Introduction Pediatric brain tumors are the most common solid tumors in children and a leading cause of cancerrelated mortality. Magnetic resonance imaging (MRI) is the primary tool for assessing tumor progression and treatment response. However, distinguishing treatment effects from tumor progression remains a challenge, complicating clinical decision-making. Phosphorus-31 Magnetic Resonance Spectroscopic Imaging (31P-MRSI) is a non-invasive technique that provides spatially resolved metabolic information by detecting phosphorus-containing metabolites, such as phosphocreatine (PCr), adenosine triphosphate (ATP), and phosphomonoesters (PME). These metabolites offer insights into energy metabolism, cell membrane turnover, and intracellular pH regulation, which could help differentiate tumor progression from treatment-related changes. A major challenge in 31P-MRSI is its low signal-to-noise ratio (SNR), due to the relatively low gyromagnetic ratio and the low concentration of phosphorus metabolites in tissues. This necessitates large voxel sizes, leading to partial volume effects, where a voxel contains signals from multiple tissue types (e.g., tumor and normal-appearing white matter (NAWM)). To address this, post-processing regridding techniques can be applied to recalculate the spatial position of the 31P-MRSI signal and improve grid alignment with anatomical structures. Aim This study investigates post-processing regridding techniques to optimize spatial localization in 31PMRSI and evaluates longitudinal metabolic changes in pediatric brain tumors and NAWM. Methods Phantom and in vivo clinical data were acquired using a 7T MRI scanner. In the phantom experiment, a spherical phantom with inorganic phosphate (Pi) was scanned with different MRSI grid configurations. Two post-processing regridding techniques were evaluated by comparing regridded data to a reference: 1) K-space phase adjustment, which applies a phase shift before Fourier transformation, and 2) Interpolation-based regridding, which estimates spectral data at shifted voxel positions. Correlation analysis was performed with Pearsons correlation. Following phantom validation, the best-performing regridding technique was applied to in vivo pediatric brain tumor data. The impact of regridding was assessed by analyzing metabolic ratio changes (PE/GPE, PCr/γATP, pH, Pi/ATP) in tumors and NAWM, alongside spectral quality metrics such as SNR and linewidth. Additionally, longitudinal data from eight pediatric patients with low-grade glioma were analyzed to assess metabolic ratio changes over time. Patients underwent different chemotherapy regimens, enabling an evaluation of therapy-related metabolic alterations. Results K-space phase adjustment performed superior compared to the spatial interpolation, evidenced by higher correlations and more accurate peak intensities in voxels near the Pi bead. In in vivo data, regridding improved voxel alignment with tumor regions, increasing tumor contribution within individual voxels. Spectral quality (SNR and linewidth) remained stable after regridding, and metabolic ratios were not significantly altered. Longitudinal analysis revealed metabolic changes in tumors and NAWM over time. However, normalizing tumor metabolic ratios to NAWM did not produce consistent trends across patients. Conclusion Regridding enhances spatial localization in 31P-MRSI without degrading spectral quality, making it a viable post-processing approach. While metabolic alterations in tumors and NAWM were observed, they did not reach statistical significance. Further validation in larger cohorts is required to assess the clinical significance of these metabolic changes