A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography

Jyotirmoy Banerjee, Premal A. Patel, Fred Ushakov, Donald Peebles, Jan Deprest, Sebastien Ourselin, David Hawkes, Tom Vercauteren

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)
115 Downloads (Pure)


We propose a spatial compounding technique and variational framework to improve 3D ultrasound image quality by compositing multiple ultrasound volumes acquired from different probe orientations. In the composite volume, instead of intensity values, we estimate a tensor at every voxel. The resultant tensor image encapsulates the directional information of the underlying imaging data and can be used to generate ultrasound volumes from arbitrary, potentially unseen, probe positions. Extending the work of Hennersperger et al.,1 we introduce a log-Euclidean framework to ensure that the tensors are positive-definite, eventually ensuring non-negative images. Additionally, we regularise the underpinning ill-posed variational problem while preserving edge information by relying on a total variation penalisation of the tensor field in the log domain. We present results on in vivo human data to show the efficacy of the approach.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage Processing
ISBN (Electronic)9781510616370
Publication statusPublished - 6 Feb 2018
EventMedical Imaging 2018: Image Processing - Houston, United States
Duration: 11 Feb 201813 Feb 2018


ConferenceMedical Imaging 2018: Image Processing
Country/TerritoryUnited States


  • Compositing
  • Compounding
  • Computational Sonography
  • Image Registration
  • Inverse Problem
  • Tensor Imaging
  • Total Variation
  • Ultrasound


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