Background and purpose: To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. Materials and methods: We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. Results: A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher (P=0.003). Conclusion: Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.
De Vito, A., Maino, C., Lombardi, S., Ragusi, M., Talei Franzesi, C., Ippolito, D., et al. (2021). Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations. THE NEURORADIOLOGY JOURNAL.
|Citazione:||De Vito, A., Maino, C., Lombardi, S., Ragusi, M., Talei Franzesi, C., Ippolito, D., et al. (2021). Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations. THE NEURORADIOLOGY JOURNAL.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations|
|Autori:||De Vito, A; Maino, C; Lombardi, S; Ragusi, M; Talei Franzesi, C; Ippolito, D; Sironi, S|
IPPOLITO, DAVIDE (Corresponding)
|Data di pubblicazione:||2021|
|Rivista:||THE NEURORADIOLOGY JOURNAL|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1177/19714009211008751|
|Appare nelle tipologie:||01 - Articolo su rivista|