Catálogo de publicaciones - libros

This paper focuses on the estimation of statistical atlases of 3D images by means of diffeomorphic transformations. Within a Log-Euclidean framework, the exponential and logarithm maps of diffeomorphisms need to be computed. In this framework, the Inverse Scaling and Squaring (ISS) method has been recently extended for the computation of the logarithm map, which is one of the most time demanding stages. In this work we propose to apply the Baker-Campbell-Hausdorff (BCH) formula instead. In a 3D simulation study, BCH formula and ISS method obtained similar accuracy but BCH formula was more than 100 times faster. This approach allowed us to estimate a 3D statistical brain atlas in a reasonable time, including the average and the modes of variation. Details for the computation of the modes of variation in the Sobolev tangent space of diffeomorphisms are also provided.

In this paper we present a new way of measuring brain variability based on the registration of sulcal lines sets in the large deformation framework. Lines are modelled geometrically as currents, avoiding then matchings based on point correspondences. At the end we retrieve a globally consistent deformation of the underlying brain space that best matches the lines. Thanks to this framework the measured variability is defined everywhere whereas a previous method introduced by P. Fillard requires tensors extrapolation. Evaluating both methods on the same database, we show that our new approach enables to describe different details of the variability and to highlight the major trends of deformation in the database thanks to a Tangent-PCA analysis.

In this paper, we propose a unified framework for computing atlases from manually labeled data at various degrees of “sharpness” and the joint registration-segmentation of a new brain with these atlases. In non-rigid registration, the tradeoff between warp regularization and image fidelity is typically set empirically. In segmentation, this leads to a probabilistic atlas of arbitrary “sharpness”: weak regularization results in well-aligned training images and a “sharp” atlas; strong regularization yields a “blurry” atlas. We study the effects of this tradeoff in the context of cortical surface parcellation by comparing three special cases of our framework, namely: progressive registration-segmentation of a new brain to increasingly “sharp” atlases with increasingly flexible warps; secondly, progressive registration to a single atlas with increasingly flexible warps; and thirdly, registration to a single atlas with fixed constrained warps. The optimal parcellation in all three cases corresponds to a unique balance of atlas “sharpness” and warp regularization that yield statistically significant improvements over the previously demonstrated parcellation results.

Despite being routinely required in medical applications, deformable surface registration is notoriously difficult due to large intersubject variability and complex geometry of most medical datasets. We present a general and flexible deformable matching framework based on generalized surface flows that efficiently tackles these issues through tailored deformation priors and multiresolution computations. The value of our approach over existing methods is demonstrated for automatic and user-guided cortical registration.

Clinical employment of biomechanical modelling techniques in areas of medical image analysis and surgical simulation is often hindered by conflicting requirements for high fidelity in the modelling approach and high solution speeds. We report the development of techniques for high-speed nonlinear finite element (FE) analysis for surgical simulation. We employ a previously developed nonlinear total Lagrangian explicit FE formulation which offers significant computational advantages for soft tissue simulation. However, the key contribution of the work is the presentation of a fast graphics processing unit (GPU) solution scheme for the FE equations. To the best of our knowledge this represents the first GPU implementation of a nonlinear FE solver. We show that the present explicit FE scheme is well-suited to solution via highly parallel graphics hardware, and that even a midrange GPU allows significant solution speed gains (up to 16.4×) compared with equivalent CPU implementations. For the models tested the scheme allows real-time solution of models with up to 16000 tetrahedral elements. The use of GPUs for such purposes offers a cost-effective high-performance alternative to expensive multi-CPU machines, and may have important applications in medical image analysis and surgical simulation.

A needle-tissue interaction model is an essential part of every needle insertion simulator. In this paper, a new experimental method for the modeling of needle-tissue interaction is presented. The method consists of measuring needle and tissue displacements with ultrasound, measuring needle base forces, and using a deformation simulation model to identify the parameters of a needle-tissue interaction model. The feasibility of this non-invasive approach was demonstrated in an experiment in which a brachytherapy needle was inserted into a prostate phantom. Ultrasound radio-frequency data and the time-domain cross-correlation method, often used in ultrasound elastography, were used to generate the tissue displacement field during needle insertion. A three-parameter force density model was assumed for the needle-tissue interaction. With the needle displacement, tissue displacement and needle base forces as input data, finite element simulations were carried out to adjust the model parameters to achieve a good fit between simulated and measured data.

We simulate the intravasation of liquid distention media into the systemic circulation as it occurs during hysteroscopy and transurethral resection of the prostate. A linear network flow model is extended with a correction for non-newtonian blood behaviour in small vessels and an appropriate handling of vessel compliance. We then integrate a fast lookup scheme in order to allow for real-time simulation. Cutting of tissue is accounted for by adjusting pressure boundary conditions for all cut vessels. We investigate the influence of changing distention fluid pressure settings and of the position of tissue cuts. Our simulation predicts significant intravasation only on the venous side, and just in cases when larger veins are cut. The implemented methods allow the realistic control of bleeding for short-term and the total resulting intravasation volume for long-term complication scenarios. While the simulation is fast enough to support real-time training, it is also adequate for explaining intravasation effects which were previously observed on a phenomenological level only.

The introduction of hybrid scanners has greatly increased the popularity of molecular imaging techniques. Many clinical applications benefit from combining complementary information based on the precise alignment of the two modalities. In case the alignment is inaccurate, then this crucial assumption often made for subsequent processing steps will be violated. However, this violation may not be apparent to the physician. In CT-based attenuation correction (AC) for cardiac SPECT/CT data, critical misalignments between SPECT and CT can lead to spurious perfusion defects. In this work, we focus on increasing the accuracy of rigid volume registration of cardiac SPECT/CT data by using prior knowledge. A new weighting scheme for an intensity co-occurrence prior is introduced to assure accurate and robust alignment in the local heart region. Experimental results demonstrate that the proposed method outperforms mutual information registration and shows robustness across a selection of learned distributions acquired from 15 different patients.

We developed a discrete tomography method for prostate implant reconstructions using only a limited number of X-ray projection images. A 3D voxel volume is reconstructed by back-projection and using distance maps generated from the projection images. The true seed locations are extracted from the voxel volume while false positive seeds are eliminated using a novel optimal geometry coverage model. The attractive feature of our method is that it does not require exact seed segmentation of the X-ray images and it yields near 100% correct reconstruction from only six images with an average reconstruction accuracy of 0.86 mm (std=0.46mm).

We present a new method for blind deconvolution of multiple noisy images blurred by a shift-variant point-spread-function (PSF). We focus on a setting in which several images of the same object are available, and a transformation between these images is known. This setting occurs frequently in biomedical imaging, for example in microscopy or in medical ultrasound imaging. By using the information from multiple observations, we are able to improve the quality of images blurred by a shift-variant filter, prior knowledge of this filter. Also, in contrast to other work on blind and shift-variant deconvolution, in our approach no parametrization of the PSF is required. We evaluate the proposed method quantitatively on synthetically degraded data as well as qualitatively on 3D ultrasound images of liver. The algorithm yields good restoration results and proves to be robust even in presence of high noise levels in the images.