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

• Published in: Radiologia medica Vol. 128; no. 4; pp. 434 - 444
• Main Authors: De Santis, Domenico, Polidori, Tiziano, Tremamunno, Giuseppe, Rucci, Carlotta, Piccinni, Giulia, Zerunian, Marta, Pugliese, Luca, Del Gaudio, Antonella, Guido, Gisella, Barbato, Luca, Laghi, Andrea, Caruso, Damiano
• Format: Journal Article
• Language: English
• Published: Milan Springer Milan 01-04-2023; Springer Nature B.V
• Subjects: Algorithms; Angiography; Cardiac Radiology; Computed tomography; Computed Tomography Angiography - methods; Coronary Angiography - methods; Correlation coefficients; Datasets; Deep Learning; Diagnostic Radiology; Humans; Image contrast; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Imaging; Interventional Radiology; Iterative methods; Machine learning; Male; Medical imaging; Medicine; Medicine & Public Health; Neuroradiology; Quality assessment; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Signal to noise ratio; Ultrasound; Deep learning; CCTA; Artificial intelligence; Coronary computed tomography angiography; Image reconstruction
• ISSN: 1826-6983; 0033-8362; 1826-6983
• DOI: 10.1007/s11547-023-01607-8

Purpose
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).
Material and methods
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.
Results
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).
Conclusion
DLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.