This study aimed to investigate the impact of selected analysis conditions on blood flow values and color maps in canine brain perfusion computed tomography (PCT) and to propose optimal analysis conditions. Dynamic computed tomography imaging was performed on six beagle dogs. Color maps were generated using a combination of analysis algorithms (box-modulation transfer function (Box-MTF) and singular value deconvolution plus (SVD+) methods), slice thicknesses (4.0 and 8.0 mm), analysis matrix sizes (512 × 512, 256 × 256, and 128 × 128), and noise reduction levels (strong and weak). Cerebral blood flow (CBF) and cerebral blood volume (CBV) were calculated for gray matter, white matter, basal ganglia, hippocampus, thalamus, and cerebellum in each map. CBF and CBV values obtained using SVD+ were significantly higher than those obtained using Box-MTF. Noise reduction was more effective with larger matrix sizes
however, excessive noise reduction led to the blurring of anatomical structures in the color map. Across all analysis algorithms, anatomical structures were challenging to visualize at 8.0 mm. For canine brain PCT, it is essential to choose a straightforward algorithm that remains unaffected by circulatory velocity or intracranial bone structure. Given the brain's size, the slice thickness should be minimal, noise reduction level should be suitable for the targeted area, and matrix size should be maximized.