Super-Resolution MR Spectroscopic Imaging via Diffusion Models for Tumor Metabolism Mapping.
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 acquir...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 3; pp. 2163 - 2175 |
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| Autores principales: | , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194225538&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194225538 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2026 vid: 39 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194225538 194225538 194225538 10.1007/s10278-025-01652-x 194225538 ppf: 2163 ppct: 12 formats: tig: atl: Super-Resolution MR Spectroscopic Imaging via Diffusion Models for Tumor Metabolism Mapping. aug: au: Alsubaie, Mohammed Perera, Sirani M. Gu, Linxia Subasi, Sean B. Andronesi, Ovidiu C. Li, Xianqi affil: https://ror.org/014g1a453 Department of Mathematics, College of Khurma University College, Taif Univeristy, 21944, Taif, Saudi Arabia sug: subj: Magnetic Resonance Spectroscopy Methods Image Processing, Computer Assisted Evaluation Glioma Metabolism Mutation Deep Learning Oxidoreductases Diffusion of Innovation Human Female Male Scales Prediction Algorithms Conceptual Framework Algorithms Software Mathematics Repeated Measures One-Way Analysis of Variance Post Hoc Analysis Matched-Pair Analysis Data Analysis Software Descriptive Statistics Female Male ab: 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. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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