Sparse Bayesian Registration

International audienceWe propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely over-complete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of model parameters (e.g. noise and regularization levels). We demonstrate the feasibility and performance of our approach on cine MR, tagged MR and $3$D US cardiac images, and show state-of-the-art results on benchmark data sets evaluating accuracy of motion and strain

Quantifying Registration Uncertainty with Sparse Bayesian Modelling

International audienceWe investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion. In earlier work, approximate inference under the sparse Bayesian model was tackled in an efficient Variational Bayes (VB) framework. In this paper we are interested in the theoretical and empirical quality of uncertainty estimates derived under this approximate scheme vs. under the exact model. We implement an (asymptotically) exact inference scheme based on reversible jump Markov Chain Monte Carlo (MCMC) sampling to characterize the posterior distribution of the transformation and compare the predictions of the VB and MCMC based methods. The true posterior distribution under the sparse Bayesian model is found to be meaningful: orders of magnitude for the estimated uncertainty are quantitatively reasonable, the uncertainty is higher in textureless regions and lower in the direction of strong intensity gradients

Deformation Estimation and Assessment of Its Accuracy in Ultrasound Images

This thesis aims to address two problems; one in ultrasound elastography and one in image registration. The first problem entails estimation of tissue displacement in Ultrasound Elastography (UE). UE is an emerging technique used to estimate mechanical properties of tissue. It involves calculating the displacement field between two ultrasound Radio Frequency (RF) frames taken before and after a tissue deformation. A common way to calculate the displacement is to use correlation based approaches. However, these approaches fail in the presence of signal decorrelation. To address this issue, Dynamic Programming was used to find the optimum displacement using all the information on the RF-line. Although taking this approach improved the results, some failures persisted. In this thesis, we have formulated the DP method on a tree. Doing so allows for more information to be used for estimating the displacement and therefore reducing the error. We evaluated our method on simulation, phantom and real patient data. Our results shows that the proposed method outperforms the previous method in terms of accuracy with small added computational cost. In this work, we also address a problem in image registration. Although there is a vast literature in image registration, quality evaluation of registration is a field that has not received as much attention. This evaluation becomes even more crucial in medical imaging due to the sensitive nature of the field. We have addressed the said problem in the context of ultrasound guided radiotherapy. Image guidance has become an important part of radiotherapy wherein image registration is a critical step. Therefore, an evaluation of this registration can play an important role in the outcome of the therapy. In this work, we propose using both bootstrapping and supervised learning methods to evaluate the registration. We test our methods on 2D and 3D data acquired from phantom and patients. According to our results, both methods perform well while having advantages and disadvantages over one another. Supervised learning methods offer more accuracy and less computation time. On the other hand, for bootstrapping, no training data is required and also offers more sensitivity