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First-generation clinical dual-source photon-counting CT: ultra-low-dose quantitative spectral imaging.

Leening P Liu^1,2, Nadav Shapira³, Andrew A Chen^4,5

¹Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. leening@seas.upenn.edu.

European Radiology

|June 16, 2022

Summary

Photon-counting CT (PCCT) offers precise Hounsfield unit measurements at ultra-low radiation doses, outperforming dual-energy CT (DECT) by reducing electronic noise. This advancement benefits quantification and imaging for obese patients.

Keywords:

Diagnostic imaging Radiation dosage X-ray computed tomography

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Area of Science:

Medical Imaging
Radiology
Computed Tomography

Background:

Dual-source dual-energy CT (DECT) is a standard imaging technique.
Photon-counting computed tomography (PCCT) is an emerging technology with potential advantages.
Evaluating PCCT performance at low radiation doses is crucial for clinical adoption.

Purpose of the Study:

To compare image characteristics of a first-generation dual-source PCCT with a DECT scanner at ultra-low radiation doses.
To assess the precision and repeatability of Hounsfield unit (HU) measurements.
To evaluate the impact of electronic background noise reduction.

Main Methods:

Phantom imaging with and without an extension ring on both PCCT and DECT scanners.
Acquisition at various radiation dose levels (0.4–15.0 mGy) and spectral settings.
Characterization of Hounsfield units and image noise on virtual mono-energetic images.

Main Results:

PCCT demonstrated high precision in HU measurements across a wide range of radiation doses.
PCCT outperformed DECT at low radiation exposures.
PCCT significantly reduced electronic background noise by 74% compared to DECT (70/Sn150 kVp) and 60% compared to DECT (100/Sn150 kVp).

Conclusions:

First clinical experiences with dual-source PCCT show reliable HU values.
PCCT effectively removes electronic background noise, even at ultra-low doses.
PCCT offers diagnostic benefits for ultra-low-dose quantification and imaging of obese patients.