Advertisement

Image Quality Transfer via Random Forest Regression: Applications in Diffusion MRI

  • Daniel C. Alexander
  • Darko Zikic
  • Jiaying Zhang
  • Hui Zhang
  • Antonio Criminisi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

This paper introduces image quality transfer. The aim is to learn the fine structural detail of medical images from high quality data sets acquired with long acquisition times or from bespoke devices and transfer that information to enhance lower quality data sets from standard acquisitions. We propose a framework for solving this problem using random forest regression to relate patches in the low-quality data set to voxel values in the high quality data set. Two examples in diffusion MRI demonstrate the idea. In both cases, we learn from the Human Connectome Project (HCP) data set, which uses an hour of acquisition time per subject, just for diffusion imaging, using custom built scanner hardware and rapid imaging techniques. The first example, super-resolution of diffusion tensor images (DTIs), enhances spatial resolution of standard data sets with information from the high-resolution HCP data. The second, parameter mapping, constructs neurite orientation density and dispersion imaging (NODDI) parameter maps, which usually require specialist data sets with two b-values, from standard single-shell high angular resolution diffusion imaging (HARDI) data sets with b = 1000 s mm− 2. Experiments quantify the improvement against alternative image reconstructions in comparison to ground truth from the HCP data set in both examples and demonstrate efficacy on a standard data set.

Keywords

Input Data Point Random Forest Regression Central Voxel Full Resolution Image Input Patch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sotiropoulos, S.N., et al.: Advances in diffusion MRI acquisition and processing in the human connectome project. NeuroImage 80, 125–143 (2013)CrossRefGoogle Scholar
  2. 2.
    Zhang, H., et al.: NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61, 1000–1016 (2012)CrossRefGoogle Scholar
  3. 3.
    Rousseau, F.: Brain Hallucination. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 497–508. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Rueda, A., Malpica, N., Romero, E.: Single-image super-resolution of brain MR images using overcomplete dictionaries. Med. Im. An. 17(1), 113–132 (2013)CrossRefGoogle Scholar
  5. 5.
    Coupé, P., Manjón, J.V., Chamberland, M., Descoteaux, M., Hiba, B.: Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage 83, 245–261 (2013)CrossRefGoogle Scholar
  6. 6.
    Ye, D.H., Zikic, D., Glocker, B., Criminisi, A., Konukoglu, E.: Modality propagation: coherent synthesis of subject-specific scans with data-driven regularisation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 606–613. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Criminisi, A., Shotton, J.: Decision forests for computer vision and medical image analysis. Springer (2013)Google Scholar
  9. 9.
    Seunarine, K.K., Alexander, D.C.: Multiple fibers: beyond the diffusion tensor. In: Johansen-Berg, H., Behrens, T.E.J. (eds.) Diffusion MRI: from Quantitative Measurement to in Vivo Neuroanatomy, pp. 56–74. Academic Press (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daniel C. Alexander
    • 1
  • Darko Zikic
    • 2
  • Jiaying Zhang
    • 1
  • Hui Zhang
    • 1
  • Antonio Criminisi
    • 2
  1. 1.Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUK
  2. 2.Microsoft Research CambridgeCambridgeUK

Personalised recommendations