Task-specific image quality assessment framework for spectral photon-counting CT — foundational work behind the QAF product.
Read more →Research
Papers & conference acceptances
Peer-reviewed work and accepted abstracts behind QAF, MetraCT, and SpectraMAR. Drag to explore.
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QAF
RSNA 2025
Presented
Development of a universal task-specific image quality assessment framework for Spectral Photon Counting CT – a phantom study
Presented at RSNA 2025 — task-specific IQ assessment for spectral photon-counting CT, foundational work behind QAF.
Launch QAF →MetraCT
Biomedical Signal Processing and Control
Journal paper
An AMUS-GAN based framework for metal artefact reduction in CT images
Published deep-learning MAR for conventional CT — core research behind MetraCT.
View paper →MetraCT
IEEE NSS-MIC / RTSD 2026
Abstract accepted
QAF-MAR: A web-based quantitative framework for standardized evaluation of metal artefact reduction in CT
Accepted at IEEE NSS-MIC / RTSD 2026 — standardized, web-based metrics for MAR evaluation.
Launch QAF →SpectraMAR
RSNA 2026
Abstract accepted
Spectral consistency guided semi-supervised metal artefact reduction in photon-counting CT imaging
Accepted for RSNA 2026 — spectral-consistency MAR for photon-counting CT.
Learn more →SpectraMAR
IEEE NSS-MIC / RTSD 2026
Abstract accepted
Metal artefact reduction for quantitative bone and soft-tissue material decomposition in photon-counting CT
Accepted at IEEE NSS-MIC / RTSD 2026 — MAR that preserves quantitative material decomposition.
Learn more →QAF
RSNA 2025
Presented
Development of a universal task-specific image quality assessment framework for Spectral Photon Counting CT – a phantom study
Presented at RSNA 2025 — task-specific IQ assessment for spectral photon-counting CT, foundational work behind QAF.
Launch QAF →MetraCT
Biomedical Signal Processing and Control
Journal paper
An AMUS-GAN based framework for metal artefact reduction in CT images
Published deep-learning MAR for conventional CT — core research behind MetraCT.
View paper →MetraCT
IEEE NSS-MIC / RTSD 2026
Abstract accepted
QAF-MAR: A web-based quantitative framework for standardized evaluation of metal artefact reduction in CT
Accepted at IEEE NSS-MIC / RTSD 2026 — standardized, web-based metrics for MAR evaluation.
Launch QAF →SpectraMAR
RSNA 2026
Abstract accepted
Spectral consistency guided semi-supervised metal artefact reduction in photon-counting CT imaging
Accepted for RSNA 2026 — spectral-consistency MAR for photon-counting CT.
Learn more →SpectraMAR
IEEE NSS-MIC / RTSD 2026
Abstract accepted
Metal artefact reduction for quantitative bone and soft-tissue material decomposition in photon-counting CT
Accepted at IEEE NSS-MIC / RTSD 2026 — MAR that preserves quantitative material decomposition.
Learn more →Contact
Talk with the QuantiSight team.
Request a QAF demo, discuss MetraCT / SpectraMAR / SpectraMat, or explore analysis for your photon-counting or multi-energy CT program.
Flagship platform
qaf.xri-lab.comBuilt for
Medical physicists · Imaging researchers · Radiology staff · Pre-clinical labs