Voxelisation in the 3-D Fly Algorithm for PET

International audienceThe Fly Algorithm was initially developed for 3-D robot vision applications. It consists in solving the inverse problem of shape reconstruction from projections by evolving a population of 3-D points in space (the 'flies'), using an evolutionary optimisation strategy. Here, in its version dedicated to tomographic reconstruction in medical imaging, the flies are mimicking radioactive photon sources. Evolution is controlled using a fitness function based on the discrepancy of the projections simulated by the flies with the actual pattern received by the sensors. The reconstructed radioactive concentration is derived from the population of flies, i.e. a collection of points in the 3-D Euclidean space, after convergence. 'Good' flies were previously binned into voxels. In this paper, we study which flies to include in the final solution and how this information can be sampled to provide more accurate datasets in a reduced computation time. We investigate the use of density fields, based on Metaballs and on Gaussian functions respectively, to obtain a realistic output. The spread of each Gaussian kernel is modulated in function of the corresponding fly fitness. The resulting volumes are compared with previous work in terms of normalised-cross correlation. In our test-cases, data fidelity increases by more than 10% when density fields are used instead of binning. Our method also provides reconstructions comparable to those obtained using well-established techniques used in medicine (filtered back-projection and ordered subset expectation-maximisation)

Analytical calculation of volumes-of-intersection for iterative, fully 3-D PET reconstruction

Use of iterative algorithms to reconstruct three-dimensional (3-D) positron emission tomography (PET) data requires the computation of the system probability matrix. The pure geometrical contribution can easily be approximated by the length-of-intersection (LOI) between lines-of-response (LOR) and individual voxels. However, more accurate geometrical projectors are desirable. Therefore, we have developed a fast method for the analytical calculation of the 3-D shape and volume of volumes-of-intersection (VOI). This method provides an alternative robust projector with a uniformly continuous sampling of the image space. The enhanced calculation effort is facilitated by using several speedup techniques. Exploiting intrinsic symmetry relations and the sparseness of the system matrix allows to create an efficiently compressed matrix which can be precomputed and completely stored in memory. In addition, a new voxel addressing scheme has been implemented. This scheme avoids time-consuming symmetry transformations of voxel addresses by using an octant-wise symmetrically ordered field of voxels. The above methods have been applied for a fully 3-D, iterative reconstruction of 3-D sinograms recorded with a Siemens/CTI ECAT HR+ PET scanner. A comparison of the performance of the reconstruction using LOI weighting and VOI weighting is presented

Validating Stereoscopic Volume Rendering

The evaluation of stereoscopic displays for surface-based renderings is well established in terms of accurate depth perception and tasks that require an understanding of the spatial layout of the scene. In comparison direct volume rendering (DVR) that typically produces images with a high number of low opacity, overlapping features is only beginning to be critically studied on stereoscopic displays. The properties of the specific images and the choice of parameters for DVR algorithms make assessing the effectiveness of stereoscopic displays for DVR particularly challenging and as a result existing literature is sparse with inconclusive results. In this thesis stereoscopic volume rendering is analysed for tasks that require depth perception including: stereo-acuity tasks, spatial search tasks and observer preference ratings. The evaluations focus on aspects of the DVR rendering pipeline and assess how the parameters of volume resolution, reconstruction filter and transfer function may alter task performance and the perceived quality of the produced images. The results of the evaluations suggest that the transfer function and choice of recon- struction filter can have an effect on the performance on tasks with stereoscopic displays when all other parameters are kept consistent. Further, these were found to affect the sensitivity and bias response of the participants. The studies also show that properties of the reconstruction filters such as post-aliasing and smoothing do not correlate well with either task performance or quality ratings. Included in the contributions are guidelines and recommendations on the choice of pa- rameters for increased task performance and quality scores as well as image based methods of analysing stereoscopic DVR images

A new virtual ring-based system matrix generator for iterative image reconstruction in high resolution small volume PET systems

© 2015 Institute of Physics and Engineering in Medicine. A common approach to improving the spatial resolution of small animal PET scanners is to reduce the size of scintillation crystals and/or employ high resolution pixellated semiconductor detectors. The large number of detector elements results in the system matrix - an essential part of statistical iterative reconstruction algorithms - becoming impractically large. In this paper, we propose a methodology for system matrix modelling which utilises a virtual single-layer detector ring to greatly reduce the size of the system matrix without sacrificing precision. Two methods for populating the system matrix are compared; the first utilises a geometrically-derived system matrix based on Siddon's ray tracer method with the addition of an accurate detector response function, while the second uses Monte Carlo simulation to populate the system matrix. The effectiveness of both variations of the proposed technique is demonstrated via simulations of PETiPIX, an ultra high spatial resolution small animal PET scanner featuring high-resolution DoI capabilities, which has previously been simulated and characterised using classical image reconstruction methods. Compression factors of and are achieved using this methodology for the system matrices produced using the geometric and Monte Carlo-based approaches, respectively, requiring a total of 0.5-1.2 GB of memory-resident storage. Images reconstructed from Monte Carlo simulations of various point source and phantom models, produced using system matrices generated via both geometric and simulation methods, are used to evaluate the quality of the resulting system matrix in terms of achievable spatial resolution and the CRC, CoV and CW-SSIM index image quality metrics. The Monte Carlo-based system matrix is shown to provide the best image quality at the cost of substantial one-off computational effort and a lower (but still practical) compression factor. Finally, a straightforward extension of the virtual ring method to a three dimensional virtual cylinder is demonstrated using a 3D DoI PET scanner

Fabricate 2014

FABRICATE is an international peer reviewed conference that takes place every three years with a supporting publication on the theme of Digital Fabrication. Discussing the progressive integration of digital design with manufacturing processes, and its impact on design and making in the 21st century, FABRICATE brings together pioneers in design and making within architecture, construction, engineering, manufacturing, materials technology and computation. Discussion on key themes includes: how digital fabrication technologies are enabling new creative and construction opportunities from component to building scales, the difficult gap that exists between digital modelling and its realisation, material performance and manipulation, off-site and on-site construction, interdisciplinary education, economic and sustainable contexts. FABRICATE features cutting-edge built work from both academia and practice, making it a unique event that attracts delegates from all over the worl