Estimating And Resolving Uncertainty In Cardiac Respiratory Motion Modelling

TY - CHAP

T1 - Estimating And Resolving Uncertainty In Cardiac Respiratory Motion Modelling

AU - Peressutti, D.

AU - Rijkhorst, E. -J.

AU - Barratt, D. C.

AU - Penney, G. P.

AU - King, A. P.

PY - 2012

Y1 - 2012

N2 - We present a novel method for cardiac respiratory motion estimation in image-guided interventions. The technique combines a preprocedure affine motion model and intra-procedure real-time images to estimate and correct for the respiratory motion of the heart. As well as making motion estimates, the model is able to quantify the uncertainty in these estimates. This uncertainty is resolved using a Bayesian approach based on a prior probability from the motion model and a likelihood term derived from the intraprocedure images. The proposed method is validated using MR-derived motion fields and simulated 3D real-time echocardiography data for 4 volunteers and compared to 3 other motion estimation techniques. Our Bayesian approach shows improvements in respiratory motion estimation for each volunteer of 7.0%, 4.6%, 7.7% and 5.3% respectively, compared to the use of a motion model only.

AB - We present a novel method for cardiac respiratory motion estimation in image-guided interventions. The technique combines a preprocedure affine motion model and intra-procedure real-time images to estimate and correct for the respiratory motion of the heart. As well as making motion estimates, the model is able to quantify the uncertainty in these estimates. This uncertainty is resolved using a Bayesian approach based on a prior probability from the motion model and a likelihood term derived from the intraprocedure images. The proposed method is validated using MR-derived motion fields and simulated 3D real-time echocardiography data for 4 volunteers and compared to 3 other motion estimation techniques. Our Bayesian approach shows improvements in respiratory motion estimation for each volunteer of 7.0%, 4.6%, 7.7% and 5.3% respectively, compared to the use of a motion model only.

KW - Respiratory motion

KW - modelling

KW - MRI

KW - echocardiography

KW - Bayesian estimation

U2 - 10.1109/ISBI.2012.6235534

DO - 10.1109/ISBI.2012.6235534

M3 - Conference paper

SN - 9781457718588

VL - N/A

SP - 262

EP - 265

BT - 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

PB - IEEE

CY - Piscataway, N.J.

T2 - 9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

Y2 - 2 May 2012 through 5 May 2012

ER -