We present the ASTRA Toolbox as an open platform for 3D image reconstruction in tomography. Most of the software tools that are currently used in electron tomography offer limited flexibility with respect to the geometrical parameters of the acquisition model and the algorithms used for reconstruction. The ASTRA Toolbox provides an extensive set of fast and flexible building blocks that can be used to develop advanced reconstruction algorithms, effectively removing these limitations. We demonstrate this flexibility, the resulting reconstruction quality, and the computational efficiency of this toolbox by a series of experiments, based on experimental dual-axis tilt series.

Object reconstruction from a series of projection images, such as in computed tomography (CT), is a popular tool in many different application fields. Existing commercial software typically provides sufficiently accurate and convenient-to-use reconstruction tools to the end-user. However, in applications where a non-standard acquisition protocol is used, or where advanced reconstruction methods are required, the standard software tools often are incapable of computing accurate reconstruction images. This article introduces the ASTRA Toolbox. Aimed at researchers across multiple tomographic application fields, the ASTRA Toolbox provides a highly efficient and highly flexible open source set of tools for tomographic projection and reconstruction. The main features of the ASTRA Toolbox are discussed and several use cases are presented.

Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al.[1], a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU's cache is used more efficiently, making more effective use of the available memory bandwidth.Keywords: Electron Tomography, Reconstruction, GPU Recently, iterative algebraic methods, such as ART and SIRT, have gained popularity in the electron tomography community due to their flexibility with respect to the geometric parameters of the tilt series, and their ability to handle noisy projection data. The use of algebraic reconstruction methods imposes major computational demands. Depending on the number of iterations, reconstructing a large 3D volume with a sequential implementation can easily take days on a normal PC. This obstacle can be largely overcome by parallelizing the computations, in particular the projection and backprojection steps. Graphics Processing Units (GPUs) have recently emerged as powerful parallel processors for general-purpose computations. Their architecture allows operations to be performed on a large number of data elements simultaneously.Several algorithmic strategies have already been proposed for implementing algebraic methods for electron tomography on the GPU. In [2], it was demonstrated by Castaño-Diez et al. that at that time, a GPU implementation of the SIRT algorithm could achieve similar performance to a CPU implementation running on a medium sized cluster. Xu et al. recently proposed a different implementation strategy [1] that leads to a speedup of an order of magnitude compared to the results from [2]. They attribute this speedup to improvements in three categories: minimizing synchronization overhead, encouraging latency hiding, and exploiting RGBA channel parallelism. The first two design goals are interdependent and cannot be optimized separately. In this technical note, we argue that by exploiting data locality more effectively, the runtime of the projection and back projection operations can be substantially reduced, even though the required number of thread synchronization steps will increase. We demonstrate that a significant speedup can be gained in this manner. Exploiting data localityThe Graphics Processing Unit (GPU) is well suited for carrying out the computations involved in ...