169,669 research outputs found

    3D statistical facial reconstruction

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    The aim of craniofacial reconstruction is to produce a likeness of a face from the skull. Few works in computerized assisted facial reconstruction have been done in the past, due to poor machine performances and data availability, and major works are manually reconstructions. In this paper, we present an approach to build 3D statistical models of the skull and the face with soft tissues from the skull of one individual. Results on real data are presented and seem promising

    Statistical skull models from 3D X-ray images

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    We present 2 statistical models of the skull and mandible built upon an elastic registration method of 3D meshes. The aim of this work is to relate degrees of freedom of skull anatomy, as static relations are of main interest for anthropology and legal medicine. Statistical models can effectively provide reconstructions together with statistical precision. In our applications, patient-specific meshes of the skull and the mandible are high-density meshes, extracted from 3D CT scans. All our patient-specific meshes are registrated in a subject-shared reference system using our 3D-to-3D elastic matching algorithm. Registration is based upon the minimization of a distance between the high density mesh and a shared low density mesh, defined on the vertexes, in a multi resolution approach. A Principal Component analysis is performed on the normalised registrated data to build a statistical linear model of the skull and mandible shape variation. The accuracy of the reconstruction is under the millimetre in the shape space (after rigid registration). Reconstruction errors for Scan data of tests individuals are below registration noise. To take in count the articulated aspect of the skull in our model, Kernel Principal Component Analysis is applied, extracting a non-linear parameter associated with mandible position, therefore building a statistical articulated 3D model of the skull.Comment: Proceedings of the Second International Conference on Reconstruction of Soft Facial Parts RSFP'200

    A Bayesian Approach to Manifold Topology Reconstruction

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    In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated

    Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

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    Parallel MRI is a fast imaging technique that enables the acquisition of highly resolved images in space or/and in time. The performance of parallel imaging strongly depends on the reconstruction algorithm, which can proceed either in the original k-space (GRAPPA, SMASH) or in the image domain (SENSE-like methods). To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated. In this paper, we extend this approach using 3D-wavelet representations in order to handle all slices together and address reconstruction artifacts which propagate across adjacent slices. The gain induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE: 3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal acquisition is considered. Another important extension accounts for temporal correlations that exist between successive scans in functional MRI (fMRI). In addition to the case of 2D+t acquisition schemes addressed by some other methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and 4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that all regularization parameters are estimated in the maximum likelihood sense on a reference scan. The gain induced by such extensions is illustrated on both anatomical and functional image reconstruction, and also measured in terms of statistical sensitivity for the 4D-UWR-SENSE approach during a fast event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE reconstruction at the subject and group levels (15 subjects) for different contrasts of interest (eg, motor or computation tasks) and using different parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353

    Quasi pseudo-inverse reconstruction for rotating PET scanners

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    Proceeding of: 2005 IEEE Nuclear Science Symposium Conference Record, Puerto Rico, 23-29 Oct. 2005A new reconstruction strategy is proposed for 3D images acquired with rotating PET scanners. This method is based on the decomposition of the System Response Matrix (SRM) into a projection and a rotation components that are inverted independently. We present the rationale of the new reconstruction procedure as well as some examples to compare with analytical 3D reconstruction (3DRP) and 3D statistical reconstruction (3D-OSEM)Part of this work is founded by the IM3 network (G03/185 Ministerio de Sanidad), with grants from the Ministerio de EducaciĂłn y Ciencia, project TEC2004 07052 C02 01, and Ministerio de Industria, Turismo y Comercio project FIT 330101 2004

    Stochastic reconstruction of snow microstructure from x-ray microtomography images

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    Thesis (M.S.) University of Alaska Fairbanks, 2007The three-dimensional (3D) high-resolution digitized snow microstructure (pixel size 6 micron) was obtained by X-ray microtomography. The experimental result was verified by measuring the density of the snow sample. Statistical characteristics (porosity, local porosity, two-point correlation function) were extracted from cross-sectional images. The one-level-cut Gaussian random field model was used to stochastically reconstruct snow microstructure from X-ray microtomography images. Efficient computer programs were developed in MATLAB for the whole stochastic reconstruction procedure, including the numerical inversion of the correlation function and the generation of 3D large-scale Gaussian random fields by 3D inverse fast Fourier transform. The quality of the reconstruction was assessed by comparing the two-point correlation function and cross-sectional images.1. Introduction -- 2. Snow images by X-ray microtomography -- 2.1. Introduction of computed tomography -- 2.2. Acquisition and reconstruction -- 2.3. Binary images and 3D visualization -- 2.4. Statistical characteristics of the snow sample -- 3. Stochastic reconstruction of porous materials -- 3.1. Weak sense stationary gaussian random fields -- 3.2. The power spectral density function -- 3.3. The one-level-cut gaussian random field model -- 3.4. Generation of gaussian random fields -- 3.5. Stochastic reconstruction procedure -- 4. Reconstruction results -- 5. Conclusions -- References -- Appendices

    3D reconstruction of ribcage geometry from biplanar radiographs using a statistical parametric model approach

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    Rib cage 3D reconstruction is an important prerequisite for thoracic spine modelling, particularly for studies of the deformed thorax in adolescent idiopathic scoliosis. This study proposes a new method for rib cage 3D reconstruction from biplanar radiographs, using a statistical parametric model approach. Simplified parametric models were defined at the hierarchical levels of rib cage surface, rib midline and rib surface, and applied on a database of 86 trunks. The resulting parameter database served to statistical models learning which were used to quickly provide a first estimate of the reconstruction from identifications on both radiographs. This solution was then refined by manual adjustments in order to improve the matching between model and image. Accuracy was assessed by comparison with 29 rib cages from CT scans in terms of geometrical parameter differences and in terms of line-to-line error distance between the rib midlines. Intra and inter-observer reproducibility were determined regarding 20 scoliotic patients. The first estimate (mean reconstruction time of 2’30) was sufficient to extract the main rib cage global parameters with a 95% confidence interval lower than 7%, 8%, 2% and 4° for rib cage volume, antero-posterior and lateral maximal diameters and maximal rib hump, respectively. The mean error distance was 5.4 mm (max 35mm) down to 3.6 mm (max 24 mm) after the manual adjustment step (+3’30). The proposed method will improve developments of rib cage finite element modeling and evaluation of clinical outcomes.This work was funded by Paris Tech BiomecAM chair on subject specific muscular skeletal modeling, and we express our acknowledgments to the chair founders: Cotrel foundation, SociĂ©tĂ© gĂ©nĂ©rale, ProtĂ©or Company and COVEA consortium. We extend your acknowledgements to Alina Badina for medical imaging data, Alexandre JournĂ© for his advices, and Thomas Joubert for his technical support

    A Novel Self-Intersection Penalty Term for Statistical Body Shape Models and Its Applications in 3D Pose Estimation

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    Statistical body shape models are widely used in 3D pose estimation due to their low-dimensional parameters representation. However, it is difficult to avoid self-intersection between body parts accurately. Motivated by this fact, we proposed a novel self-intersection penalty term for statistical body shape models applied in 3D pose estimation. To avoid the trouble of computing self-intersection for complex surfaces like the body meshes, the gradient of our proposed self-intersection penalty term is manually derived from the perspective of geometry. First, the self-intersection penalty term is defined as the volume of the self-intersection region. To calculate the partial derivatives with respect to the coordinates of the vertices, we employed detection rays to divide vertices of statistical body shape models into different groups depending on whether the vertex is in the region of self-intersection. Second, the partial derivatives could be easily derived by the normal vectors of neighboring triangles of the vertices. Finally, this penalty term could be applied in gradient-based optimization algorithms to remove the self-intersection of triangular meshes without using any approximation. Qualitative and quantitative evaluations were conducted to demonstrate the effectiveness and generality of our proposed method compared with previous approaches. The experimental results show that our proposed penalty term can avoid self-intersection to exclude unreasonable predictions and improves the accuracy of 3D pose estimation indirectly. Further more, the proposed method could be employed universally in triangular mesh based 3D reconstruction

    Reconstructing 3d lung shape from a single 2d image during the deaeration deformation process using model-based data augmentation

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    Three-dimensional (3D) shape reconstruction is particularly important for computer assisted medical systems, especially in the case of lung surgeries, where large deaeration deformation occurs. Recently, 3D reconstruction methods based on machine learning techniques have achieved considerable success in computer vision. However, it is difficult to apply these approaches to the medical field, because the collection of a massive amount of clinic data for training is impractical. To solve this problem, this paper proposes a novel 3D shape reconstruction method that adopts both data augmentation techniques and convolutional neural networks. In the proposed method, a deformable statistical model of the 3D lungs is designed to augment various training data. As the experimental results demonstrate, even with a small database, the proposed method can realize 3D shape reconstruction for lungs during a deaeration deformation process from only one captured 2D image. Moreover, the proposed data augmentation technique can also be used in other fields where the training data are insufficient
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