Four-dimensional tomographic reconstruction by time domain decomposition

Since the beginnings of tomography, the requirement that the sample does not change during the acquisition of one tomographic rotation is unchanged. We derived and successfully implemented a tomographic reconstruction method which relaxes this decades-old requirement of static samples. In the presented method, dynamic tomographic data sets are decomposed in the temporal domain using basis functions and deploying an L1 regularization technique where the penalty factor is taken for spatial and temporal derivatives. We implemented the iterative algorithm for solving the regularization problem on modern GPU systems to demonstrate its practical use

dOpenCL: Towards a Uniform Programming Approach for Distributed Heterogeneous Multi-/Many-Core Systems

Modern computer systems are becoming increasingly heterogeneous by comprising multi-core CPUs, GPUs, and other accelerators. Current programming approaches for such systems usually require the application developer to use a combination of several programming models (e. g., MPI with OpenCL or CUDA) in order to exploit the full compute capability of a system. In this paper, we present dOpenCL (Distributed OpenCL) – a uniform approach to programming distributed heterogeneous systems with accelerators. dOpenCL extends the OpenCL standard, such that arbitrary computing devices installed on any node of a distributed system can be used together within a single application. dOpenCL allows moving data and program code to these devices in a transparent, portable manner. Since dOpenCL is designed as a fully-fledged implementation of the OpenCL API, it allows running existing OpenCL applications in a heterogeneous distributed environment without any modifications. We describe in detail the mechanisms that are required to implement OpenCL for distributed systems, including a device management mechanism for running multiple applications concurrently. Using three application studies, we compare the performance of dOpenCL with MPI+OpenCL and a standard OpenCL implementation

Photoacoustic image reconstruction from few-detector and limited-angle data

Photoacoustic tomography (PAT) is an emerging non-invasive imaging technique with great potential for a wide range of biomedical imaging applications. However, the conventional PAT reconstruction algorithms often provide distorted images with strong artifacts in cases when the signals are collected from few measurements or over an aperture that does not enclose the object. In this work, we present a total-variation-minimization (TVM) enhanced iterative reconstruction algorithm that can provide excellent photoacoustic image reconstruction from few-detector and limited-angle data. The enhancement is confirmed and evaluated using several phantom experiments

Modelling fungal colonies and communities:challenges and opportunities

This contribution, based on a Special Interest Group session held during IMC9, focuses on physiological based models of filamentous fungal colony growth and interactions. Fungi are known to be an important component of ecosystems, in terms of colony dynamics and interactions within and between trophic levels. We outline some of the essential components necessary to develop a fungal ecology: a mechanistic model of fungal colony growth and interactions, where observed behaviour can be linked to underlying function; a model of how fungi can cooperate at larger scales; and novel techniques for both exploring quantitatively the scales at which fungi operate; and addressing the computational challenges arising from this highly detailed quantification. We also propose a novel application area for fungi which may provide alternate routes for supporting scientific study of colony behaviour. This synthesis offers new potential to explore fungal community dynamics and the impact on ecosystem functioning

On Open and Strong-Scaling Tools for Atom Probe Crystallography: High-Throughput Methods for Indexing Crystal Structure and Orientation

Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemistry and the microstructure of a material. For electron and X-ray diffraction methods it is possible to develop indexing tools which compare measured and analytically computed patterns to decode the structure and relative orientation within local regions of interest. Consequently, a number of numerically efficient and automated software tools exist to solve the above characterisation tasks. For atom probe tomography (APT) experiments, however, the strategy of making comparisons between measured and analytically computed patterns is less robust because many APT datasets may contain substantial noise. Given that general enough predictive models for such noise remain elusive, crystallography tools for APT face several limitations: Their robustness to noise, and therefore, their capability to identify and distinguish different crystal structures and orientation is limited. In addition, the tools are sequential and demand substantial manual interaction. In combination, this makes robust uncertainty quantifying with automated high-throughput studies of the latent crystallographic information a difficult task with APT data. To improve the situation, we review the existent methods and discuss how they link to those in the diffraction communities. With this we modify some of the APT methods to yield more robust descriptors of the atomic arrangement. We report how this enables the development of an open-source software tool for strong-scaling and automated identifying of crystal structure and mapping crystal orientation in nanocrystalline APT datasets with multiple phases.Comment: 36 pages, 19 figures, preprin

High Speed 3D Tomography on CPU, GPU, and FPGA

12 pages; 50% d'acceptationInternational audienceBack-projection (BP) is a costly computational step in tomography image reconstruction such as positron emission tomography (PET). To reduce the computation time, this paper presents a pipelined, prefetch, and parallelized architecture for PET BP (3PA-PET). The key feature of this architecture is its original memory access strategy, masking the high latency of the external memory. Indeed, the pattern of the memory references to the data acquired hinders the processing unit. The memory access bottleneck is overcome by an efficient use of the intrinsic temporal and spatial locality of the BP algorithm. A loop reordering allows an efficient use of general purpose processor's caches, for software implementation, as well as the 3D predictive and adaptive cache (3D-AP cache), when considering hardware implementations. Parallel hardware pipelines are also efficient thanks to a hierarchical 3D-AP cache: each pipeline performs a memory reference in about one clock cycle to reach a computational throughput close to 100%. The 3PA-PET architecture is prototyped on a system on programmable chip (SoPC) to validate the system and to measure its expected performances. Time performances are compared with a desktop PC, a workstation, and a graphic processor unit (GPU)

Acceleration of GATE Monte Carlo simulations

Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography are forms of medical imaging that produce functional images that reflect biological processes. They are based on the tracer principle. A biologically active substance, a pharmaceutical, is selected so that its spatial and temporal distribution in the body reflects a certain body function or metabolism. In order to form images of the distribution, the pharmaceutical is labeled with gamma-ray-emitting or positron-emitting radionuclides (radiopharmaceuticals or tracers). After administration of the tracer to a patient, an external position-sensitive gamma-ray camera can detect the emitted radiation to form a stack of images of the radionuclide distribution after a reconstruction process. Monte Carlo methods are numerical methods that use random numbers to compute quantities of interest. This is normally done by creating a random variable whose expected value is the desired quantity. One then simulates and tabulates the random variable and uses its sample mean and variance to construct probabilistic estimates. It represents an attempt to model nature through direct simulation of the essential dynamics of the system in question. Monte Carlo modeling is the method of choice for all applications where measurements are not feasible or where analytic models are not available due to the complex nature of the problem. In addition, such modeling is a practical approach in nuclear medical imaging in several important application fields: detector design, quantification, correction methods for image degradations, detection tasks etc. Several powerful dedicated Monte Carlo simulators for PET and/or SPECT are available. However, they are often not detailed nor flexible enough to enable realistic simulations of emission tomography detector geometries while also modeling time dependent processes such as decay, tracer kinetics, patient and bed motion, dead time or detector orbits. Our Monte Carlo simulator of choice, GEANT4 Application for Tomographic Emission (GATE), was specifically designed to address all these issues. The flexibility of GATE comes at a price however. The simulation of a simple prototype SPECT detector may be feasible within hours in GATE but an acquisition with a realistic phantom may take years to complete on a single CPU. In this dissertation we therefore focus on the Achilles’ heel of GATE: efficiency. Acceleration of GATE simulations can only be achieved through a combination of efficient data analysis, dedicated variance reduction techniques, fast navigation algorithms and parallelization. In the first part of this dissertation we consider the improvement of the analysis capabilities of GATE. The static analysis module in GATE is both inflexible and incapable of storing more detail without introducing a large computational overhead. However, the design and validation of the acceleration techniques in this dissertation requires a flexible, detailed and computationally efficient analysis module. To this end, we develop a new analysis framework capable of analyzing any process, from the decay of isotopes to particle interactions and detections in any detector element for any type of phantom. The evaluation of our framework consists of the assessment of spurious activity in 124I-Bexxar PET and of contamination in 131I-Bexxar SPECT. In the case of PET we describe how our framework can detect spurious coincidences generated by non-pure isotopes, even with realistic phantoms. We show that optimized energy thresholds, which can readily be applied in the clinic, can now be derived in order to minimize the contamination. We also show that the spurious activity itself is not spatially uniform. Therefore standard reconstruction and correction techniques are not adequate. In the case of SPECT we describe how it is now possible to classify detections into geometric detections, phantom scatter, penetration through the collimator, collimator scatter and backscatter in the end parts. We show that standard correction algorithms such as triple energy window correction cannot correct for septal penetration. We demonstrate that 124I PET with optimized energy thresholds offer better image quality than 131I SPECT when using standard reconstruction techniques. In the second part of this dissertation we focus on improving the efficiency of GATE with a variance reduction technique called Geometrical Importance Sampling (GIS). We describe how only 0.02% of all emitted photons can reach the crystal surface of a SPECT detector head with a low energy high resolution collimator. A lot of computing power is therefore wasted by tracking photons that will not contribute to the result. A twofold strategy is used to solve this problem: GIS employs Russian Roulette to discard those photons that will not likely contribute to the result. Photons in more important regions on the other hand are split into several photons with reduced weight to increase their survival chance. We show that this technique introduces branches into the particle history. We describe how this can be taken into account by a particle history tree that is used for the analysis of the results. The evaluation of GIS consists of energy spectra validation, spatial resolution and sensitivity for low and medium energy isotopes. We show that GIS reaches acceleration factors between 5 and 13 over analog GATE simulations for the isotopes in the study. It is a general acceleration technique that can be used for any isotope, phantom and detector combination. Although GIS is useful as a safe and accurate acceleration technique, it cannot deliver clinically acceptable simulation times. The main reason lies in its inability to force photons in a specific direction. In the third part of this dissertation we solve this problem for 99mTc SPECT simulations. Our approach is twofold. Firstly, we introduce two variance reduction techniques: forced detection (FD) and convolution-based forced detection (CFD) with multiple projection sampling (MPS). FD and CFD force copies of photons at decay and at every interaction point to be transported through the phantom in a direction sampled within a solid angle toward the SPECT detector head at all SPECT angles simultaneously. We describe how a weight must be assigned to each photon in order to compensate for the forced direction and non-absorption at emission and scatter. We show how the weights are calculated from the total and differential Compton and Rayleigh cross sections per electron with incorporation of Hubbell’s atomic form factor. In the case of FD all detector interactions are modeled by Monte Carlo, while in the case of CFD the detector is modeled analytically. Secondly, we describe the design of an FD and CFD specialized navigator to accelerate the slow tracking algorithms in GEANT4. The validation study shows that both FD and CFD closely match the analog GATE simulations and that we can obtain an acceleration factor between 3 (FD) and 6 (CFD) orders of magnitude over analog simulations. This allows for the simulation of a realistic acquisition with a torso phantom within 130 seconds. In the fourth part of this dissertation we exploit the intrinsic parallel nature of Monte Carlo simulations. We show how Monte Carlo simulations should scale linearly as a function of the number of processing nodes but that this is usually not achieved due to job setup time, output handling and cluster overhead. We describe how our approach is based on two steps: job distribution and output data handling. The job distribution is based on a time-domain partitioning scheme that retains all experimental parameters and that guarantees the statistical independence of each subsimulation. We also reduce the job setup time by the introduction of a parameterized collimator model for SPECT simulations. We reduce the data output handling time by a chain-based output merger. The scalability study is based on a set of simulations on a 70 CPU cluster and shows an acceleration factor of approximately 66 on 70 CPUs for both PET and SPECT.We also show that our method of parallelization does not introduce any approximations and that it can be readily combined with any of the previous acceleration techniques described above

Real-time reconstruction and visualisation towards dynamic feedback control during time-resolved tomography experiments at TOMCAT

Tomographic X-ray microscopy beamlines at synchrotron light sources worldwide have pushed the achievable time-resolution for dynamic 3-dimensional structural investigations down to a fraction of a second, allowing the study of quickly evolving systems. The large data rates involved impose heavy demands on computational resources, making it difficult to readily process and interrogate the resulting volumes. The data acquisition is thus performed essentially blindly. Such a sequential process makes it hard to notice problems with the measurement protocol or sample conditions, potentially rendering the acquired data unusable, and it keeps the user from optimizing the experimental parameters of the imaging task at hand. We present an efficient approach to address this issue based on the real-time reconstruction, visualisation and on-the-fly an