A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative pose until the mutual information between images is maximized. In our derivation of the registration procedure, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used with a wide variety of imaging devices. This approach works directly with raw images; no preprocessing or feature detection is required. As opposed to feature-based techniques, all of the information in the scan is used to evaluate the registration. This technique is however more flexible and robust than other intensity based techniques like correlation. Additionally, it has an efficient implementation that is based on stochastic approximation. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images with comput...

Abstract: We describe a comprehensive framework for performing rapid and automatic nonlabel-based nonlinear spatial normalizations. The approach adopted minimizes the residual squared difference between an image and a template of the same modality. In order to reduce the number of parameters to be fitted, the nonlinear warps are described by a linear combination of low spatial frequency basis functions. The objective is to determine the optimum coefficients for each of the bases by minimizing the sum of squared differences between the image and template, while simultaneously maximizing the smoothness of the transformation using a maximum a posteriori (MAP) approach. Most MAP approaches assume that the variance associated with each voxel is already known and that there is no covariance between neighboring voxels. The approach described here attempts to estimate this variance from the data, and also corrects for the correlations between neighboring voxels. This makes the same approach suitable for the spatial normalization of both high-quality magnetic resonance images, and low-resolution noisy positron emission tomography images. A fast algorithm has been developed that utilizes Taylor’s theorem and the separable nature of the basis functions, meaning that most of the nonlinear spatial variability between images can be automatically corrected within a few minutes. Hum. Brain Mapping 7:254–266, 1999.

This paper presents a new method for image registration based on jointly estimating the forward and reverse transformations between two images while constraining these transforms to be inverses of one another. This approach produces a consistent set of transformations that have less pairwise registration error, i.e., better correspondence, than traditional methods that estimate the forward and reverse transformations independently. The transformations are estimated iteratively and are restricted to preserve topology by constraining them to obey the laws of continuum mechanics. The transformations are parameterized by a Fourier series to diagonalize the covariance structure imposed by the continuum mechanics constraints and to provide a computationally efficient numerical implementation. Results using a linear elastic material constraint are presented using both magnetic resonance and X-ray computed tomography image data. The results show that the joint estimation of a consistent set of forward and reverse transformations constrained by linear-elasticity give better registration results than using either constraint alone or none at all.

Introduction The ever expanding gamut of medical imaging techniques provides the clinician an increasingly multifaceted view of brain function and anatomy. The information provided by the various imaging modalities is often complementary (i.e. provides separate but useful information) and synergistic (i.e. the combination of information provides useful extra information). For example, X-ray computed tomography (CT) and magnetic resonance (MR) imaging exquisitely demonstrate brain anatomy but provide little functional information. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) scans display aspects of brain function and allow metabolic measurements but poorly delineate anatomy. Furthermore, CT and MR images describe complementary morphologic features. For example, bone and calcifications are best seen on CT images, while soft-tissue structures are better differentiated by MR imaging. Clinical diagnosis and therapy planning and evaluatio

In this paper, the current status of cardiac image registration methods is reviewed. The combination of information from multiple cardiac image modalities, such as magnetic resonance imaging, computed tomography, positron emission tomography, single-photon emission computed tomography, and ultrasound, is of increasing interest in the medical community for physiologic understanding and diagnostic purposes. Registration of cardiac images is a more complex problem than brain image registration because the heart is a nonrigid moving organ inside a moving body. Moreover, as compared to the registration of brain images, the heart exhibits much fewer accurate anatomical landmarks. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time.

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...

Abstract. We developed an automated system that registers chest CT images temporally. Our registration method matches corresponding anatomical landmarks to obtain initial registration parameters. The initial point-to-point registration is then generalized to an iterative surface-tosurface registration method. Our “goodness-of-fit ” measure is evaluated at each step in the iterative scheme until the registration performance is sufficient. We applied our method to register the 3D lung surfaces of 10 pairs of chest CT scans and report a promising registration performance. 1 1

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Advanced imaging techniques have been widely used to study the anatomical structure and functional metabolism in medical and clinical applications. Images are acquired from a variety of scanners (CT/MR/PET/SPECT/Ultrasound), which provide physicians with complementary information to diagnose and detect specific regions of a patient. However, due to the different modalities and imaging orientations, these images rarely align spatially. They need to be registered for consistent and repeatable analyses. Therefore, image registration is a critical component of medical imaging applications. Since the brains of rodent animal mostly behave in the rigid manner, their alignments may be generally described by a rigid model without local deformation. Mutual information is an excellent strategy to measure the statistical dependence of image from mono-modality or multi-modalities. The registration system with rigid model was developed to combine with mutual information for functional magnetic resonance (fMRI) analysis, which has five components: (1) rigid body and affine transformation, (2) mutual information as the similarity measure, (3) partial volume interpolation, (4) multi-

We describe functional brain mapping experiments using a transcranial magnetic stimulation (TMS) device. This device, when placed on a subject’s scalp, stimulates the underlying neurons by generating focused magnetic field pulses. A brain mapping is then generated by measuring responses of different motor and sensory functions to this stimulation. The key process in generating this mapping is the association of the 3-D positions and orientations of the TMS probe on the scalp to a 3-D brain reconstruction such as is feasible with a magnetic resonance image (MRI). We have developed a registration system which not only generates functional brain maps using such a device, but also provides real-time feedback to guide the technician in placing the probe at appropriate points on the head to achieve the desired map resolution. Functional areas we have mapped are the motor and visual cortex. Validation experiments focus on repeatability tests for mapping the same subjects several times. Applications of the technique include neuroanatomy research, surgical planning and guidance, treatment and disease monitoring, and therapeutic procedures.