Advance reconstruction with Tyger

From release v1.0.0, MaRGE have compatibility with Tyger. The open-source toolkit from Microsoft to stream signal data to and from remote compute for on-demand processing.

The MaRGE–Tyger integration enables seamless execution of remote reconstruction and processing pipelines directly within the MaRGE workflow. Its architecture, illustrated in next figure, is designed to bridge local data acquisition with scalable remote computation, allowing users to leverage containerized processing resources without disrupting the standard scanning procedure.

In this integration, native MaRGE raw data files are first converted locally from the proprietary .mat format into the open MRD (Magnetic Resonance Data) standard. Alongside the converted dataset, MaRGE generates a .yaml configuration file that specifies the selected reconstruction or processing pipeline and defines all required inputs. These parameters are configured directly within the MaRGE graphical environment, ensuring consistency between acquisition settings and remote execution.

The MRD dataset and corresponding configuration file are then streamed to a remote computing resource via Tyger. The requested pipeline is executed inside a Docker container, ensuring reproducibility and environment isolation. When deployed in a cloud environment (e.g., Azure), the required container image can be dynamically retrieved from a public registry; alternatively, it may be pre-deployed on a designated remote node. Upon completion of processing, the reconstructed data are streamed back to the local system and automatically integrated into the MaRGE interface.

Capabilities

The MaRGE–Tyger integration extends the standard acquisition workflow with advanced remote and model-based processing features. The current implementation supports the following capabilities:

• Streaming of high-demanding tasks
  Computationally intensive reconstruction and processing pipelines can be offloaded to remote computing resources through Tyger. Raw MRD datasets and their associated configuration files are streamed for execution within containerized environments, enabling scalable performance without impacting local scanner operation.

• SNRAware processing using the local model
  The framework supports SNRAware reconstruction strategies that leverage a locally available noise model. This improves robustness and reconstruction quality under varying noise regimes.

• Reconstruction with prior knowledge of the magnetic field
  Reconstruction pipelines can incorporate prior information about the magnetic field distribution. By integrating field knowledge into the processing stage, the system enables improved correction of field-related distortions and enhanced image fidelity.

Requirements

Integration

The integration supports both acquisition-time and post-processing workflows. Within the acquisition interface, authenticated access to Tyger services is provided through the graphical interface, and users can select the desired processing pipeline prior to sequence execution. Reconstruction results are returned and displayed automatically as part of the acquisition output, requiring no additional intervention beyond launching the scan. To prevent disruption of scanner operation, MaRGE supports parallel execution: new sequences may be initiated while previously acquired datasets are being transmitted or reconstructed remotely.

In addition to real-time acquisition workflows, Tyger functionality is also accessible from the post-processing interface. Users can apply remote pipelines to previously acquired raw datasets through a dedicated Tyger tab, extending the same remote execution capabilities beyond the live scanning context.

All features described here are implemented in the current version of the MaRGE repository.

Configuration

• Tyger server: server where processing will take place. It may be a tyger TEP server, a local server computer, or even the own client computer.
• Tyger batch size: batch size used to do image reconstruction with prior knowledge
• SNRAware version: None, Local, or TEP, according to the SNRAware version to use.
• Docker for distortion correction: path to the docker file used for distortion correction.

Working with tyger TEP

Running reconstruction in Tyger TEP

Running reconstruction in a local server

Running SNRAware open-source

Running SNRAware in Tyger TEP

Reconstruction with prior-knowledge