arXiv subjects

A. Webb

Publications and source records attributed to A. Webb.

4 recordsLinked to original sources

arXiv2026

A fully open-source framework for streaming and cloud-processing of low-field MRI data

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.

physics.comp-ph↗

arXiv2022

Benchmarking the performance of a low-cost Magnetic Resonance Control System at multiple sites in the open MaRCoS community

Purpose: To describe the current properties and capabilities of an open-source hardware and software package that is being developed by many sites internationally with the aim of providing an inexpensive yet flexible platform for low-cost MRI. Methods: This paper describes three different setups from 50 to 360 mT in different settings, all of which used the MaRCoS console for acquiring data, and different types of software interfaces (custom-built GUI or PulSeq overlay) to acquire the data. Results: Images are presented from both phantoms and in vivo from healthy volunteers to demonstrate the image quality that can be obtained from the MaRCoS hardware/software interfaced to different low-field magnets. Conclusions: The results presented here show that a number of different sequences commonly used in the clinic can be programmed into an open-source system relatively quickly and easily, and can produce good quality images even at this early stage of development. Both the hardware and software will continue to develop, and it is an aim of this paper to encourage other groups to join this international consortium.

physics.med-ph↗

arXiv2016

On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach

This paper begins with considering the identification of sparse linear time-invariant networks described by multivariable ARX models. Such models possess relatively simple structure thus used as a benchmark to promote further research. With identifiability of the network guaranteed, this paper presents an identification method that infers both the Boolean structure of the network and the internal dynamics between nodes. Identification is performed directly from data without any prior knowledge of the system, including its order. The proposed method solves the identification problem using Maximum a posteriori estimation (MAP) but with inseparable penalties for complexity, both in terms of element (order of nonzero connections) and group sparsity (network topology). Such an approach is widely applied in Compressive Sensing (CS) and known as Sparse Bayesian Learning (SBL). We then propose a novel scheme that combines sparse Bayesian and group sparse Bayesian to efficiently solve the problem. The resulted algorithm has a similar form of the standard Sparse Group Lasso (SGL) while with known noise variance, it simplifies to exact re-weighted SGL. The method and the developed toolbox can be applied to infer networks from a wide range of fields, including systems biology applications such as signaling and genetic regulatory networks.

eess.SY↗

arXiv2016

Sparse Bayesian Inference of Multivariable ARX Networks

Increasing attention has recently been given to the inference of sparse networks. In biology, for example, most molecules only bind to a small number of other molecules, leading to sparse molecular interaction networks. To achieve sparseness, a common approach consists of applying weighted penalties to the number of links between nodes in the network and the complexity of the dynamics of existing links. The selection of proper weights, however, is non-trivial. Alternatively, this paper proposes a novel data-driven method, called GESBL, that is able to penalise both network sparsity and model complexity without any tuning. GESBL combines Sparse Bayesian Learning (SBL) and Group Sparse Bayesian Learning (GSBL) to introduce penalties for complexity, both in terms of element (system order of nonzero connections) and group sparsity (network topology). The paper considers a class of sparse linear time-invariant networks where the dynamics are represented by multivariable ARX models. Data generated from sparse random ARX networks and synthetic gene regulatory networks indicate that our method, on average, considerably outperforms existing state-of-the-art methods. The proposed method can be applied to a wide range of fields, from systems biology applications in signalling and genetic regulatory networks to power systems.

eess.SY↗