Neural field patient-specific super resolution for enhanced 1.5 Tesla brain MRI visualization

Brain magnetic resonance imaging serves as a cornerstone of preoperative neurosurgical assessment. Neural fields represent an emerging machine learning approach capable of super-resolution reconstruction and novel view synthesis without requiring large training datasets. We evaluated ten 1.5-Tesla brain MRI sequences (nine anisotropic and one isotropic) to train patient-specific neural field models using a proprietary framework (Radscaler(C)). Image quality assessment was performed on reconstructions upscaled by factors of 2x, 3x, and 4x relative to original resolution. The method achieved favorable quality metrics across all scaling factors: mean SSIM of 0.85 ({+/-}0.04), MS-SSIM of 0.95 ({+/-}0.01), and LPIPS of 0.09 ({+/-}0.04). Neural field reconstruction enabled enhanced visualization of micro-anatomical structures through improved spatial resolution and interpolation of intermediate views not present in the original acquisition. These findings demonstrate that neural fields provide a clinically viable approach for volumetric MRI super-resolution and novel view synthesis, particularly valuable for addressing anisotropic acquisition limitations in neurosurgical planning.