Purpose: To compare the impact of deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR) on vertebral mass depiction, detection, and diagnosis of spinal cord compression on computed tomography (CT).
Methods: This retrospective study included 29 and 20 patients with and without vertebral masses. CT images were reconstructed using DLR and hybrid-IR. Three readers performed vertebral mass detection tests and evaluated the presence of spinal cord compression, the depiction of vertebral masses, and image noise. Quantitative image noise was measured by placing regions of interest on the aorta and spinal cord.
Results: Deep learning reconstruction tended to improve the performance of readers with less diagnostic imaging experience in detecting vertebral masses (area under the receiver operating characteristic curve [AUC] = 0.892-0.966) compared with hybrid-IR (AUC = 0.839-0.917). Diagnostic performance in evaluating spinal cord compression in DLR (AUC = 0.887-0.995) also improved compared with that in hybrid-IR (AUC = 0.866-0.942) for some readers. The depiction of vertebral masses and subjective image noise in DLR were significantly improved compared with those in hybrid-IR (P < .041). Quantitative image noise in DLR was also significantly reduced compared with that in hybrid-IR (P < .001).
Conclusion: Deep learning reconstruction improved the depiction of vertebral masses, which resulted in a tendency to improve the performance of CT compared to hybrid-IR in detecting vertebral masses and diagnosing spinal cord compression for some readers.
Keywords: computed tomography; deep learning reconstruction; iterative reconstruction; spinal cord compression; vertebral mass.