High-resolution magnetic resonance spectroscopic imaging (MRSI) plays a crucial role in characterizing tumor metabolism and guiding clinical decisions for glioma patients. However, due to inherently low metabolite concentrations and signal-to-noise ratio (SNR) limitations, MRSI data are often acquired at low spatial resolution, hindering accurate visualization of tumor heterogeneity and margins. In this study, we propose a novel deep learning framework based on conditional denoising diffusion probabilistic models for super-resolution reconstruction of MRSI, with a particular focus on mutant isocitrate dehydrogenase (IDH) gliomas. The model progressively transforms noise into high-fidelity metabolite maps through a learned reverse diffusion process, conditioned on low-resolution inputs. Leveraging a Self-Attention UNet backbone, the proposed approach integrates global contextual features and achieves superior detail preservation. On simulated patient data, the proposed method achieved Structural Similarity Index Measure (SSIM) values of 0. 956, 0. 939, and 0. 893; Peak Signal-to-Noise Ratio (PSNR) values of 29. 73, 27. 84, and 26. 39 dB; and Learned Perceptual Image Patch Similarity (LPIPS) values of 0. 025, 0. 036, and 0. 045 for upsampling factors of 2, 4, and 8, respectively, with LPIPS improvements statistically significant compared to all baselines (p < 0. 01). We validated the framework on in vivo MRSI from healthy volunteers and glioma patients, where it accurately reconstructed small lesions, preserved critical textural and structural information, and enhanced tumor boundary delineation in metabolic ratio maps, revealing heterogeneity not visible in other approaches. These results highlight the promise of diffusion-based deep learning models as clinically relevant tools for noninvasive, high-resolution metabolic imaging in glioma and potentially other neurological disorders.
Alsubaie et al. (Tue,) studied this question.