Beyond the Resolution Limit: Diffusion Parameter Estimation in Partial Volume

  • Zach Eaton-Rosen
  • Andrew Melbourne
  • M. Jorge Cardoso
  • Neil Marlow
  • Sebastien Ourselin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)


Diffusion MRI is a frequently-used imaging modality that can infer microstructural properties of tissue, down to the scale of microns. For single-compartment models, such as the diffusion tensor (DT), the model interpretation depends on voxels having homogeneous composition. This limitation makes it difficult to measure diffusion parameters for small structures such as the fornix in the brain, because of partial volume. In this work, we use a segmentation from a structural scan to calculate the tissue composition for each diffusion voxel. We model the measured diffusion signal as a linear combination of signals from each of the tissues present in the voxel, and fit parameters on a per-region basis by optimising over all diffusion data simultaneously. We test the proposed method by using diffusion data from the Human Connectome Project (HCP). We downsample the HCP data, and show that our method returns parameter estimates that are closer to the high-resolution ground truths than for classical methods. We show that our method allows accurate estimation of diffusion parameters for regions with partial volume. Finally, we apply the method to compare diffusion in the fornix for adults born extremely preterm and matched controls.


Fractional Anisotropy Diffusion Tensor Diffusion Data Diffusion Parameter Tissue Class 



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

HCP data were provided by the HCP, WU-Minn Consortium (PIs: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by NIH and Wash. U.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zach Eaton-Rosen
    • 1
  • Andrew Melbourne
    • 1
  • M. Jorge Cardoso
    • 1
  • Neil Marlow
    • 2
  • Sebastien Ourselin
    • 1
  1. 1.Translational Imaging Group, Centre for Medical Image ComputingUniversity College LondonLondonUK
  2. 2.Academic NeonatologyEGA UCL Institute for Women’s HealthLondonUK

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