Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography.

Male Humans Computed Tomography Angiography / methods Deep Learning Radiographic Image Interpretation, Computer-Assisted / methods Coronary Angiography / methods Algorithms Radiation Dosage Image Processing, Computer-Assisted / methods
Artificial intelligence CCTA Coronary computed tomography angiography Deep learning Image reconstruction

Journal

La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625

Informations de publication

Date de publication:
Apr 2023
Historique:
received: 14 07 2022
accepted: 03 02 2023
medline: 24 4 2023
pubmed: 28 2 2023
entrez: 27 2 2023
Statut: ppublish

Résumé

To perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V). Fifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient. DLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001). DLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.

Identifiants

DOI: 10.1007/s11547-023-01607-8 PMID: 36847992 PMC: PMC10119038
pubmed: 36847992
doi: 10.1007/s11547-023-01607-8
pii: 10.1007/s11547-023-01607-8
pmc: PMC10119038
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

434-444

Informations de copyright

© 2023. The Author(s).

Références

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Auteurs

Domenico De Santis (D)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0002-1197-456X

Tiziano Polidori (T)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0002-4633-6760

Giuseppe Tremamunno (G)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0001-6484-9765

Carlotta Rucci (C)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0003-3793-2277

Giulia Piccinni (G)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0003-3219-2839

Marta Zerunian (M)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0003-4635-7999

Luca Pugliese (L)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0001-9061-7247

Antonella Del Gaudio (A)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0001-6017-8976

Gisella Guido (G)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0002-0711-2975

Luca Barbato (L)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0001-5607-6918

Andrea Laghi (A)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy. andrea.laghi@uniroma1.it.
ORCID: 0000-0002-3091-7819

Damiano Caruso (D)

Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
ORCID: 0000-0001-9285-4764

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