Purpose <p>Computed tomography perfusion (CTP) imaging for acute ischemic stroke relies on accurately identifying hypoperfused brain tissue to guide treatment decisions. However, deconvolution-based methods often suffer from variability in perfusion parameters and lesion volumes across different software. This study evaluated the feasibility of temporal fractal analysis, specifically, time series-derived fractal dimension (FD) using the Higuchi method, as a biomarker for detecting hypoperfused brain tissue.</p> Methods <p>Fractal analysis was applied to voxel-wise time-series data from both simulated phantom datasets and 149 CTP images from the publicly available Ischemic Stroke Lesion Segmentation (ISLES) 2024 dataset. FD was calculated using optimized parameters determined through the phantom study. In the patient study, the ischemic core was defined by follow-up MRI, and the penumbra was defined as tissue with Tmax &gt; 6&#xa0;s. FD values were statistically compared between core, penumbra, and normal tissue. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis.</p> Results <p>In the phantom study, FD showed a strong correlation (ρ &gt; 0.9) with true cerebral blood flow (CBF) across all cerebral blood volume (CBV) values when the tuning parameter <i>k</i>_<i>max</i> was optimized based on the number of CTP frames. In the patient study, FD differed significantly across tissue types (<i>p</i> &lt; 0.001). For penumbra versus normal classification, FD achieved an AUC of 0.732, outperforming CBF and CBV (<i>p</i> &lt; 0.001). In core versus penumbra classification, FD showed the highest AUC of 0.641 among all metrics.</p> Conclusion <p>Time series-derived FD offers a promising approach to characterizing perfusion abnormalities in stroke, with potential as a complementary metric to conventional CTP parameters.</p>