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Lorentzian-Corrected Apparent Exchange-Dependent Relaxation (LAREX) Ω-Plot Analysis-An Adaptation for qCEST in a Multi-Pool System: Comprehensive In Silico, In Situ, and In Vivo Studies.

Karl Ludger RadkeLena Marie WilmsMichael FrenkenJulia StabinskaMarek KnetBenedikt KampThomas Andreas ThielTimm Joachim FillerSven NebelungGerald AntochDaniel Benjamin AbrarHans-Jörg WittsackAnja Müller-Lutz
Published in: International journal of molecular sciences (2022)
Based on in silico, in situ, and in vivo studies, this study aims to develop a new method for the quantitative chemical exchange saturation transfer (qCEST) technique considering multi-pool systems. To this end, we extended the state-of-the-art apparent exchange-dependent relaxation (AREX) method with a Lorentzian correction (LAREX). We then validated this new method with in situ and in vivo experiments on human intervertebral discs (IVDs) using the Kendall-Tau correlation coefficient. In the in silico experiments, we observed significant deviations of the AREX method as a function of the underlying exchange rate (k ba ) and fractional concentration (f b ) compared to the ground truth due to the influence of other exchange pools. In comparison to AREX, the LAREX-based Ω-plot approach yielded a substantial improvement. In the subsequent in situ and in vivo experiments on human IVDs, no correlation to the histological reference standard or Pfirrmann classification could be found for the f b (in situ: τ = -0.17 p = 0.51; in vivo: τ = 0.13 p = 0.30) and k ba (in situ: τ = 0.042 p = 0.87; in vivo: τ = -0.26 p = 0.04) of Glycosaminoglycan (GAG) with AREX. In contrast, the influence of interfering pools could be corrected by LAREX, and a moderate to strong correlation was observed for the fractional concentration of GAG for both in situ (τ = -0.71 p = 0.005) and in vivo (τ = -0.49 p < 0.001) experiments. The study presented here is the first to introduce a new qCEST method that enables qCEST imaging in systems with multiple proton pools.
Keyphrases
  • endothelial cells
  • high resolution
  • molecular docking
  • machine learning
  • induced pluripotent stem cells
  • computed tomography
  • case control