Affiliation:
1. Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
2. Neuroradiology Unit, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
3. Department of neuroscience, Imaging and Clinical Sciences, “G. D'Annunzio” University of Chieti-Pescara, Chieti, Italy.
Abstract
Objective
The increasing number of coronary computed tomography angiography (CCTA) requests raised concerns about dose exposure. New dose reduction strategies based on artificial intelligence have been proposed to overcome limitations of iterative reconstruction (IR) algorithms. Our prospective study sought to explore the added value of deep-learning image reconstruction (DLIR) in comparison with a hybrid IR algorithm (adaptive statistical iterative reconstruction-veo [ASiR-V]) in CCTA, even in clinical challenging scenarios, as obesity, heavily calcified vessels and coronary stents.
Methods
We prospectively included 103 consecutive patients who underwent CCTA. Data sets were reconstructed with ASiR-V and DLIR. For each reconstruction signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) was calculated, and qualitative assessment was made with a four-point Likert scale by two independent and blinded radiologists with different expertise.
Results
Both SNR and CNR were significantly higher in DLIR (SNR-DLIR median value [interquartile range] of 13.89 [11.06–16.35] and SNR-ASiR-V 25.42 [22.46–32.22], P < 0.001; CNR-DLIR 16.84 [9.83–27.08] vs CNR-ASiR-V 10.09 [5.69–13.5], P < 0.001).
Median qualitative score was 4 for DLIR images versus 3 for ASiR-V (P < 0.001), with a good interreader reliability [intraclass correlation coefficient(2,1)e intraclass correlation coefficient(3,1) 0.60 for DLIR and 0.62 and 0.73 for ASiR-V].
In the obese and in the “calcifications and stents” groups, DLIR showed significantly higher values of SNR (24.23 vs 11.11, P < 0.001 and 24.55 vs 14.09, P < 0.001, respectively) and CNR (16.08 vs 8.04, P = 0.008 and 17.31 vs 10.14, P = 0.003) and image quality.
Conclusions
Deep-learning image reconstruction in CCTA allows better SNR, CNR, and qualitative assessment than ASiR-V, with an added value in the most challenging clinical scenarios.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Radiology, Nuclear Medicine and imaging
Cited by
1 articles.
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