Feasibility of diffusion tensor imaging (DTI) with fibre tractography of the normal female pelvic floor

To prospectively determine the feasibility of diffusion tensor imaging (DTI) with fibre tractography as a tool for the three-dimensional (3D) visualisation of normal pelvic floor anatomy. Five young female nulliparous subjects (mean age 28 ± 3 years) underwent DTI at 3.0T. Two-dimensional diffusion-weighted axial spin-echo echo-planar (SP-EPI) pulse sequence of the pelvic floor was performed, with additional T2-TSE multiplanar sequences for anatomical reference. Fibre tractography for visualisation of predefined pelvic floor and pelvic wall muscles was performed offline by two observers, applying a consensus method. Three eigenvalues (λ1, λ2, λ3), fractional anisotropy (FA) and mean diffusivity (MD) were calculated from the fibre trajectories. In all subjects fibre tractography resulted in a satisfactory anatomical representation of the pubovisceral muscle, perineal body, anal - and urethral sphincter complex and internal obturator muscle. Mean FA values ranged from 0.23 ± 0.02 to 0.30 ± 0.04, MD values from 1.30 ± 0.08 to 1.73 ± 0.12 × 10(-)³ mm²/s. Muscular structures in the superficial layer of the pelvic floor could not be satisfactorily identified. This study demonstrates the feasibility of visualising the complex three-dimensional pelvic floor architecture using 3T-DTI with fibre tractography. DTI of the deep female pelvic floor may provide new insights into pelvic floor disorder

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Performance evaluation of a distributed clustering approach for spatial datasets

The analysis of big data requires powerful, scalable, and accurate data analytics techniques that the traditional data mining and machine learning do not have as a whole. Therefore, new data analytics frameworks are needed to deal with the big data challenges such as volumes, velocity, veracity, variety of the data. Distributed data mining constitutes a promising approach for big data sets, as they are usually produced in distributed locations, and processing them on their local sites will reduce significantly the response times, communications, etc. In this paper, we propose to study the performance of a distributed clustering, called Dynamic Distributed Clustering (DDC). DDC has the ability to remotely generate clusters and then aggregate them using an efficient aggregation algorithm. The technique is developed for spatial datasets. We evaluated the DDC using two types of communications (synchronous and asynchronous), and tested using various load distributions. The experimental results show that the approach has super-linear speed-up, scales up very well, and can take advantage of the recent programming models, such as MapReduce model, as its results are not affected by the types of communication

Visualization of uncertainty in fiber tracking based on diffusion tensor imaging

Diffusion tensor imaging (DTI) is an imaging technique based on magnetic resonance that describes, in each point of the tissue, the distribution of diffusing water molecules. The distribution is mathematically modelled using a second-order tensor. In fibrous tissues the diffusion tensor will have an elongated, ellipsoid shape whose main axis is assumed to be aligned with the underlying fiber structure. Fiber tractography traces paths through the tensor field by following each tensor's main direction thereby resulting in a three-dimensional reconstruction of the fibers. This is particularly interesting for the exploration and visualization of neuronal connections in brain white matter and has great potential for applications in neuroscience and neurosurgery. DTI and fiber tractography are unique in that they provide insight into white matter structures in vivo and non-invasively. However, despite these capabilities the application of DTI and fiber tractography in clinical practice remains limited. The image acquisition and post-processing pipeline is complex and consists of many stages. At each stage errors and uncertainties are introduced due to image noise, magnetic distortions, partial volume effects, scanner settings, diffusion model assumptions and user parameters. These uncertainties are propagated through the pipeline and possibly enhanced in subsequent stages thereby leading to potentially unreliable results in the final tractography output. To the user the processing pipeline behaves like a black box whose internal details remain hidden and whose quality of output cannot be reliably assessed. Contrary to standard CT and MR images it is not possible to look at the "raw" diffusion-weighted images. Without further processing the images are practically meaningless. This means the user either has to accept (and trust) the processing output or refrain from using fiber tracking all together. In this thesis we assume that the user has certain reservations about the quality of the tractography output. Unfortunately, there is no gold standard against which the output of tractography can be validated. Consequently, we cannot make definitive statements about the "true" certainty or uncertainty of fiber reconstructions. We can, however, discuss tractography output in terms of stability and reproducibility. The output of tractography algorithms can be subject to large variations. In this thesis we present a number of visualization strategies that make these variations visible to the user and allow a better assessment of the reliability of fiber reconstructions obtained from any given tractography algorithm

Flexible GPU-based multi-volume ray-casting

Using combinations of different volumetric datasets is becoming more common in scientific applications, especially medical environments such as neurosurgery where multiple imaging modalities are required to provide insight to both anatomical and functional structures in the brain. Such data sets are usually in different orientations and have different resolutions. Furthermore, it is often interesting, e.g. for surgical planning or intraoperative applications to add the visualization of foreign objects (e.g., surgical tools, reference grids, 3D measurement widgets). We propose a flexible framework based on GPU-accelerated ray-casting and depth peeling, that allows volume rendering of multiple, arbitrarily positioned volumes intersected with opaque or translucent geometric objects. These objects can also be used as convex or concave clipping shapes. We consider the main contribution of our work to be the flexible combination of the above-mentioned features in a single framework. As such, it can serve as a basis for neurosurgery applications but also for other fields where multi-volume rendering is important