Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Caruso D

PROVIDER: S-EPMC10957592 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Publications

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

Caruso Damiano D De Santis Domenico D Del Gaudio Antonella A Guido Gisella G Zerunian Marta M Polici Michela M Valanzuolo Daniela D Pugliese Dominga D Persechino Raffaello R Cremona Antonio A Barbato Luca L Caloisi Andrea A Iannicelli Elsa E Laghi Andrea A

European radiology 20230909 4

<h4>Objectives</h4>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.<h4>Materials and methods</h4>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- ...[more]

PMID: 37688618

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Dataset Information

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

Objectives

Materials and methods

Results

Conclusions

Clinical relevance statement

Key points

Publications

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

Similar Datasets

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