Purpose: To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrhages with abdominopelvic contrast-enhanced computed tomography (CT) compared with hybrid iterative reconstruction.
Methods: This retrospective study included 16 patients (mean age: 54.2 ± 22.1 years; 8 men and 8 women) with acute hemorrhage who underwent contrast-enhanced CT. Unenhanced axial, arterial phase axial, arterial phase coronal, and delayed phase axial images were reconstructed with deep learning reconstruction, hybrid iterative reconstruction, and filtered back projection, which was used as a control in qualitative analyses. Circular and line regions of interest were placed on the aorta and superior mesenteric artery (SMA), respectively, in quantitative analyses. Using a blind process, 2 radiologists independently evaluated image noise, depiction of arteries, and suitability for the evaluation of hemorrhage in qualitative image analyses.
Results: Image noise in deep learning reconstruction was significantly reduced compared with hybrid iterative reconstruction in the quantitative ( < .001) and qualitative analyses (Reader 1, ≤ .001 for all series; Reader 2, = .002, .001, and < .001). The slope at the half maximum in deep learning reconstruction (123.8 ± 63.2 HU/mm) significantly improved compared with hybrid iterative reconstruction (105.3 ± 51.0 HU/mm) in the CT attenuation profile of the SMA ( < .001). Qualitative analyses revealed a significantly improved depiction of arteries (Reader 1, < .001 for all series; Reader 2, = .037, .008, and < .001) and suitability for evaluating acute hemorrhage in the arterial phase image (Reader 1, < .001 for both series; Reader 2, = .041 and .004) with deep learning reconstruction compared with hybrid iterative reconstruction.
Discussion: Deep learning reconstruction provided images with a significantly better depiction of arteries and more suitable quality arterial phase images for the evaluation of abdominopelvic hemorrhage compared with hybrid iterative reconstruction.
Conclusion: Deep learning reconstruction is better for reconstructing abdominopelvic contrast-enhanced CT images when evaluating hemorrhages; however, a prospective study including a large number of patients is needed to strengthen the findings of this study.
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