MO-FG-CAMPUS-IeP2-05: Feasibility Demonstration of High-Voltage Clinical CT and Impact On X-Ray Penetration Through Metal Objects

Authors


Abstract

Purpose:

To demonstrate the possibility and quantify the impact of operating a clinical CT scanner at exceptionally high x-ray tube voltage for better penetration through metal objects and facilitating metal artifact reduction.

Methods:

We categorize metal objects according to the data corruption severeness (level of distortion and complete photon starvation fraction). To demonstrate feasibility and investigate the impact of high voltage scanning we modified a commercial GE LightSpeed VCT scanner (generator and software) to enable CT scans with x-ray tube voltages as high as 175 kVp. A 20 cm diameter water phantom with two metal rods (10 mm stainless and 25 mm titanium) and a water phantom with realistic metal object (spine cage) were used to evaluate the data corruption and image artifacts in the absence of any algorithm correction. We also performed simulations to confirm our understanding of the transmitted photon levels through metal objects with different size and composition.

Results:

The reconstructed images at 175 kVp still have significant dark shading artifacts, as expected since no special scatter correction or beam hardening was performed but show substantially lower noise and photon starvation than at lower kVp due to better beam penetration. Analysis of the raw data shows that the photon starved data is reduced from over 4% at 140 kVp to below 0.2% at 175 kVp. The simulations indicate that for clinically relevant titanium and stainless objects a 175 kVp tube voltage effectively avoids photon starvation.

Conclusion:

The use of exceptionally high tube voltage on a clinical CT system is a practical and effective solution to avoid photon starvation caused by certain metal implants. Sparse and hybrid high-voltage protocols are being considered to maintain low patient dose. This opens the door to algorithmic physics-based corrections rather than treating the data as missing and relying on missing data algorithms.

Some of the authors are employees of General Electric

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