Saman Sotoudeh-Paima, Ehsan Samei, And Ehsan Abadi
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引用次数: 0
Abstract
Objectives: To evaluate how photon-counting CT (PCCT) reproducibly quantifies lung density compared to conventional CT (energy-integrating CT or EICT) and assess the relevance of image quality.
Methods: In this retrospective analysis of a prospective study, subjects underwent acquisitions using EICT and PCCT systems (August-December 2021) on the same day. PCCT projections were reconstructed with four kernels and two voxel sizes using 2024 reconstruction software (ReconCT, Siemens). PCCT and EICT data were compared using lung density measurements: percentage of low-attenuation areas <-950 HU (LAA-950), 15th percentile of lung density histogram (Perc15), and mean lung density (MLD). Measurement reproducibility and its relation to image quality parameters (modulation transfer function and noise) were analyzed.
Results: Fifty-four subjects (mean age, 67.4 years ± 9.6, 30 female) were studied. PCCT images had the closest agreement to EICT under identical voxel size and similar kernels, yielding lower LAA-950 (-0.9%±2.7, p < 0.05), higher Perc15 (1.4HU ± 7.9, p > 0.05), and higher MLD (2.4 HU ± 10.8, p > 0.05). At higher PCCT resolution (1024 × 1024 matrix, 0.2 mm slice thickness), the smoother quantitative kernel (Qr36) improved agreement, with differences of -0.5%±3.0, -0.1HU ± 9.8, and 2.8HU ± 11.1, for LAA-950, Perc15, and MLD (p > 0.05). Image quality parameters strongly correlated with density measurements (RPerc152=85.3%).
Conclusions: PCCT yields reproducible lung density measurements comparable to EICT, with optimal reproducibility dependent on optimized reconstruction parameters and explainable by image quality parameters.
Advances in knowledge: Lung density reproducibility is critical for integrating PCCT into EICT-based workflows. In our study, PCCT aligned more closely with EICT using similar kernels at identical voxel sizes, or smoother kernels at smaller voxel sizes.
期刊介绍:
BJR is the international research journal of the British Institute of Radiology and is the oldest scientific journal in the field of radiology and related sciences.
Dating back to 1896, BJR’s history is radiology’s history, and the journal has featured some landmark papers such as the first description of Computed Tomography "Computerized transverse axial tomography" by Godfrey Hounsfield in 1973. A valuable historical resource, the complete BJR archive has been digitized from 1896.
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