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

TY - CHAP

T1 - A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography

AU - Banerjee, Jyotirmoy

AU - Patel, Premal A.

AU - Ushakov, Fred

AU - Peebles, Donald

AU - Deprest, Jan

AU - Ourselin, Sebastien

AU - Hawkes, David

AU - Vercauteren, Tom

PY - 2018/2/6

Y1 - 2018/2/6

N2 - 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.

AB - 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.

KW - Compositing

KW - Compounding

KW - Computational Sonography

KW - Image Registration

KW - Inverse Problem

KW - Tensor Imaging

KW - Total Variation

KW - Ultrasound

UR - http://www.scopus.com/inward/record.url?scp=85047362930&partnerID=8YFLogxK

U2 - 10.1117/12.2292501

DO - 10.1117/12.2292501

M3 - Conference paper

AN - SCOPUS:85047362930

VL - 10574

BT - Medical Imaging 2018

PB - SPIE

T2 - Medical Imaging 2018: Image Processing

Y2 - 11 February 2018 through 13 February 2018

ER -