Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data - 04/01/22
, Julien Frandon a, 1, Salim Si-Mohamed b, Djamel Dabli a, Aymeric Hamard a, Asmaa Belaouni a, Philippe Akessoul a, Francis Besse c, Boris Guiu d, Jean-Paul Beregi a
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Highlights |
• | 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. |
Abstract |
Purpose |
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.
Results |
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.
Conclusion |
DLR algorithms reduce the image-noise and improve lesion detectability. Their operations and properties impacted both noise-texture and spatial resolution.
El texto completo de este artículo está 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
Esquema
Vol 103 - N° 1
P. 21-30 - janvier 2022 Regresar al número
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