A fully open-source framework for streaming and cloud-processing of low-field MRI data
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| Autores principales: | , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| author | Guallart-Naval, T. Stairs, J. Algarín, J. M. Xue, H. Benlloch, J. Benlloch, P. Borreguero, J. Conejero, J. Galve, F. García-Cristóbal, P. Lacalle, M. Lena, B. Porcar, L. Schiff, S. J. Webb, A. Hansen, M. Alonso, J. |
| author_facet | Guallart-Naval, T. Stairs, J. Algarín, J. M. Xue, H. Benlloch, J. Benlloch, P. Borreguero, J. Conejero, J. Galve, F. García-Cristóbal, P. Lacalle, M. Lena, B. Porcar, L. Schiff, S. J. Webb, A. Hansen, M. Alonso, J. |
| contents | Purpose: To present a fully open-source framework for quasi-real-time streaming and cloud-based processing of low-field (LF) MRI data, addressing the growing computational demands of advanced reconstruction and post-processing pipelines in portable and affordable MRI systems. Methods: The proposed framework integrates open-source scanner control software with a network-enabled streaming architecture, allowing for raw data to be transmitted directly from the MRI console to remote compute resources. Cloud-based processing modules support image reconstruction and advanced post-processing, including computationally intensive physics- and learning-based methods, while maintaining compatibility with low-cost on-device control hardware. Results: The system enables continuous acquisition-to-reconstruction workflows in LF-MRI without requiring specialized high-performance console architectures. Selected example applications include deep-learning-based denoising, field-induced distortion correction, and non-Cartesian image reconstruction. Experimental demonstrations show reliable streaming performance. Conclusions: Open-source streaming and cloud-processing provide an effective pathway to overcome the computational limitations of embedded LF-MRI consoles. By decoupling acquisition hardware from intensive reconstruction workloads, the proposed framework supports scalable deployment of advanced algorithms while preserving the affordability and portability that motivate LF-MRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19287 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A fully open-source framework for streaming and cloud-processing of low-field MRI data Guallart-Naval, T. Stairs, J. Algarín, J. M. Xue, H. Benlloch, J. Benlloch, P. Borreguero, J. Conejero, J. Galve, F. García-Cristóbal, P. Lacalle, M. Lena, B. Porcar, L. Schiff, S. J. Webb, A. Hansen, M. Alonso, J. Computational Physics Instrumentation and Detectors Medical Physics Purpose: To present a fully open-source framework for quasi-real-time streaming and cloud-based processing of low-field (LF) MRI data, addressing the growing computational demands of advanced reconstruction and post-processing pipelines in portable and affordable MRI systems. Methods: The proposed framework integrates open-source scanner control software with a network-enabled streaming architecture, allowing for raw data to be transmitted directly from the MRI console to remote compute resources. Cloud-based processing modules support image reconstruction and advanced post-processing, including computationally intensive physics- and learning-based methods, while maintaining compatibility with low-cost on-device control hardware. Results: The system enables continuous acquisition-to-reconstruction workflows in LF-MRI without requiring specialized high-performance console architectures. Selected example applications include deep-learning-based denoising, field-induced distortion correction, and non-Cartesian image reconstruction. Experimental demonstrations show reliable streaming performance. Conclusions: Open-source streaming and cloud-processing provide an effective pathway to overcome the computational limitations of embedded LF-MRI consoles. By decoupling acquisition hardware from intensive reconstruction workloads, the proposed framework supports scalable deployment of advanced algorithms while preserving the affordability and portability that motivate LF-MRI. |
| title | A fully open-source framework for streaming and cloud-processing of low-field MRI data |
| topic | Computational Physics Instrumentation and Detectors Medical Physics |
| url | https://arxiv.org/abs/2603.19287 |