Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study - 01/02/23
, Julien Frandon a, Quentin Durand a, Tarek Kammoun a, Maeliss Loisy a, Jean-Paul Beregi a, Djamel Dabli a, b
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Highlights |
• | The impact of a new artificial intelligence deep-learning reconstruction algorithm on image quality and dose for lumbar spine CT was compared to a hybrid iterative reconstruction algorithm. |
• | For bone reconstruction kernel, from Standard to Smoother levels, the noise magnitude and the detectability of bone lesions are improved using the new artificial intelligence deep-learning reconstruction. |
• | The use of Smooth and Smoother levels allows a significant dose reduction (up to 72%) with a high detectability for the detection of lytic and sclerotic bone lesions and excellent overall clinical image quality. |
Abstract |
Purpose |
The purpose of this study was to assess the impact of the new artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and radiation dose compared with iterative reconstruction algorithm in lumbar spine computed tomography (CT) examination.
Materials and methods |
Acquisitions on phantoms were performed using a tube current modulation system for four DoseRight Indexes (DRI) (i.e., 26/23/20/15). Raw data were reconstructed using the Level 4 of iDose4 (i4) and three levels of AI-DLR (Smoother/Smooth/Standard) with a bone reconstruction kernel. The Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d’) were computed (d’ modeled detection of a lytic and a sclerotic bone lesions). Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists.
Results |
The Noise magnitude was lower with AI-DLR than i4 and decreased from Standard to Smooth (-31 ± 0.1 [SD]%) and Smooth to Smoother (-48 ± 0.1 [SD]%). The average NPS spatial frequency was similar with i4 (0.43 ± 0.01 [SD] mm–1) and Standard (0.42 ± 0.01 [SD] mm–1) but decreased from Standard to Smoother (0.36 ± 0.01 [SD] mm–1). TTF values at 50% decreased as the dose decreased but were similar with i4 and all AI-DLR levels. For both simulated lesions, d’ values increased from Standard to Smoother levels. Higher detectabilities were found with a DRI at 15 and Smooth and Smoother levels than with a DRI at 26 and i4. The images obtained with these dose and AI-DLR levels were rated satisfactory for clinical use by the radiologists.
Conclusion |
Using Smooth and Smoother levels with CT allows a significant dose reduction (up to 72%) with a high detectability of lytic and sclerotic bone lesions and a clinical overall image quality.
Il testo completo di questo articolo è disponibile in PDF.Keywords : Artificial intelligence, Deep learning image reconstruction algorithm, Multidetector computed tomography, Task-based image quality assessment, Lumbar spine
Abbreviations : AI-DLR, BMI, CT, CTDIvol, DLR, DRI, HU, IR, NPS, ROI, SD, TCM, TTF
Mappa
Vol 104 - N° 2
P. 76-83 - febbraio 2023 Ritorno al numero
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