gammaSTAR

gammaSTAR represents the next step in the development of MRI pulse sequences and provides a universal format that is compatible with different MRI models, software versions and vendors.

This solution eliminates reliance on scanner-specific sequences, enables seamless integration and accelerates the transfer of MRI technology into clinical settings.

The integration of gammaSTAR saves time and costs in clinical workflows by enhancing MRI compatibility and minimizing manual sequence adjustments and interaction. In large imaging studies, gammaSTAR helps maintain consistency across multiple devices.

Universal MRI Pulse Sequences

gammaSTAR offers hardware-independent MRI techniques using a novel sequence description language, ensuring compatibility across vendors.

Dynamic Sequence Adaptation

With gammaSTAR, MRI sequences can be configured directly at the MRI and adjusted in real-time during examination, providing unmatched flexibility and advanced MRI techniques.

User-friendly Web Interface and Mature Sequence Library

gammaSTAR offers intuitive development with a state-of-the-art sequence module database that enables easy exploration of MRI sequences.

gammaSTAR is a powerful framework that offers benefits for various user groups.

MRI Sequence Developers benefit from a simplified development process using gammaSTAR's modular sequence design, which allows easy testing and quick design. It also provides ready-to-use MRI sequences with seamless integration into clinical workflows.

Clinicians and Researchers benefit from cross-vendor MRI compatibility, reducing hardware costs, simplifying multi-center studies, and improving efficiency by minimizing sequence adaptation and accelerating the transition from research to clinical use.

Clinic Managers can cut costs by using different MRI models in studies without sacrificing image consistency.

Patients indirectly benefit from faster, more efficient imaging processes, ensuring consistent diagnostic quality across multiple systems.

gammaSTAR is a powerful framework that enables researchers to develop MRI pulse sequences on a user-friendly web platform, featuring real-time visualization and customization tools. It significantly speeds up sequence development/creation through simplified coding and provides universal, ready-to-use results.

We offer a library of ready-to-use, vendor-independent MRI techniques for conventional and advanced imaging, providing consistent performance across MRI platforms.

gammaSTAR is shipped with mature MRI scanner driver software that ensures compatibility across different MRI models, enabling seamless integration of generalized sequences without vendor-specific adjustments.

gammaSTAR’s development potential lies in expanding its library of MRI sequences for clinical use. Further collaboration with MRI manufacturers and the integration of advanced imaging techniques will be key to achieve widespread clinical adoption. We are looking for partners from industry to make use of custom-built quality-assured MRI sequences for clinical use and trials. We provide sustainable, configurable, dynamic, and real-time capable imaging techniques.

Cordes C, Konstandin S, Porter D, Günther M. Portable and platform‐independent MR pulse sequence programs. Magn Reson Med (2020) 83:1277–1290

Hoinkiss DC, Cordes C, Konstandin S, Günther M. Event-Based Traversing of Hierarchical Sequences Allows Real-Time Execution and Arbitrary Looping in a Scanner-Independent MRI Framework. Proc ISMRM (2020) 1043

Hoinkiss DC, Cordes C, Konstandin S, Huber J, Wilke RN, Günther M. Real-Time Sequence Control for Prospective Motion Correction in a Dynamic, Platform-Independent MRI Framework. Proc ESMRMB (2019) S14.02

Nagtegaal M, Hoinkiss DC, Konstandin S, de Bresser J, Günther M, van Osch MJP (2023) Vendor-agnostic pulse programming on gammaSTAR: A traveling head experiment to test the Philips driver. Proceedings of the 39th Annual Scientific Meeting of the ESMRMB. LT54

Hoinkiss DC, Huber J, Plump C, Lüth C, Drechsler R, Günther M (2023) AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language. Frontiers in Neuroimaging – Neuroimaging Analysis and Protocols 2