High-sensitivity CEST mapping using a spatiotemporal correlation-enhanced method

Research output: Contribution to journalComment/debatepeer-review

3 Scopus citations


Purpose: To obtain high-sensitivity CEST maps by exploiting the spatiotemporal correlation between CEST images. Methods: A postprocessing method accomplished by multilinear singular value decomposition (MLSVD) was used to enhance the CEST SNR by exploiting the correlation between the Z-spectrum for each voxel and the low-rank property of the overall CEST data. The performance of this method was evaluated using CrCEST in ischemic mouse brain at 11.7 tesla. Then, MLSVD CEST was applied to obtain Cr, amide, and amine CEST maps of the ischemic mouse brain to demonstrate its general applications. Results: Complex-valued Gaussian noise was added to CEST k-space data to mimic a low SNR situation. MLSVD CEST analysis was able to suppress the noise, recover the degraded CEST peak, and provide better CrCEST quality compared to the smoothing and singular value decomposition (SVD)-based denoising methods. High-resolution Cr, amide, and amine CEST maps of an ischemic stroke using MLSVD CEST suggest that CrCEST is also a sensitive pH mapping method, and a wide range of pH changes can be detected by combing CrCEST with amine CEST at high magnetic fields. Conclusion: MLSVD CEST provides a simple and efficient way to improve the SNR of CEST images.

Original languageEnglish (US)
Pages (from-to)3342-3350
Number of pages9
JournalMagnetic resonance in medicine
Issue number6
StatePublished - Dec 1 2020


  • amide
  • amine
  • chemical exchange saturation transfer (CEST)
  • creatine (Cr)
  • ischemic stroke
  • multilinear singular value decomposition (MLSVD)
  • pH mapping
  • polynomial and Lorentzian line-shape fitting (PLOF)
  • singular value decomposition (SVD)

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'High-sensitivity CEST mapping using a spatiotemporal correlation-enhanced method'. Together they form a unique fingerprint.

Cite this