Imaging of tophaceous gout: computed tomography provides specific images compared with magnetic resonance imaging and ultrasonography.

OBJECTIVE: To determine the usefulness of computed tomography (CT), magnetic resonance imaging (MRI), and Doppler ultrasonography (US) in providing specific images of gouty tophi. METHODS: Four male patients with chronic gout with tophi affecting the knee joints (three cases) or the olecranon processes of the elbows (one case) were assessed. Crystallographic analyses of the synovial fluid or tissue aspirates of the areas of interest were made with polarising light microscopy, alizarin red staining, and x ray diffraction. CT was performed with a GE scanner, MR imaging was obtained with a 1.5 T Magneton (Siemens), and ultrasonography with colour Doppler was carried out by standard technique. RESULTS: Crystallographic analyses showed monosodium urate (MSU) crystals in the specimens of the four patients; hydroxyapatite and calcium pyrophosphate dihydrate (CPPD) crystals were not found. A diffuse soft tissue thickening was seen on plain radiographs but no calcifications or ossifications of the tophi. CT disclosed lesions containing round and oval opacities, with a mean density of about 160 Hounsfield units (HU). With MRI, lesions were of low to intermediate signal intensity on T(1) and T(2) weighting. After contrast injection in two cases, enhancement of the tophus was seen in one. Colour Doppler US showed the tophi to be hypoechogenic with peripheral increase of the blood flow in three cases. CONCLUSION: The MR and colour Doppler US images showed the tophi as masses surrounded by a hypervascular area, which cannot be considered as specific for gout. But on CT images, masses of about 160 HU density were clearly seen, which correspond to MSU crystal deposits.

Snapshot gradient-recalled echo-planar images of rat brains at long echo time at 9.4 T.

With improved B 0 homogeneity along with satisfactory gradient performance at high magnetic fields, snapshot gradient-recalled echo-planar imaging (GRE-EPI) would perform at long echo times (TEs) on the order of T2*, which intrinsically allows obtaining strongly T2*-weighted images with embedded substantial anatomical details in ultrashort time. The aim of this study was to investigate the feasibility and quality of long TE snapshot GRE-EPI images of rat brain at 9.4 T. When compensating for B 0 inhomogeneities, especially second-order shim terms, a 200 x 200 microm2 in-plane resolution image was reproducibly obtained at long TE (>25 ms). The resulting coronal images at 30 ms had diminished geometric distortions and, thus, embedded substantial anatomical details. Concurrently with the very consistent stability, such GRE-EPI images should permit to resolve functional data not only with high specificity but also with substantial anatomical details, therefore allowing coregistration of the acquired functional data on the same image data set.

Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images.

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.

An Efficient Segmentation Method for Ultrasound Images based on a Semi-supervised Approach and Patch-based Features

Segmenting ultrasound images is a challenging problemwhere standard unsupervised segmentation methods such asthe well-known Chan-Vese method fail. We propose in thispaper an efficient segmentation method for this class ofimages. Our proposed algorithm is based on asemi-supervised approach (user labels) and the use ofimage patches as data features. We also consider thePearson distance between patches, which has been shown tobe robust w.r.t speckle noise present in ultrasoundimages. Our results on phantom and clinical data show avery high similarity agreement with the ground truthprovided by a medical expert.