Accuracy of thin-slice model-based iterative reconstruction designed for brain CT to diagnose acute ischemic stroke in the middle cerebral artery territory: a multicenter study

Purpose Model-based iterative reconstruction (MBIR) yields higher spatial resolution and a lower image noise than conventional reconstruction methods. We hypothesized that thin-slice MBIR designed for brain CT could improve the detectability of acute ischemic stroke in the middle cerebral artery (MCA) territory. Methods Included were 41 patients with acute ischemic stroke in the MCA territory; they were seen at 4 medical centers. The controls were 39 subjects without acute stroke. Images were reconstructed with hybrid IR and with MBIR designed for brain CT at slice thickness of 2 mm. We measured the image noise in the ventricle and compared the contrast-to-noise ratio (CNR) in the ischemic lesion. We analyzed the ability of reconstructed images to detect ischemic lesions using receiver operating characteristics (ROC) analysis; 8 observers read the routine clinical hybrid IR with 5 mm-thick images, while referring to 2 mm-thick hybrid IR images or MBIR images. Results The image noise was significantly lower on MBIR- than hybrid IR images (1.2 vs. 3.4, p < 0.001). The CNR was significantly higher with MBIR than hybrid IR (6.3 vs. 1.6, p < 0.001). The mean area under the ROC curve was also significantly higher on hybrid IR plus MBIR than hybrid IR (0.55 vs. 0.48, p < 0.036). Sensitivity, specificity, and accuracy were 41.2%, 88.8%, and 65.7%, respectively, for hybrid IR; they were 58.8%, 86.1%, and 72.9%, respectively, for hybrid IR plus MBIR. Conclusion The additional thin-slice MBIR designed for brain CT may improve the detection of acute MCA stroke.