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Using TrueFidelityTM algorithm, spatial resolution is independent of dose and contrast and image texture modification is limited.
Using AiCE algorithm, spatial resolution depends on dose and contrast and image is smoother, especially at highest level.
Detectability of chest lesions is greater with AiCE than with TrueFidelityTM at low dose levels but AiCE changes noise texture.
The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications.
Material and methods
Acquisitions on image quality and anthropomorphic phantoms were performed at six dose levels (CTDIvol: 10/7.5/5/2.5/1/0.5mGy) on two CT scanners equipped with two different DLR algorithms (TrueFidelityTM and AiCE). Raw data were reconstructed using the filtered back-projection (FBP) and the lowest/intermediate/highest DLR levels (L-DLR/M-DLR/H-DLR) of each algorithm. Noise power spectrum, task-based transfer function (TTF) and detectability index (d’) were computed: d’ modelled detection of a soft tissue mediastinal nodule, ground-glass opacity, or high-contrast pulmonary lesion. Subjective image quality of anthropomorphic phantom images was analyzed by two radiologists.
For the L-DLR/M-DLR levels, the noise magnitude was lower with TrueFidelityTM than with AiCE from 2.5 to 10 mGy. For H-DLR, noise magnitude was lower with AiCE . For L-DLR and M-DLR, the average NPS spatial frequency (fav) values were greater for AiCE except for 0.5 mGy. For H-DLR levels, fav was greater for TrueFidelityTM than for AiCE. TTF50% values were greater with AiCE for the air insert, and lower than TrueFidelityTM for the polyethylene insert. From 2.5 to10 mGy, d’ was greater for AiCE than for TrueFidelityTM for H-DLR for all lesions, but similar for L-DLR and M-DLR. Image quality was rated clinically appropriate for all levels of both algorithms, for dose from 2.5 to 10 mGy, except for L-DLR of AiCE.
DLR algorithms reduce the image-noise and improve lesion detectability. Their operations and properties impacted both noise-texture and spatial resolution.Le texte complet de cet article est disponible en PDF.
Keywords : Multidetector computed tomography, Task-based image quality assessment, Deep learning image reconstruction
Abbreviations : AiCE, CT, DNN, DLR, GGO, HCP, HU, IR, NPS, ROI, SD, TF, TTF