| Abstract: |
| TIGRE (Tomographic Iterative GPU-based Reconstruction Toolbox) and LION (Learning-based Iterative Optimized Networks) are two advanced software toolboxes designed for applied computed tomography (CT) reconstruction, each reflecting a different paradigm in modern imaging. TIGRE Toolbox is an open-source, GPU-accelerated platform developed to support fast and flexible implementation of iterative reconstruction algorithms, particularly for cone-beam CT. It provides a wide range of analytical and iterative methods, enabling researchers to model system geometry accurately and experiment with techniques such as total variation minimization and algebraic reconstruction. Its emphasis on computational efficiency makes it well suited for large-scale 3D problems and real-time applications.
In contrast, LION Toolbox focuses on integrating deep learning into the reconstruction pipeline. It enables the design and training of neural networks that mimic or enhance iterative algorithms, often leading to improved image quality and reduced reconstruction time. LION supports hybrid approaches that combine model-based physics with data-driven learning, making it particularly relevant for low-dose and sparse-view CT scenarios.
Together, TIGRE and LION illustrate the convergence of classical optimization and modern machine learning, providing complementary tools for advancing research and practical applications in CT reconstruction. |
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