Work

Utility of photon-counting detectors for MV-kV dual-energy computed tomography imaging

Year: 2024

Type: article

Abstract: PurposeHigh soft-tissue contrast imaging is essential for effective radiotherapy treatment. This could potentially be realized using both megavoltage and kilovoltage x-ray sources available on some therapy treatment systems to perform “MV-kV” dual-energy (DE) computed tomography (CT). However, noisy megavoltage images obtained with existing energy-integrating detectors (EIDs) are a limiting factor for clinical translation. We explore the potential for non-spectral photon-counting detectors (PCDs) to improve MV-kV image quality simply by equally weighting all MV photons rather than up-weighting the less informative, lower contrast high-energy photons as in an EID.ApproachThree computational methods were applied to compare non-spectral PCDs with EIDs in MV-kV DE imaging. A single-line integral estimation theory approach was used to calculate the basis material signal-to-noise ratio (SNR) of tissue (1 to 50 cm) and bone (0.1 to 10 cm). CT images of a tissue cylinder with seven bone inserts (densities 1.0 to 2.2 g/cm3) were simulated to assess material decomposition accuracy. Multiple noisy simulations of an anthropomorphic phantom were performed to generate pixel-by-pixel noise profiles.ResultsPCDs yielded a 15% to 45% improvement in single-line integral SNR for both materials. In CT simulations, similar material decomposition accuracy was achieved, with both EIDs and PCDs slightly underestimating bone density. However, PCDs yield a higher contrast-to-noise ratio and more uniform noise texture than EIDs in virtual monoenergetic images.ConclusionsWe demonstrate the potential for improved MV-kV DE CT imaging using non-spectral PCDs and quantify the degree of improvement in a range of object compositions. This work motivates the experimental assessment of PCDs for megavoltage imaging and the potential clinical translation of PCDs to radiotherapy imaging.

Source: Journal of medical imaging

DOI: 10.1117/1.jmi.11.s1.s12811

Authors: Giavanna Jadick, Maya Ventura, Patrick J. La Rivière

Institutions: University of Chicago, University of Chicago Medical Center, University of Chicago Medicine Comprehensive Cancer Center

Language: English

Topic: Advanced X-ray and CT Imaging

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: National Cancer Institute