Retrospective registration of tomographic brain images

In modern clinical practice, the clinician can make use of a vast array of specialized imaging techniques supporting diagnosis and treatment. For various reasons, the same anatomy of one patient is sometimes imaged more than once, either using the same imaging apparatus (monomodal acquisition ), or different ones (multimodal acquisition). To make simultaneous use of the acquired images, it is often necessary to bring these images in registration, i.e., to align their anatomical coordinate systems. The problem of medical image registration as concerns human brain images is addressed in this thesis. The specific chapters include a survey of recent literature, CT/MR registration using mathematical image features (edges and ridges), monomodal SPECT registration, and CT/MR/SPECT/PET registration using image features extracted by the use of mathematical morphology

Automatic 4-D Registration in Dynamic MR Renography Based on Over-complete Dyadic Wavelet and Fourier Transforms

Dynamic contrast-enhanced 4-D MR renography has the potential for broad clinical applications, but suffers from respiratory motion that limits analysis and interpretation. Since each examination yields at least over 10-20 serial 3-D images of the abdomen, manual registration is prohibitively labor-intensive. Besides in-plane motion and translation, out-of-plane motion and rotation are observed in the image series. In this paper, a novel robust and automated technique for removing out-of-plane translation and rotation with sub-voxel accuracy in 4-D dynamic MR images is presented. The method was evaluated on simulated motion data derived directly from a clinical patient's data. The method was also tested on 24 clinical patient kidney data sets. Registration results were compared with a mutual information method, in which differences between manually co-registered time-intensity curves and tested time-intensity curves were compared. Evaluation results showed that our method agreed well with these ground truth data

Accelerating Feature Based Registration Using the Johnson-Lindenstrauss Lemma

Abstract. We introduce an efficient search strategy to substantially accelerate feature based registration. Previous feature based registration algorithms often use truncated search strategies in order to achieve small computation times. Our new accelerated search strategy is based on the realization that the search for corresponding features can be dramat-ically accelerated by utilizing Johnson-Lindenstrauss dimension reduc-tion. Order of magnitude calculations for the search strategy we propose here indicate that the algorithm proposed is more than a million times faster than previously utilized naive search strategies, and this advan-tage in speed is directly translated into an advantage in accuracy as the fast speed enables more comparisons to be made in the same amount of time. We describe the accelerated scheme together with a full complex-ity analysis. The registration algorithm was applied to large transmission electron microscopy (TEM) images of neural ultrastructure. Our experi-ments demonstrate that our algorithm enables alignment of TEM images with increased accuracy and efficiency compared to previous algorithms.

Polyrigid and Polyaffine Transformations: A New Class of Diffeomorphisms for Locally Rigid or Affine Registration

MICCAI 2003 Best Student Award in Image Processing and Visualization.International audienceOBJECTIVE: The goal of this work is to improve the usability of a non-rigid registration software for medical images. METHOD: We have built a registration grid service in order to use the interactivity of a visualization workstation and the computing power of a cluster. On the user side, the system is composed of a graphical interface that interacts in a complex and fluid manner with the registration software running on a remote cluster. CONCLUSION: Although the transmission of images back and forth between the computer running the user interface and the cluster running the registration service adds to the total registration time, it provides a user-friendly way of using the registration software without heavy infrastructure investments in hospitals. The system exhibits good performances even if the user is connected to the grid service through a low throughput network such as a wireless network interface or ADSL

Normalized mutual information based PET-MR registration using K-Means clustering and shading correction

A method for the efficient re-binning and shading based correction of intensity distributions of the images prior to normalized mutual information based registration is presented. Our intensity distribution re-binning method is based on the K-means clustering algorithm as opposed to the generally used equidistant binning method. K-means clustering is a binning method with a variable size for each bin which is adjusted to achieve a natural clustering. Furthermore, a shading correction method is applied to reduce the effect of intensity inhomogeneities in MR images. Registering clinical shading corrected MR images to PET images using our method shows that a significant reduction in computational time without loss of accuracy as compared to the standard equidistant binning based registration is possible. © Springer-Verlag Berlin Heidelberg 2003

Interpolation artefacts in mutual information-based image registration

Image registration requires the transformation of one image to another so as to spatially align the two images. This involves interpolation to estimate gray values of one of the images at positions other than the grid points. When registering two images that have equal grid distances in one or more dimensions, the grid points can be aligned in those dimensions for certain geometric transformations. Consequently, the number of times interpolation is required to compute the registration measure of two images is dependent on the image transformation. When an entropy-based registration measure, such as mutual information, is plotted as a function of the transformation, it will show sudden changes in value for grid-aligning transformations. Such patterns of local extrema impede the registration optimization process. More importantly, they rule out subvoxel accuracy. In this paper, two frequently applied interpolation methods in mutual information-based image registration are analyzed, viz. linear interpolation and partial volume interpolation. It is shown how the registration function depends on the interpolation method and how a slight resampling of one of the images may drastically improve the smoothness of this function

<title><emph type="1">f</emph>-information measures in medical image registration</title>

A much-used measure for registration of three-dimensional medical images is mutual information, which originates from information theory. However, information theory offers many more measures that may be suitable for image registration. Such measures denote the divergence of the joint grey value distribution of two images from the joint distribution for complete independence of the images. This paper compares the performance of mutual information as a registration measure with that of other information measures. The measures are applied to rigid registration of clinical PET/MR and MR/CT images, for 35 and 41 image pairs respectively. An accurate gold standard transformation is available for the images, based on implanted markers. Both registration performance and accuracy of the measures are studied. The results indicate that some information measures perform very poorly for the chosen registration problems, yielding many misregistrations, even when using a good starting estimate. Other measures, however , were shown to produce significantly more accurate results than mutual information