Search

Many quantitative analysis methods for myocardial perfusion studies require as a central step a comparison with a 'normal' or average density distribution map or reference image. It has been recognized, however, that the normal distribution can be affected by patient attributes, including sex and weight or body habitus, and by acquisition attributes, including the choice of tracer and the position of the patient during imaging. Some authors have proposed separate reference images for the sexes and the tracer. This approach fails if a large number of binary attributes have to be considered, since one would need 2" reference images for each attribute. The problem is compounded when continuous attributes (e.g. age and weight) are included, especially if the approach is to average separate homogeneous groups for each attribute. We propose to create case-specific reference images for the interpretation of myocardial perfusion studies by creating a model based on the influence of each attribute. From a non-homogeneous population of normal cases, or cases presumed to be normal on the basis of the Diamond and Forrester stratification, the effect of patient and study attributes on the density distribution in the stress image and the density differences between rest and stress images were computed. The effects are computed by multi-linear regression, to account for cross-correlation. Significance is assigned on the basis of a partial Fisher test. The data are myocardial perfusion images matched in 3D to a template by an elastic transformation. Even though there was some cross-correlation in the data, we were able to show independent effects of sex, position (prone or supine), age, weight, tracer combination and stress method (exercise, persantine and adenosine). Taken as a whole, the multi-linear regression demonstrated a significant effect in 72% of the pixels within the myocardial volume. In addition, the distribution predicted by the model was equivalent to average images from homogeneous matched groups. In conclusion, our approach makes it possible to produce case-specific reference images without the need for multiple homogeneous large groups to produce averages for each possible patient or study attribute.

International audience; Abstract--Evaluating precisely the temporal variations of lesion volumes is very important for at least three types of practical applications: pharmaceutical trials, decision making for drug treatment or surgery, and patient follow-up. In this paper we present a volumetric analysis technique, combining precise rigid registration of three-dimensional (3-D) (volumetric) medical images, nonrigid deformation computation, and flow-field analysis. Our analysis technique has two outcomes: the detection of evolving lesions and the quantitative measurement of volume variations. The originality of our approach is that no precise segmentation of the lesion is needed but the approximative designation of a region of interest (ROI) which can be automated. We distinguish between tissue transformation (image intensity changes without deformation) and expansion or contraction effects reflecting a change of mass within the tissue. A real lesion is generally the combination of both effects. The method is tested with synthesized volumetric image sequences and applied, in a first attempt to quantify in vivo a mass effect, to the analysis of a real patient case with multiple sclerosis (MS).

RADIOTHERAPY OF ADULT NODAL NON HODGKIN'S LYMPHOMA: The role of radiotherapy in the treatment of nodal non-Hodgkin's lymphoma has been modified by the introduction of efficient chemotherapy and the development of different pathological classifications. INTERMEDIATE GRADE OR HIGH GRADE LYMPHOMA: The recommended treatment of early-stage aggressive lymphomas is primarily a combination chemotherapy. The interest of adjuvant radiotherapy remains unclear and has to be established through large prospective trials. If radiation therapy has to be delivered, the historical results of exclusive radiation therapy showed that involved-fields and a dose of 35-40 Gy (daily fraction of 1.8 Gy, 5 days a week) are the optimal schedule. The interest of radiotherapy in the treatment of advanced-stage aggressive lymphoma is yet to be proven. Further studies had to stratify localized stages according to the factors of the International Prognostic Index. LOW-GRADE LYMPHOMA: For early-stage low-grade lymphoma, radiotherapy remains the standard treatment. However, the appropriate technique to use is controversial. Involved-field irradiation at a dose of 35 Gy seems to be the optimal schedule, providing a 10-year disease-free survival rate of 50% and no major toxicity. There is no standard indication of radiotherapy in the treatment advanced-stage low-grade lymphoma. RARE AND NEW ENTITY: For "new" nodal lymphoma's types, the indication of radiotherapy cannot be established (mantle-zone lymphoma, marginal zone B-cell lymphoma) or must take into account the natural history (Burkitt's lymphoma, peripheral T-cell lymphoma) and the sensibility to others therapeutic methods.

This paper describes parts of a patent taken out by the INRIA; This paper describes a totally new way to process Xray data in order to reconstruct the external and internal boudaries of objects, which do not involve Computed Tomography (CT). We show that the segmentation can be performed directrly with the raw data, the sinogram produced with the scanner, and that those segmented shapes can be geomtetrically transformed into reconstructed shapes in the usual space. Thus, if we are interested in only the boundaries of the objects, our method eliminates the computationally expensive step of Computed Tomography. Experimental results are presented for both synthetic and real data, leading to subpixel positioning fo the reconstructed boundaries. Our method gives its best results for sparse, high contrasted objects such as bones or blood vessels in angiograms. It can be adapted to any kind of scanner, including 3D scabbers. At last, we present an extension of our method which allows "on the fly" processing of the data and real time tracking of the objects boudnaries.

The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate the authors' approach, the entire brain, the cerebellum, and the head of the caudate have been segmented manually by two raters on one of the volumes (the reference volume) and mapped back onto all the other volumes, using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain. Manual delineation was performed twice by the same two raters to test inter- and intrarater variability. For the brain and the cerebellum, results indicate that for each rater, contours obtained manually and contours obtained automatically by deforming his own atlas are virtually indistinguishable. Furthermore, contours obtained manually by one rater and contours obtained automatically by deforming this rater's own atlas are more similar than contours obtained manually by two raters. For the caudate, manual intra- and interrater similarity indexes remain slightly better than manual versus automatic indexes, mainly because of the spatial resolution of the images used in this study. Qualitative results also suggest that this method can be used for the segmentation of more complex structures, such as the hippocampus.

Evaluating precisely the temporal variations of lesion volumes is very important for at least three types of practical applications: pharmaceutical trials, decision making for drug treatment or surgery and patient follow-up. The authors present a volumetric analysis technique, combining precise rigid registration of 3D medical images, nonrigid deformation computation and flowfield analysis. Their analysis technique has two outcomes: the detection of evolving lesions and the quantitative measurement of volume variations. The originality of their approach is that no precise segmentation of the lesion is needed but the approximative designation of a region of interest, which can be automatized. They distinguish between tissue transformation (image intensity changes without deformation) and expansion or contraction effects reflecting a change of mass within the tissue; a real lesion being generally the combination of both effects. The method is tested with synthesized 3D image sequences and applied, in a first attempt to quantify in vivo a mass effect, to the analysis of a patient with multiple sclerosis (MS).

We present the concept of non-rigid matching based on demons, by reference to Maxwell's demons. We contrast this concept with the more conventional viewpoint of attraction. We show that demons and attractive points are clearly distinct for large deformations, but also that they become similar for small displacements, encompassing techniques close to optical flow. We describe a general iterative matching method based on demons, and derive from it three different non-rigid matching algorithms, one using all the image intensities, one using only contours, and one for already segmented images. At last, we present results with synthesized and real deformations, with applications to Computer Vision and Medical Image Processing.

Presents a method, based on the filtered backprojection technique (FBP), to extract directly the boundaries of X-ray images, without previous image reconstruction. The author preprocess the raw data in order to compute directly the reconstructed values of the gradient or of the Laplacian at any location in the plane (defined with real coordinates). The reconstructed value of the gradient and of the Laplacian correspond to the exact mathematical definition of the differentials of the image. For noisy data, the author proposes also to use an extension of existing FBP techniques, adapted to the computation of the gradient and of the Laplacian. Finally, the author shows how to use the corresponding operators to perform the segmentation of a slice, without image reconstruction. Images of the reconstructed gradient, Laplacian, and segmented objects are presented. >

This paper presents a new technique to perform automatically the 3D registration of two 3D images1, The aim is to compute the rigid geometric transform that exists between two data acquisitions of the same patient, taken into two different positions. Our method is based on crest lines and is fully automatic. The computing time remains low because crest lines are a very compact representation of the geometric information of the 3D image.

Geometric tomography (GT), a technique for processing tomographic projections in order to reconstruct the external and internal boundaries of objects, is presented. GT does not necessitate the reconstruction of an image of the slice of the object. It is shown that the segmentation can be performed directly with the raw data, the sinogram produced with the scanner, and that those segmented shapes can be geometrically transformed into reconstructed shapes in the usual space. If one is interested in only the boundaries of the objects, they do not need to reconstruct an image, and therefore the method needs much less computation than those using traditional computed tomography techniques. Experimental results are presented for both synthesized and real data, leading to subpixel positioning of the reconstructed boundaries. GT gives its best results for sparse, highly contrasted objects such as bones or blood vessels in angiograms, it allows 'on the fly' processing of the data, and real time tracking of the object boundaries. >