Nonrigid Medical Image Registration using Adaptive Gradient Optimizer

Medical image registration has a significant role in several applications. It has sequential processes, including transformation, similarity metric calculation, diffusion regularization, and optimization of the transformation parameters (i.e., rotation, translation, and shear). The optimization process for determining the optimal set of the transformation vectors is considered the main stage affecting the performance of the registration process. Hence, medical image registration can be deliberated as an optimization problem for computing the geometric transformations to realize maximum similarity between the moving image and the fixed one. In this work, a mono-modal nonrigid image registration using B-spline is designed for the alignment of Computed Tomography (CT) images of thorax using Adaptive Gradient algorithm (AdaGrad) optimizer. In addition, a comparative study with other first order optimizers, such as Stochastic Gradient Descent (SGD), Adaptive Moment Estimation (Adam) algorithm (AdaMaX), AdamP, and RangerQH were conducted. Also, a comparison with the limited memory Broyden-Fletcher-Goldfarb-Shannon (LBFGS) as a second order optimizer was also carried out. The results showed the superiority of the AdaGrad optimizer by 56.99% and 48.37% improvement in the compared to the target registration error (TRE) compared to the SGD, and the LBFGS optimizer, respectively

Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni- / multi- modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.Comment: Add new discussion

Retrospective study on phantom for the application of medical image registration in the operating room scenario

This paper presents a phantom study to asses the feasibility of the medical image registration algorithms in the operating room (OR) scenario. The main issues of the registration algorithms in an OR application are, on one hand, the lack of the initial guess of the registration transformation - the images to be registered may be completely independentand, on the other hand, the multimodality of the data. Other requirements to be addressed by the OR registration algorithms are: real-time execution and the necessity of the validation of the results. This work analyzes how, under these requirements, the current state of the art algorithms in medical image registration may be used and shows which direction should be taken when designing a OR navigation system that includes registration as a component

Gradient Based Image Registration Using Importance Sampling

Analytical gradient based non-rigid image registration methods, using intensity based similarity measures (e.g. mutual information), have proven to be capable of accurately handling many types of deformations. While their versatility is largely in part to their high degrees of freedom, the computation of the gradient of the similarity measure with respect to the many warp parameters becomes very time consuming. Recently, a simple stochastic approximation method using a small random subset of image pixels to approximate this gradient has been shown to be effective. We propose to use importance sampling to improve the accuracy and reduce the variance of this approximation by preferentially selecting pixels near image edges. Initial empirical results show that a combination of stochastic approximation methods and importance sampling greatly improves the rate of convergence of the registration process while preserving accuracy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86019/1/Fessler217.pd

Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo

We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backpropagation and the variational inference by backpropagation frameworks, in order to efficiently draw samples from the posterior distribution of transformation parameters. To address the modelling issues, we formulate a Bayesian model for image registration that overcomes the existing barriers when using a dense, high-dimensional, and diffeomorphic transformation parametrisation. This results in improved calibration of uncertainty estimates. We compare the model in terms of both image registration accuracy and uncertainty quantification to VoxelMorph, a state-of-the-art image registration model based on deep learning

MRI Super-Resolution using Multi-Channel Total Variation

This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast. The model recasts the recovery of high resolution images as an inverse problem, in which a forward model simulates the slice-select profile of the MR scanner. The paper introduces a prior based on multi-channel total variation for MRI super-resolution. Bias-variance trade-off is handled by estimating hyper-parameters from the low resolution input scans. The model was validated on a large database of brain images. The validation showed that the model can improve brain segmentation, that it can recover anatomical information between images of different MR contrasts, and that it generalises well to the large variability present in MR images of different subjects. The implementation is freely available at https://github.com/brudfors/spm_superre