Background: Small, unrandomized studies have indicated that pharmacologically induced blood pressure elevation may improve function in ischemic stroke, presumably by improving blood flow to ischemic, but noninfarcted tissue (which may be indicated by diffusion-perfusion mismatch on MRI). We conducted a pilot, randomized trial to evaluate effects of pharmacologically induced blood pressure elevation on function and perfusion in acute stroke. Methods: Consecutive series of patients with large diffusion-perfusion mismatch were randomly assigned to induced blood pressure elevation (‘treated’ patients, n = 9) or conventional management (‘untreated’ patients, n = 6). Results: There were no significant differences between groups at baseline. NIH Stroke Scale (NIHSS) scores were lower (better) in treated versus untreated patients at day 3 (mean 5.6 vs. 12.3; p = 0.01) and week 6–8 (mean 2.8 vs. 9.7; p < 0.04). Treated (but not untreated) patients showed significant improvement from day 1 to day 3 in NIHSS score (from mean 10.2 to 5.6; p < 0.002), cognitive score (from mean 58.7 to 27.9% errors; p < 0.002), and volume of hypoperfused tissue (mean 132 to 58 ml; p < 0.02). High Pearson correlations between the mean arterial pressure (MAP) and accuracy on daily cognitive tests indicated that functional changes were due to changes in MAP. Conclusion: Results warrant a full-scale, double-blind clinical trial to evaluate the efficacy and risk of induced blood pressure elevation in selective patients with acute/subacute stroke.

Multi-label brain segmentation from brain magnetic resonance imaging (MRI) provides valuable structural information for most neurological analyses. Due to the complexity of the brain segmentation algorithm, it could delay the delivery of neuroimaging findings. Therefore, we introduce Split-Attention U-Net (SAU-Net), a convolutional neural network with skip pathways and a split-attention module that segments brain MRI scans. The proposed architecture employs split-attention blocks, skip pathways with pyramid levels, and evolving normalization layers. For efficient training, we performed pre-training and fine-tuning with the original and manually modified FreeSurfer labels, respectively. This learning strategy enables involvement of heterogeneous neuroimaging data in the training without the need for many manual annotations. Using nine evaluation datasets, we demonstrated that SAU-Net achieved better segmentation accuracy with better reliability that surpasses those of state-of-the-art methods. We believe that SAU-Net has excellent potential due to its robustness to neuroanatomical variability that would enable almost instantaneous access to accurate neuroimaging biomarkers and its swift processing runtime compared to other methods investigated.

Purpose This study aimed to examine the inter-method reliability and volumetric differences between NeuroQuant (NQ) and Freesurfer (FS) using T1 volume imaging sequence with different slice thicknesses in patients with mild cognitive impairment (MCI). Materials and Methods This retrospective study enrolled 80 patients diagnosed with MCI at our memory clinic. NQ and FS were used for volumetric analysis of three-dimensional T1-weighted images with slice thickness of 1 and 1.2 mm. Inter-method reliability was measured with Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and effect size (ES). Results Overall, NQ volumes were larger than FS volumes in several locations: whole brain (0.78%), cortical gray matter (5.34%), and white matter (2.68%). Volume measures by NQ and FS showed good-to-excellent ICCs with both 1 and 1.2 mm slice thickness (ICC=0.75–0.97, ES=−1.0–0.73 vs. ICC=0.78–0.96, ES=−0.9–0.77, respectively), except for putamen, pallidum, thalamus, and total intracranial volumes. The ICCs in all locations, except the putamen and cerebellum, were slightly higher with a slice thickness of 1 mm compared to those of 1.2 mm. Conclusion Inter-method reliability between NQ and FS was good-to-excellent in most regions with improvement with a 1-mm slice thickness. This finding indicates that the potential effects of slice thickness should be considered when performing volumetric measurements for cognitive impairment.