Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study

• Published in: European radiology Vol. 33; no. 1; pp. 699 - 710
• Main Authors: Greffier, Joël, Durand, Quentin, Frandon, Julien, Si-Mohamed, Salim, Loisy, Maeliss, de Oliveira, Fabien, Beregi, Jean-Paul, Dabli, Djamel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computed Tomography; Deep Learning; Diagnostic Radiology; Drug Tapering; Frequency dependence; Hepatocellular carcinoma; Humans; Image processing; Image quality; Image reconstruction; Imaging; Inserts; Internal Medicine; Interventional Radiology; Iterative methods; Learning; Lesions; Liver cancer; Machine learning; Medicine; Medicine & Public Health; Metastases; Neuroradiology; Noise standards; Phantoms, Imaging; Quality assessment; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Spatial discrimination; Spatial resolution; Tomography, X-Ray Computed - methods; Transfer functions; Ultrasound; Deep learning; Artificial intelligence; Image processing, computer-assisted; Image enhancement; Multidetector computed tomography
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-09003-y

Objectives To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications. Methods Acquisitions on phantoms were performed at 5 dose levels (CTDI vol : 13/11/9/6/1.8 mGy). Raw data were reconstructed using level 4 of iDose 4 (i4) and 3 levels of AI-DLR (Smoother/Smooth/Standard). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index ( d ′) were computed: d ′ modelled detection of a liver metastasis (LM) and hepatocellular carcinoma at portal (HCCp) and arterial (HCCa) phases. Image quality was subjectively assessed on an anthropomorphic phantom by 2 radiologists. Results From Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability ( d ′) of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d ′ values were higher using the Smoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level. Conclusion Use of the Smooth and Smoother levels of AI-DLR reduces the image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality. Key Points • Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm. • The Smooth and Smoother levels of AI-DLR reduced the image noise and improved the detectability of lesions and spatial resolution for standard and low-dose levels. • The Smooth level seems the best compromise between the lowest dose level and adequate image quality.