ArticlePloS one2017
FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- On the fragility of neural architecture search: the role of overfitting and task complexity in medical image analysis.Frontiers in artificial intelligence · 2026Article
- Hybrid Reconstruction Approach for Polychromatic Computed Tomography in Highly Limited-Data Scenarios.Sensors (Basel, Switzerland) · 2024Article
- Simplified Statistical Image Reconstruction for X-ray CT With Beam-Hardening Artifact Compensation.IEEE transactions on medical imaging · 2020Article
- GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems.BMC bioinformatics · 2018Article
- Enabling tomography with low-cost C-arm systems.PloS one · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The availability of digital X-ray detectors, together with advances in reconstruction algorithms, creates an opportunity for bringing 3D capabilities to conventional radiology systems. The downside is that reconstruction algorithms for non-standard acquisition protocols are generally based on iterative approaches that involve a high computational burden. The development of new flexible X-ray systems could benefit from computer simulations, which may enable performance to be checked before expensive real systems are implemented. The development of simulation/reconstruction algorithms in this context poses three main difficulties. First, the algorithms deal with large data volumes and are computationally expensive, thus leading to the need for hardware and software optimizations. Second, these optimizations are limited by the high flexibility required to explore new scanning geometries, including fully configurable positioning of source and detector elements. And third, the evolution of the various hardware setups increases the effort required for maintaining and adapting the implementations to current and future programming models. Previous works lack support for completely flexible geometries and/or compatibility with multiple programming models and platforms. In this paper, we present FUX-Sim, a novel X-ray simulation/reconstruction framework that was designed to be flexible and fast. Optimized implementation for different families of GPUs (CUDA and OpenCL) and multi-core CPUs was achieved thanks to a modularized approach based on a layered architecture and parallel implementation of the algorithms for both architectures. A detailed performance evaluation demonstrates that for different system configurations and hardware platforms, FUX-Sim maximizes performance with the CUDA programming model (5 times faster than other state-of-the-art implementations). Furthermore, the CPU and OpenCL programming models allow FUX-Sim to be executed over a wide range of hardware platforms.
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Registered trials
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