A Line--Search--Based Stochastic Gradient Method for 3D Computed Tomography
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866909036192989184 |
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| author | Bubba, Tatiana A. Morotti, Elena Porta, Federica Ruggiero, Valeria Trombini, Ilaria |
| author_facet | Bubba, Tatiana A. Morotti, Elena Porta, Federica Ruggiero, Valeria Trombini, Ilaria |
| contents | We introduce FB-LISA, a forward-backward (FB) generalization of a recently proposed line-search-based stochastic gradient algorithm to address the imaging problem of volumetric reconstruction in Computed Tomography, a substantially high demanding problem, which involves orders of magnitude of data, a high computational burden for forward and backprojection, and memory requirements that push current GPU architectures to their limits. Our formulation employs stochastic mini-batches composed of full 2D projections, preserving the physical structure of the acquisition process while enabling significant speed-ups during early iterations. The resulting method demonstrates how concepts traditionally associated with deep learning can be repurposed to accelerate large-scale inverse problems, without relying on training data or learned priors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12085 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Line--Search--Based Stochastic Gradient Method for 3D Computed Tomography Bubba, Tatiana A. Morotti, Elena Porta, Federica Ruggiero, Valeria Trombini, Ilaria Numerical Analysis We introduce FB-LISA, a forward-backward (FB) generalization of a recently proposed line-search-based stochastic gradient algorithm to address the imaging problem of volumetric reconstruction in Computed Tomography, a substantially high demanding problem, which involves orders of magnitude of data, a high computational burden for forward and backprojection, and memory requirements that push current GPU architectures to their limits. Our formulation employs stochastic mini-batches composed of full 2D projections, preserving the physical structure of the acquisition process while enabling significant speed-ups during early iterations. The resulting method demonstrates how concepts traditionally associated with deep learning can be repurposed to accelerate large-scale inverse problems, without relying on training data or learned priors. |
| title | A Line--Search--Based Stochastic Gradient Method for 3D Computed Tomography |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2605.12085 |