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Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study

Abstract : Objectives: To assess the impact on image quality and dose reduction of a new deep learning image reconstruction (DLIR) algorithm compared with a hybrid iterative reconstruction (IR) algorithm. Methods: Data acquisitions were performed at seven dose levels (CTDIvol : 15/10/7.5/5/2.5/1/0.5 mGy) using a standard phantom designed for image quality assessment. Raw data were reconstructed using the filtered back projection (FBP), two levels of IR (ASiR-V50% (AV50); ASiR-V100% (AV100)), and three levels of DLIR (TrueFidelity™ low, medium, high). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index (d') was computed to model a large mass in the liver, a small calcification, and a small subtle lesion with low contrast. Results: NPS peaks were higher with AV50 than with all DLIR levels and only higher with DLIR-H than with AV100. The average NPS spatial frequencies were higher with DLIR than with IR. For all DLIR levels, TTF50% obtained with DLIR was higher than that with IR. d' was higher with DLIR than with AV50 but lower with DLIR-L and DLIR-M than with AV100. d' values were higher with DLIR-H than with AV100 for the small low-contrast lesion (10 ± 4%) and in the same range for the other simulated lesions. Conclusions: New DLIR algorithm reduced noise and improved spatial resolution and detectability without changing the noise texture. Images obtained with DLIR seem to indicate a greater potential for dose optimization than those with hybrid IR. Key points: • This study assessed the impact on image quality and radiation dose of a new deep learning image reconstruction (DLIR) algorithm as compared with hybrid iterative reconstruction (IR) algorithm. • The new DLIR algorithm reduced noise and improved spatial resolution and detectability without perceived alteration of the texture, commonly reported with IR. • As compared with IR, DLIR seems to open further possibility of dose optimization.
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https://hal.umontpellier.fr/hal-03349978
Contributeur : Nathalie Salvy-Cordoba Connectez-vous pour contacter le contributeur
Soumis le : mardi 21 septembre 2021 - 09:31:05
Dernière modification le : mardi 15 mars 2022 - 09:39:02

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Distributed under a Creative Commons Paternité 4.0 International License

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Joël Greffier, Aymeric Hamard, F. Pereira, Corinne Barrau, Hugo Pasquier, et al.. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. European Radiology, Springer Verlag, 2020, 30 (7), pp.3951-3959. ⟨10.1007/s00330-020-06724-w⟩. ⟨hal-03349978⟩

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