PLoS One

Quantitative analysis of the vascular network anatomy is critical for the understanding of the vasculature structure and function. In this study, we have combined microcomputed tomography (microCT) and computational analysis to provide quantitative three-dimensional geometrical and topological characterization of the normal kidney vasculature, and to investigate how 2 core genes of the Wnt/planar cell polarity, Frizzled4 and Frizzled6, affect vascular network morphogenesis. Experiments were performed on frizzled4 (Fzd4-/-) and frizzled6 (Fzd6-/-) deleted mice and littermate controls (WT) perfused with a contrast medium after euthanasia and exsanguination. The kidneys were scanned with a high-resolution (16 μm) microCT imaging system, followed by 3D reconstruction of the arterial vasculature. Computational treatment includes decomposition of 3D networks based on Diameter-Defined Strahler Order (DDSO). We have calculated quantitative (i) Global scale parameters, such as the volume of the vasculature and its fractal dimension (ii) Structural parameters depending on the DDSO hierarchical levels such as hierarchical ordering, diameter, length and branching angles of the vessel segments, and (iii) Functional parameters such as estimated resistance to blood flow alongside the vascular tree and average density of terminal arterioles. In normal kidneys, fractal dimension was 2.07±0.11 (n = 7), and was significantly lower in Fzd4-/- (1.71±0.04; n = 4), and Fzd6-/- (1.54±0.09; n = 3) kidneys. The DDSO number was 5 in WT and Fzd4-/-, and only 4 in Fzd6-/-. Scaling characteristics such as diameter and length of vessel segments were altered in mutants, whereas bifurcation angles were not different from WT. Fzd4 and Fzd6 deletion increased vessel resistance, calculated using the Hagen-Poiseuille equation, for each DDSO, and decreased the density and the homogeneity of the distal vessel segments. Our results show that our methodology is suitable for 3D quantitative characterization of vascular networks, and that Fzd4 and Fzd6 genes have a deep patterning effect on arterial vessel morphogenesis that may determine its functional efficiency

Matching Of 3D Medical Images With A Potential Based Method

Three dimensional (3D) images take an increasing importance in the medical imaging field. They may be produced by X-ray Computed Tomography (CT), Magnetic Resonance Imaging #MRI# or Positon Emitting Tomography #PET# for example. They usually contain complementary informations which have to be combined together in order to be useful to physicians. Combining the information of several 3D images sets the problem of the registration of these images. It arises when two images of the same modality are taken with different positions or taken at different times #before and after a surgery for example# or with two images of different modalities. We present a new method for computing the rigid transformation between two 3D objects. Our method looks like a gradient method that minimizes the distance between both objects, but is more powerful because it is in fact an application of the fundamental laws of dynamics which uses both #rst and second temporal derivatives of positions and allows an intuitive control of the minimization process. We show and discuss synthetic examples as well as real examples involving the registration of CT or MRI data of the head and the registration of PET and MRI images of the brain

Fusion of autoradiographies with an MR volume using 2-D and 3-D linear transformations

In the past years, the development of 3-D medical imaging devices has given access to the 3-D imaging of in vivo tissues, from an anatomical (MR, CT) or even functional point of view (fMRI, PET, SPECT). However, despite huge technological progress, the resolution of these images is still not suffiient to image to anatomical or functional details, that can only be revealed by in vitro imaging (e.g. histology, autoradiography), eventually enhanced by staining. The deep motivation of this work is the comparison of activations detected..

Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration

While intensity-based similarity measures are increasingly used for medical image registration, they often rely on implicit assumptions regarding the physics of imaging. The motivation of this paper is to determine what are the assumptions corresponding to a number of popular similarity measures in order to better understand their use, and finally help choosing the one which is the most appropriate for a given class of problems. After formalizing registration based on general image acquisition models, we show that the search for an optimal measure can be cast into a maximum likelihood estimation problem. We then derive similarity measures corresponding to di#erent modeling assumptions and retrieve some well-known measures (correlation coe#cient, correlation ratio, mutual information). Finally, we present results of registration between 3D MR and 3D Ultrasound images to illustrate the importance of choosing an appropriate similarity measure

Multimodal Image Registration by Maximization of the Correlation Ratio

Over the last five years, new "voxel-based" approaches have allowed important leaps in multimodal image registration, notably due to the increasing use of information-theoretic similarity measures. Their wide success has led to the progressive abandon of measures using standard image statistics (mean and variance). Until now, such measures have essentially been based on heuristics. In this paper, we address the determination of a new measure based on standard statistics from a theoretical point of view. We show that it naturally leads to a known concept of probability theory, the correlation ratio. In our derivation, we take as the hypothesis the functional dependence between the image intensities. This means that one image is considered as a model of the other. Although suchahypothesis is not validate in every circumstance, it enables us to incorporate implicitely an a priori smoothness model. We also demonstrate preliminary results of multimodal rigid registration involving Magnetic Resonance (MR), Computed Tomography (CT), and Positron Emission Tomography (PET) images. These results suggest that the correlation ratio provides a good trade-off between accuracy and robustness

Piecewise Affine Registration of Biological Images

This manuscript tackles the registration of 2D biological images (histological sections or autoradiographs) to 2D images from the same or di#erent modalities (e.g., histology or MRI). The process of acquiring these images typically induces composite transformations that can be modeled as a number of rigid or a#ne local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. A hierarchical clustering algorithm then automatically partitions this field into a number of classes from which we extract independent pairs of sub-images. Finally, the pairs of sub-images are, independently, a#nely registered and a hybrid a#ne/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach under a variety of conditions, and discuss examples using real biomedical images, including MRI, histology and cryosection data

Robust Plant Cell Tracking in Fluorescence Microscopy 3D+T Series

International audienceAutomatic tracking of cell deformation during development using time-lapse confocal microscopy is a challenging task. In plant cell tissues, large deformations and several division cycles can occur between two consecutive time-points making the image registration and tracking procedure particularly difficult. Here, we propose an iterative approach where an initial registration transformation and cell-to-cell mapping are alternatively refined using high-confidence associations selected on the basis of a geometric context preservation score. The method, evaluated on a long time-lapse series of floral meristem, clearly demonstrates its superiority over a non-iterative approach. In addition, we show that the geometric context preservation score can be used to define a lineage quality assessment metric that makes it possible for an expert to provide locally nudges to finalize the lineage detection if necessary in a semi-automatic way

Using SPM to Detect Evolving MS Lesions

Clinicians need to study the effects of new treatments: it is sometimes possible to detect and quantify those effects by looking at evolutions in the medical images of a patient over time especially in the case of multiple sclerosis (MS) where lesions are related to clinical signs [1]. Some methods allow to compare two images to know where there are differences, typically between the last and the previous exam [2, 3]. However a retrospective analysis might be done on the whole set of images to find the moments when evolutions occur [4]. We propose to use the analogy between an activation in functional imaging (for instance PET, SPECT and fMRI) [5] and a signal change due to an evolving multiple sclerosis lesion. Voxels corresponding to evolving pathological areas are named ELV (Evolving Lesion Voxels) in this abstract

Automated Piecewise Affine Registration of 2-D Images via Hierarchical Clustering of Dense Similarity Maps

We propose a fully automated method for locally registering 2-D multi-modal images. We model the images as a number of anatomically separate components, subject to aOEne transformations, and embedded in a deformable medium

Filtrage, topologie et mise en correspondance d'images medicales multidimensionnelles

Available from INIST (FR), Document Supply Service, under shelf-number : T 83751 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc