Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm

• Published in: European radiology Vol. 34; no. 4; pp. 2384 - 2393
• Main Authors: Caruso, Damiano, De Santis, Domenico, Del Gaudio, Antonella, Guido, Gisella, Zerunian, Marta, Polici, Michela, Valanzuolo, Daniela, Pugliese, Dominga, Persechino, Raffaello, Cremona, Antonio, Barbato, Luca, Caloisi, Andrea, Iannicelli, Elsa, Laghi, Andrea
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Computed Tomography; Deep learning; Diagnostic Radiology; Diagnostic systems; Figure of merit; Image contrast; Image enhancement; Image processing; Image quality; Image reconstruction; Imaging; Internal Medicine; Interventional Radiology; Lesions; Liver; Machine learning; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Metastases; Neuroradiology; Radiology; Signal to noise ratio; Statistical analysis; Ultrasound; Deep learning; Iterative reconstruction; Artificial intelligence; Diagnostic accuracy; Liver
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-10171-8

Objectives To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm. Materials and methods Oncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale. Results Fifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% ( p  ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4–5; p  ≤ .001) and significant median increase (29%) in FOM ( p  < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% ( p  = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%. Conclusions DLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm. Clinical relevance statement Deep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm. Key Points • Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.