Detection of intracranial hemorrhage using ultralow-dose brain computed tomography with deep learning reconstruction versus conventional-dose computed tomography

Abstract

Background This study aimed to evaluate the diagnostic performance, image quality, and radiation dose among ultralow-dose protocol with deep learning reconstruction (DLR), ultralow-dose computed tomography (CT) with iterative reconstruction (IR), and conventional-dose protocols for detecting intracranial hemorrhage. Methods This retrospective study enrolled 93 patients (median age: 67 years; interquartile range [IQR]: 59-76 years; 61 males). A conventional-dose CT was obtained using 120 kVp, 123-188 mA and IR. Follow-up ultralow-dose CT was obtained using 120 kVp, 50 mA with IR and DLR. Qualitative assessments and quantitative assessments were conducted. The diagnostic performance for detecting intracranial hemorrhage was assessed. Results An approximately 84.0% reduction in median volume CT dose index was found in the ultralow-dose CT protocol (5.6 mGy) compared with conventional-dose CT (35.02 mGy). Ultralow-dose CT with DLR significantly (p < 0.001) reduced image noise, improved signal-to-nosie ratio, and contrast-to-tnoise ratio compared with ultralow-dose CT with IR and conventional-dose CT. Ultralow-dose CT with DLR resulted in higher sensitivity (99.3% vs. 98.6%) and specificity (97.5% vs. 97.5%) for detecting intracranial hemorrhage than ultralow-dose CT with IR. Conclusion Ultralow-dose CT with DLR is not inferior to conventional-dose CT in terms of image quality and diagnostic performance for the detection of intracranial hemorrhage, while achieving an approximate 87.7% reduction in radiation dose.