Abstract
In this study, we aimed to determine whether iterative model reconstruction designed for brain CT (IMR-neuro) would improve the accuracy of posterior fossa stroke diagnosis on brain CT. We enrolled 37 patients with ischaemic stroke in the posterior fossa and 37 patients without stroke (controls). Using axial images reconstructed using filtered back-projection (FBP) and IMR-neuro, we compared the CT numbers in infarcted areas, image noise in the pons, and contrast-to-noise ratios (CNRs) of infarcted and non-infarcted areas on scans subjected to IMR-neuro and FBP. To analyse the performance of hypo-attenuation detection, we used receiver-operating characteristic (ROC) curve techniques. The image noise was significantly lower (2.2 ± 0.5 vs. 5.1 ± 0.9 Hounsfield units, p < 0.01) and the difference in CNR between the infarcted and non-infarcted areas was significantly higher with IMR-neuro than with FBP (2.2 ± 1.7 vs. 4.0 ± 3.6, p < 0.01). Furthermore, the average area under the ROC curve was significantly higher with IMR-neuro (0.90 vs. 0.86 for FBP, p = 0.04). IMR-neuro yielded better image quality and improved hypo-attenuation detection in patients with ischaemic stroke. • Iterative model reconstruction of brain CT data can facilitate the diagnosis of ischaemic stroke. • IMR improved the detectability of low-contrast lesions in the posterior fossa. • IMR-neuro yielded better image quality and improved observer performance.
Published Version
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