Learning-based Deformation Estimation for Fast Non-rigid Registration

Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients

This paper describes a framework for learning a statistical model of non-rigid deformations induced by interventional procedures. We make use of this learned model to perform constrained non-rigid registration of pre-procedural and post-procedural imaging. We demonstrate results applying this framework to non-rigidly register post-surgical computed tomography (CT) brain images to pre-surgical magnetic resonance images (MRIs) of epilepsy patients who had intra-cranial electroencephalography electrodes surgically implanted. Deformations caused by this surgical procedure, imaging artifacts caused by the electrodes, and the use of multi-modal imaging data make non-rigid registration challenging. Our results show that the use of our proposed framework to constrain the non-rigid registration process results in significantly improved and more robust registration performance compared to using standard rigid and non-rigid registration methods.

Voxel-Wise Displacement as Independent Features in Classification of Multiple Sclerosis

We present a method that utilizes registration displacement fields to perform accurate classification of magnetic resonance images (MRI) of the brain acquired from healthy individuals and patients diagnosed with multiple sclerosis (MS). Contrary to standard approaches, each voxel in the displacement field is treated as an independent feature that is classified individually. Results show that when used with a simple linear discriminant and majority voting, the approach is superior to using the displacement field with a single classifier, even when compared against more sophisticated classification methods such as adaptive boosting, random forests, and support vector machines. Leave-one-out cross-validation was used to evaluate this method for classifying images by disease, MS subtype (Acc: 77%-88%), and age (Acc: 96%-100%).

Learning nonrigid deformations for constrained multi-modal image registration

We present a new strategy to constrain nonrigid registrations of multi-modal images using a low-dimensional statistical deformation model and test this in registering pre-operative and post-operative images from epilepsy patients. For those patients who may undergo surgical resection for treatment, the current gold-standard to identify regions of seizure involves craniotomy and implantation of intracranial electrodes. To guide surgical resection, surgeons utilize pre-op anatomical and functional MR images in conjunction with post-electrode implantation MR and CT images. The electrode positions from the CT image need to be registered to pre-op functional and structural MR images. The post-op MRI serves as an intermediate registration step between the pre-op MR and CT images. In this work, we propose to bypass the post-op MR image registration step and directly register the pre-op MR and post-op CT images using a low-dimensional nonrigid registration that captures the gross deformation after electrode implantation. We learn the nonrigid deformation characteristics from a principal component analysis of a set of training deformations and demonstrate results using clinical data. We show that our technique significantly outperforms both standard rigid and nonrigid intensity-based registration methods in terms of mean and maximum registration error.

FAST NONRIGID IMAGE REGISTRATION USING STATISTICAL DEFORMATION MODELS LEARNED FROM RICHLY-ANNOTATED DATA

Nonrigid image registrations require a large number of degrees of freedom (DoFs) to capture intersubject anatomical variations. With such high DoFs and lack of anatomical correspondences, algorithms may not converge to the globally optimal solution. In this work, we propose a fast, two-step nonrigid registration procedure with low DoFs to accurately register brain images. Our method makes use of a statistical deformation model based upon a principal component analysis of deformations learned from a manually-segmented dataset to perform an initial registration. We then follow with a low DoF nonrigid transformation to complete the registration. Our results show the same registration accuracy in terms of volume of interest overlap as high DoF transformations, but with a 96% reduction in DoF and 98% decrease in computation time.

Intermediate templates guided groupwise registration of diffusion tensor images

Registration of a population of diffusion tensor images (DTIs) is one of the key steps in medical image analysis, and it plays an important role in the statistical analysis of white matter related neurological diseases. However, pairwise registration with respect to a pre-selected template may not give precise results if the selected template deviates significantly from the distribution of images. To cater for more accurate and consistent registration, a novel framework is proposed for groupwise registration with the guidance from one or more intermediate templates determined from the population of images. Specifically, we first use a Euclidean distance, defined as a combinative measure based on the FA map and ADC map, for gauging the similarity of each pair of DTIs. A fully connected graph is then built with each node denoting an image and each edge denoting the distance between a pair of images. The root template image is determined automatically as the image with the overall shortest path length to all other images on the minimum spanning tree (MST) of the graph. Finally, a sequence of registration steps is applied to progressively warping each image towards the root template image with the help of intermediate templates distributed along its path to the root node on the MST. Extensive experimental results using diffusion tensor images of real subjects indicate that registration accuracy and fiber tract alignment are significantly improved, compared with the direct registration from each image to the root template image.