A log-Euclidean and total variation based variational framework for computational sonography

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

doi:10.1117/12.2292501

2 March 2018 A log-Euclidean and total variation based variational framework for computational sonography

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

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105740D (2018) https://doi.org/10.1117/12.2292501
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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.

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Citation Download Citation

Jyotirmoy Banerjee, Premal A. Patel, Fred Ushakov, Donald Peebles, Jan Deprest, Sébastien Ourselin, David Hawkes, and Tom Vercauteren "A log-Euclidean and total variation based variational framework for computational sonography", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740D (2 March 2018); https://doi.org/10.1117/12.2292501

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