DRAMMS: deformable registration via attribute matching and mutual-saliency weighting

A general-purpose deformable registration algorithm referred to as ”DRAMMS” is presented in this paper. DRAMMS adds to the literature of registration methods that bridge between the traditional voxel-wise methods and landmark/feature-based methods. In particular, DRAMMS extracts Gabor attributes at each voxel and selects the optimal components, so that they form a highly distinctive morphological signature reflecting the anatomical context around each voxel in a multi-scale and multi-resolution fashion. Compared with intensity or mutual-information based methods, the high-dimensional optimal Gabor attributes render different anatomical regions relatively distinctively identifiable and therefore help establish more accurate and reliable correspondence. Moreover, the optimal Gabor attribute vector is constructed in a way that generalizes well, i.e., it can be applied to different registration tasks, regardless of the image contents under registration. A second characteristic of DRAMMS is that it is based on a cost function that weights different voxel pairs according to a metric referred to as ”mutual-saliency”, which reflects the uniqueness (reliability) of anatomical correspondences implied by the tentative transformation. As a result, image voxels do not contribute equally to the optimization process, as in most voxel-wise methods, or in a binary selection fashion, as in most landmark/feature-based methods. Instead, they contribute according to a continuously-valued mutual-saliency map, which is dynamically updated during the algorithm’s evolution. The general applicability and accuracy of DRAMMS are demonstrated by experiments in simulated images, inter-subject images, single-/multi-modality images, and longitudinal images, from human and mouse brains, breast, heart, and prostate

Utilizing Segmented MRI Data in Image-Guided Surgery

While the role and utility of Magnetic Resonance Images as a diagnostic tool is well established in current clinical practice, there are a number of emerging medical arenas in which MRI can play an equally important role. In this article, we consider the problem of image-guided surgery, and provide an overview of a series of techniques that we have recently developed in order to automatically utilize MRI-based anatomical reconstructions for surgical guidance and navigation. 1 Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge MA 2 Department of Radiology, Brigham and Womens Hospital, Harvard Medical School, Boston MA 1 Introduction In recent years, Magnetic Resonance Imaging (MRI) has become a commonplace medical diagnostic tool [2], especially for cases involving soft tissue, such as in the brain. Two factors combine to make MRI a very valuable clinical tool: fine scale spatial resolution allows for the detection and delineation of detailed stru..

Analysis of Functional MRI Data Using Mutual Information

. A new information-theoretic approach is presented for analyzing fMRI data to calculate the brain activation map. The method is based on a formulation of the mutual information between two waveforms-- the fMRI temporal response of a voxel and the experimental protocol timeline. Scores based on mutual information are generated for all voxels and then used to compute the activation map of an experiment. Mutual information for fMRI analysis is employed because it has been shown to be robust in quantifying the relationship between any two waveforms. More importantly, our technique takes a principled approach toward calculating the brain activation map by making few assumptions about the relationship between the protocol timeline and the temporal response of a voxel. This is important especially in fMRI experiments where little is known about the relationship between these two waveforms. Experiments are presented to demonstrate this approach of computing the brain activation ..