2.3. Image Preprocessing and Feature Extraction

All imaging features described above were extracted for the patients available in this study using the AnToNIa software tool [36]. Briefly described, DWI acquired with diffusion weightings of b = 0 and b = 1000 s/mm² were used to calculate a quantitative apparent diffusion coefficient (ADC) map, which was then used to segment the brain tissue, cerebrospinal fluid (ADC > 1200 × 10⁻⁶ mm²/s), and the ischemic core (ADC < 550 × 10⁻⁶ mm²/s). Subsequently, the ischemic core segmentation was used to compute a map of the shortest Euclidean distance from each voxel to the ischemic core. For each PWI dataset, maps of cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and time-to-maximum of the residual curve (Tmax) were calculated using a block-circulant singular value decomposition with a threshold of 0.15 [37]. The arterial input function needed for this was automatically identified using an atlas-based approach. For each follow-up image, an experienced medical expert used AnToNIa to manually segment the final lesion, yielding a binary mask of the real tissue outcome. Finally, image registration was used to transfer each perfusion parameter map (CBV, CBF, MTT, Tmax), the tissue type information (voxel-wise white and gray matter probabilities) and region labels from the MNI atlas, and the follow-up real tissue outcome to the patient’s ADC space. Clinical variables were associated with each voxel of the ADC image by simple repetition. As a result, each voxel was associated with a total of 12 features: ADC, distance to ischemic core, tissue type, anatomical location, CBV, MTT, Tmax, CBF, NIHSS, age, sex, and time from symptom onset, whereas the real tissue outcome was used as the outcome variable for training and testing of the machine learning models.