Affiliations 

  • 1 Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
  • 2 Acute Vascular Imaging Centre, Radcliffe Department of Medicine, University of Oxford, UK
  • 3 Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia
  • 4 Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
  • 5 Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK; Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, UK. Electronic address: michael.chappell@eng.ox.ac.uk
Neuroimage Clin, 2019;23:101833.
PMID: 31063943 DOI: 10.1016/j.nicl.2019.101833

Abstract

BACKGROUND: Amide proton transfer (APT) imaging may help identify the ischaemic penumbra in stroke patients, the classical definition of which is a region of tissue around the ischaemic core that is hypoperfused and metabolically stressed. Given the potential of APT imaging to complement existing imaging techniques to provide clinically-relevant information, there is a need to develop analysis techniques that deliver a robust and repeatable APT metric. The challenge to accurate quantification of an APT metric has been the heterogeneous in-vivo environment of human tissue, which exhibits several confounding magnetisation transfer effects including spectrally-asymmetric nuclear Overhauser effects (NOEs). The recent literature has introduced various model-free and model-based approaches to analysis that seek to overcome these limitations.

OBJECTIVES: The objective of this work was to compare quantification techniques for CEST imaging that specifically separate APT and NOE effects for application in the clinical setting. Towards this end a methodological comparison of different CEST quantification techniques was undertaken in healthy subjects, and around clinical endpoints in a cohort of acute stroke patients.

METHODS: MRI data from 12 patients presenting with ischaemic stroke were retrospectively analysed. Six APT quantification techniques, comprising model-based and model-free techniques, were compared for repeatability and ability for APT to distinguish pathological tissue in acute stroke.

RESULTS: Robustness analysis of six quantification techniques indicated that the multi-pool model-based technique had the smallest contrast between grey and white matter (2%), whereas model-free techniques exhibited the highest contrast (>30%). Model-based techniques also exhibited the lowest spatial variability, of which 4-pool APTR∗ was by far the most uniform (10% coefficient of variation, CoV), followed by 3-pool analysis (20%). Four-pool analysis yielded the highest ischaemic core contrast-to-noise ratio (0.74). Four-pool modelling of APT effects was more repeatable (3.2% CoV) than 3-pool modelling (4.6% CoV), but this appears to come at the cost of reduced contrast between infarct growth tissue and normal tissue.

CONCLUSION: The multi-pool measures performed best across the analyses of repeatability, spatial variability, contrast-to-noise ratio, and grey matter-white matter contrast, and might therefore be more suitable for use in clinical imaging of acute stroke. Addition of a fourth pool that separates NOEs and semisolid effects appeared to be more biophysically accurate and provided better separation of the APT signal compared to the 3-pool equivalent, but this improvement appeared be accompanied by reduced contrast between infarct growth tissue and normal tissue.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.