Advertisement

Anatomy-Driven Modelling of Spatial Correlation for Regularisation of Arterial Spin Labelling Images

  • David OwenEmail author
  • Andrew Melbourne
  • Zach Eaton-Rosen
  • David L. Thomas
  • Neil Marlow
  • Jonathan Rohrer
  • Sebastien Ourselin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

Arterial spin labelling (ASL) allows blood flow to be measured in the brain and other organs of the body, which is valuable for both research and clinical use. Unfortunately, ASL suffers from an inherently low signal to noise ratio, necessitating methodological advances in ASL acquisition and processing. Spatial regularisation improves the effective signal to noise ratio, and is a common step in ASL processing. However, the standard spatial regularisation technique requires a manually-specified smoothing kernel of an arbitrary size, and can lead to loss of fine detail. Here, we present a Bayesian model of spatial correlation, which uses anatomical information from structural images to perform principled spatial regularisation, modelling the underlying signal and removing the need to set arbitrary smoothing parameters. Using data from a large cohort (N = 130) of preterm-born adolescents and age-matched controls, we show our method yields significant improvements in test-retest reproducibility, increasing the correlation coefficient by 14% relative to Gaussian smoothing and giving a corresponding improvement in statistical power. This novel technique has the potential to significantly improve single inversion time ASL studies, allowing more reliable detection of perfusion differences with a smaller number of subjects.

Notes

Acknowledgements

We acknowledge the MRC (MR/J01107X/1), the National Institute for Health Research (NIHR), the EPSRC (EP/H046410/1) and the NIHR University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative BW.mn.BRC10269). This work is supported by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1) and the Wolfson Foundation.

References

  1. 1.
    Alsop, D., et al.: Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications. MRM 73(1), 102–116 (2015)CrossRefGoogle Scholar
  2. 2.
    Buxton, R., et al.: A general kinetic model for quantitative perfusion imaging with arterial spin labeling. MRM 40(3), 383–396 (1998)CrossRefGoogle Scholar
  3. 3.
    Groves, A., Chappell, M., Woolrich, M.: Combined spatial and non-spatial prior for inference on MRI time-series. Neuroimage 45(3), 795–809 (2009)CrossRefGoogle Scholar
  4. 4.
    Orton, M., Collins, D., Koh, D., Leach, M.: Improved IVIM analysis of diffusion weighted imaging by data driven Bayesian modeling. MRM 71(1), 411–420 (2014)CrossRefGoogle Scholar
  5. 5.
    Melbourne, A., Toussaint, N., Owen, D., Simpson, I., Anthopoulos, T., De Vita, E., Atkinson, D., Ourselin, S.: NiftyFit: a software package for multi-parametric model-fitting of 4D magnetic resonance imaging data. Neuroinformatics 14(3), 319–337 (2016)CrossRefGoogle Scholar
  6. 6.
    Cardoso, M., Modat, M., Wolz, R., Melbourne, A., Cash, D., Rueckert, D., Ourselin, S.: Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging 34(9), 1976–1988 (2015)CrossRefGoogle Scholar
  7. 7.
    Chappell, M., Groves, A., MacIntosh, B., et al.: Partial volume correction of multiple inversion time arterial spin labeling MRI data. MRM 65(4), 1173–1183 (2011)CrossRefGoogle Scholar
  8. 8.
    Asllani, I., Borogovac, A., Brown, T.: Regression algorithm correcting for partial volume effects in arterial spin labeling MRI. MRM 60(6), 1362–1371 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • David Owen
    • 1
    Email author
  • Andrew Melbourne
    • 1
  • Zach Eaton-Rosen
    • 1
  • David L. Thomas
    • 1
    • 2
  • Neil Marlow
    • 3
  • Jonathan Rohrer
    • 2
  • Sebastien Ourselin
    • 1
  1. 1.Translational Imaging GroupUniversity College LondonLondonUK
  2. 2.Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
  3. 3.Institute for Women’s HealthUniversity College LondonLondonUK

Personalised recommendations