Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning-based image reconstruction algorithm on CT: a phantom study.
Eur Radiol
; 32(2): 1247-1255, 2022 Feb.
Article
em En
| MEDLINE
| ID: mdl-34390372
ABSTRACT
OBJECTIVES:
To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep learning-based image reconstruction algorithm (DLR, TrueFidelity™).METHODS:
Computed tomography (CT) images of a multi-sized image quality phantom (Mercury v4.0) were acquired under six radiation dose levels (0.48/0.97/1.93/3.87/7.74/15.47 mGy) and were reconstructed using filtered back projection (FBP) and three strength levels of the DLR (low/medium/high). The FBP images were denoised using the DLM. For all DLM and DLR images, the detectability index (d') (a task-based detection performance metric) was obtained, under various combinations of three target sizes (10/5/1 mm), five inlets (CT value difference with the background; -895/50/90/335/1000 HU), five phantom diameters (36/31/26/21/16 cm), and six radiation dose levels. Dose reduction potential (DRP) measures the dose reduction made by using DLM or DLR, while yielding d' equivalent to that of FBP at full dose.RESULTS:
The DRPs of the DLM, DLR-low, DLR-medium, and DLR-high were 86% (81-88%), 60% (46-67%), 76% (60-81%), and 87% (78-92%), respectively. For 10-mm targets, the DRP of the DLM (87%) was higher than that of all DLR algorithms (58-86%). However, for smaller targets (5 mm/1 mm), the DRPs of the DLR-high (89/88%) were greater than those of the DLM (87/84%).CONCLUSION:
The dose reduction potential of the vendor-agnostic DLM was shown to be comparable to that of the vendor-specific DLR at high strength and superior to those of the DLRs at medium and low strengths. KEY POINTS ⢠DRP of the vendor-agnostic model was comparable to that of high-strength vendor-specific model and superior to those of medium- and low-strength models. ⢠Under various radiation dose levels, the deep learning model shows higher detectability indexes compared to FBP.Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Eur Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2022
Tipo de documento:
Article