Dual-energy computed tomography imaging with megavoltage and kilovoltage X-ray spectra
- PMID: 38445223
- PMCID: PMC10910563
- DOI: 10.1117/1.JMI.11.2.023501
Dual-energy computed tomography imaging with megavoltage and kilovoltage X-ray spectra
Abstract
Purpose: Single-energy computed tomography (CT) often suffers from poor contrast yet remains critical for effective radiotherapy treatment. Modern therapy systems are often equipped with both megavoltage (MV) and kilovoltage (kV) X-ray sources and thus already possess hardware for dual-energy (DE) CT. There is unexplored potential for enhanced image contrast using MV-kV DE-CT in radiotherapy contexts.
Approach: A single-line integral toy model was designed for computing basis material signal-to-noise ratio (SNR) using estimation theory. Five dose-matched spectra (3 kV, 2 MV) and three variables were considered: spectral combination, spectral dose allocation, and object material composition. The single-line model was extended to a simulated CT acquisition of an anthropomorphic phantom with and without a metal implant. Basis material sinograms were computed and synthesized into virtual monoenergetic images (VMIs). MV-kV and kV-kV VMIs were compared with single-energy images.
Results: The 80 kV-140 kV pair typically yielded the best SNRs, but for bone thicknesses , the detunedMV-80 kV pair surpassed it. Peak MV-kV SNR was achieved with dose allocated to the MV spectrum. In CT simulations of the pelvis with a steel implant, MV-kV VMIs yielded a higher contrast-to-noise ratio (CNR) than single-energy CT and kV-kV DE-CT. Without steel, the MV-kV VMIs produced higher contrast but lower CNR than single-energy CT.
Conclusions: This work analyzes MV-kV DE-CT imaging and assesses its potential advantages. The technique may be used for metal artifact correction and generation of VMIs with higher native contrast than single-energy CT. Improved denoising is generally necessary for greater CNR without metal.
Keywords: basis material decomposition; computed tomography; dual-energy; estimation theory; megavoltage imaging; simulation.
© 2024 The Authors.
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