PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
January 26, 2026Magnetic Resonance Materials in Physics Biology and Medicine5 citationsOpen Access

Advanced methods in deuterium metabolic imaging

View Full Paper
FNFabian NiessBSBernhard StrasserBLBernard Lanz

Key Points

  • This review summarizes advances in deuterium metabolic imaging's methods, focusing on technical improvements that enhance metabolic mapping.
  • Review of hardware modifications and dual-tuned coils for improved 3D imaging.
  • Overview of spatial–spectral encoding techniques for better signal-to-noise ratio.
  • Description of AI-driven methods for enhanced spectral fitting and denoising.
  • Discussion on indirect 1H-observed deuterium detection for systems lacking multinuclear hardware.
  • Advancements allow improved spatial and temporal resolution in imaging.
  • DMI is positioned as a strong alternative to FDG-PET and 13C-MRS for studying metabolic processes.
  • New techniques address challenges in concentration estimation and kinetic modeling.

Abstract

Abstract Background Deuterium metabolic imaging (DMI) has recently been established as a versatile MR-based technique for in vivo mapping of glucose and other metabolic pathways using safe, non-ionizing 2 H-labeled tracers. Objective In this review, methodological advances in DMI over the past decade are summarized, spanning hardware, acquisition, reconstruction, and quantification. Approach and Outline Developments in multinuclear system modifications and dual-tuned head and body coils that enable 3D DMI at clinical and ultra-high field strengths are outlined. Efficient spatial–spectral encoding strategies and balanced steady-state-free-precession-based MRSI, which improve SNR efficiency and spatiotemporal resolution, are reviewed together with temporally interleaved 1 H/ 2 H acquisitions that integrate DMI into standard MRI workflows. Indirect 1 H-observed deuterium detection (QELT) is described as a complementary approach for sites without multinuclear hardware. On the reconstruction side, model-based, low-rank and AI-driven methods are highlighted for de-noising, accelerated sampling, and robust spectral–temporal fitting. Outlook Current strategies for concentration estimation, kinetic modeling, and treatment of label loss are discussed, positioning DMI as a promising complement to FDG-PET and 13 C-MRS for studying metabolism in neurological, oncological and systemic disease.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Niess et al. (2026) studied this question.

synapsesocial.com/papers/6977032e722626c4468e83f7https://doi.org/10.1007/s10334-026-01322-1
Ask AI
Helpful
Bookmark
Share
View Full Paper