Phantom task-based image quality assessment of three generations of rapid kV-switching dual-energy CT systems on virtual monoenergetic images

Joël Greffier; Anaïs Viry; Yves Barbotteau; Julien Frandon; Maeliss Loisy; Fabien de Oliveira; Jean Paul Beregi; Djamel Dabli

doi:10.1002/mp.15558

Phantom task-based image quality assessment of three generations of rapid kV-switching dual-energy CT systems on virtual monoenergetic images

Med Phys. 2022 Apr;49(4):2233-2244. doi: 10.1002/mp.15558. Epub 2022 Mar 7.

Authors

Joël Greffier¹, Anaïs Viry^{2

3}, Yves Barbotteau⁴, Julien Frandon¹, Maeliss Loisy¹, Fabien de Oliveira¹, Jean Paul Beregi¹, Djamel Dabli¹

Affiliations

¹ Department of Medical Imaging, CHU Nîmes, University of Montpellier, Nîmes Medical Imaging Group, Montpellier, France.
² Institute of Radiation Physics, Lausanne University Hospital, Lausanne, Switzerland.
³ Institute of Radiation Physics, University of Lausanne, Lausanne, Switzerland.
⁴ Hôpital Privé Clairval - Service d'Imagerie, Marseille, France.

PMID: 35184293
DOI: 10.1002/mp.15558

Abstract

Purpose: To compare the spectral performance of three rapid kV switching dual-energy CT (DECT) systems on virtual monoenergetic images (VMIs) at low-energy levels on abdominal imaging.

Methods: A multi-energy phantom was scanned on three DECT systems equipped with three different gemstone spectral imaging (GSI) platforms: GSI (1st generation, GSI-1st), GSI-Pro (2nd generation, GSI-2nd ), and GSI-Xtream (3rd generation, GSI-3rd). Acquisitions on the phantom were performed with a CTDI_vol close to 11mGy. For all platforms, raw data were reconstructed using filtered-back projection (FBP) and a hybrid iterative reconstruction algorithm (ASIR-V at 50%; AV50). A deep-learning image reconstruction (DLR) algorithm (TrueFidelity) was used only for the GSI-3rd. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 80 keV of VMIs. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions according to the keV level used.

Results: For all GSI platforms, the noise magnitude decreased from 40 to 70 keV, and using AV50 compared to FBP. The average NPS spatial frequency (f_av ) and spatial resolution (TTF_50% ) were similar from 40 to 70 keV and decreased with AV50 compared to FBP. Compared to AV50, using DLR reduced the noise magnitude (-27% ± 3%) and improved f_av values (10% ± 0%) and altering spatial resolution (2% ± 5%). For the two lesions, d' values peaked at 70 keV for GSI-1st and GSI-2nd platforms and at 40/50 keV for GSI-3rd, for all reconstruction algorithms. The highest d' values were found for the GSI-3rd with DLR.

Conclusion: Differences in image quality were found between the GSI platforms for VMIs at low keV. The new DLR algorithm on the GSI-3rd platform reduced noise and improved spatial resolution and detectability without changing the noise texture for VMIs at low keV. The choice of the best energy level in VMIs depends on the platform and the reconstruction algorithm.

Keywords: deep learning image reconstruction; dual-energy; iterative reconstruction; multidetector computed tomography; task-based image quality assessment.

MeSH terms

Algorithms
Phantoms, Imaging
Radiation Dosage
Radiographic Image Interpretation, Computer-Assisted* / methods
Signal-To-Noise Ratio
Tomography, X-Ray Computed* / methods