Medical imaging sensors can be used to noninvasively probe tissue morphology and monitor material deformations associated with growth, disease, or normal physiology. Deformable image registration provides a framework for extracting and quantifying this information. Image registration is, however, an inherently ill-posed inverse problem. The research in this dissertation investigates techniques ...

This paper presents a novel variational model for inverse consistent deformable image registration. The proposed model deforms both source and target images simultaneously, and aligns the deformed images in the way that the forward and backward transformations are inverse consistent. To avoid the direct computation of the inverse transformation fields, our model estimates two more vector fields...

The purpose of this study was to test the accuracy of a commercially available deformable image registration tool in a clinical situation. In addition, to demonstrate a method to evaluate the resulting transformation of such a tool to a reference defined by multiple experts. For 16 patients (seven head and neck, four thoracic, five abdominal), 30-50 anatomical landmarks were defined on recogniz...

In this paper, we present and validate a framework, based on deformable image registration, for automatic processing of serial three-dimensional CT images used in image-guided radiation therapy. A major assumption in deformable image registration has been that, if two images are being registered, every point of one image corresponds appropriately to some point in the other. For intra-treatment ...

In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial transformer, which generates the displacement vector field that enables the resampler to warp th...

The registration of multimodal images remains an intricate issue, especially when the multimodal image pair shows non overlapping structures, missing data, noise or outliers. In this paper, we present a deformable model-based technique for the rigid registration of 2 0 and 3D multimodal images. The deformable model embeds a priori knowledge of the spatial correspondence and statistical variabil...

In recent years, a number of approaches have been applied to the problem of deformable registration validation. However, the challenge of assessing a commercial deformable registration system - in particular, an automatic registration system in which the deformable transformation is not readily accessible - has not been addressed. Using a collection of novel and established methods, we have dev...

Many existing image registration methods have difficulties in accurately describing significant rotation and bending of entities (e.g. organs) between two datasets. A common problem in this case is to ensure that the resulting registration is physically plausible, i.e. that the registration describes the actual bending/rotation occurring rather than just introducing expansion in some areas and ...

Purpose Deformable registration is now routinely used at many radiation oncology centers not only for automatic contouring, but also for diagnostic image registration and dose accumulation. In these applications, it is essential that these registrations are not only characterized with phantom studies, but also be checked on a per-patient basis around the regions where the deformable registratio...