Impact of model-based iterative reconstruction (MBIR) on image quality in cerebral CT angiography before and after intracranial aneurysm treatment

Purpose: To subjectively and objectively assess the impact of model-based iterative reconstruction(MBIR) on image quality in cerebral computed tomography angiography compared to adaptive statistical iterative reconstruction (ASIR). Methods: 107 patients (mean age: 58 +/- 14 years) were included prior to (n = 38) and after (n = 69) intracranial aneurysm treatment. Images were acquired using a routine protocol and reconstructed with MBIR and ASIR. Image noise, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios in the internal carotid and middle cerebral arteries were compared between MBIR and ASIR using the Wilcoxon signed-rank test. Additionally, two neuroradiologists subjectively assessed noise, artefacts, vessel sharpness and overall quality using a semi-quantitative assessment scale. Results: Objective assessment revealed that MBIR reduced noise (p < 0.0001) and additionally improved SNR (p < 0.0001) and CNR (p < 0.0001) compared to ASIR in untreated and treated patients. Subjective assessment revealed that in untreated patients, MBIR improved noise reduction, artefacts, vessel sharpness and overall quality relative to ASIR (p < 0.0001). In the treated groups, noise and vessel sharpness were improved (p < 0.0001) with no change in artefacts on images reconstructed with MBIR compared to ASIR. Conclusion: MBIR significantly improves noise, SNR, CNR and vessel sharpness in untreated and treated patients with intracranial aneurysms. MBIR does not reduce artefacts generated by metallic devices following intracranial aneurysm treatment.