Abstract: This research utilizes a deep learning-based image generation algorithm to generate pseudo-sagittal STIR sequences from sagittal T1WI and T2WI MR images. The evaluations include both subjective assessments by two physicians and objective analyses, measuring image quality through SNR and CNR in ROIs of five different tissues. Further analyses, including MAE, PSNR, SSIM, and COR, establish a strong correlation between the generated STIR sequences and the gold standard, with Bland-Altman analysis indicating pixel consistency. The findings indicate that the deep learning-generated STIR sequences not only align with but potentially surpass the gold standard in terms of image quality and clinical diagnostic capabilities. Moreover, the approach demonstrates promise for clinical implementation, offering reduced scan time and enhanced imaging efficiency.