Abstract #2934

A Digital Phantom for 3D MR Spectroscopy Data Simulation

Dennis van de Sande¹, Aaron Gudmundson^2,3,4, Christopher Davies-Jenkins^2,3, Dunja Simicic^2,3, Gizeaddis Simegn^2,3, Ipek Özdemir^2,3, Sina Amirrajab¹, Julian Merkofer⁵, Helge Zöllner^2,3, Georg Oeltzschner^2,3, and Richard Edden^2,3

¹Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, ²Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ³F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ⁴The Malone Center for Engineering in Healthcare, Johns Hopkins University School of Engineering, Baltimore, MD, United States, ⁵Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands

Synopsis

Keywords: Spectroscopy, Simulation/Validation

Motivation: Simulating MRS and MRSI data is essential for validating new methodologies, yet optimal practices remain undefined due to varied signal modeling and validation approaches.

Goal(s): This study proposes a comprehensive, modular simulation framework that provides a high-quality, digital MRS phantom as an open-source Python tool, enhancing data reproducibility and validation flexibility.

Approach: A modular Python framework combines anatomical brain models with metabolic data from a curated literature database, allowing customization of simulation parameters for flexible application.

Results: The resulting framework efficiently simulates MRS(I) data in a batch-wise format, producing spectra ready for further analysis.

Impact: This MRS phantom framework provides a flexible, tissue-specific model for realistic MRS and MRSI dataset simulation. Its modular design allows for precise control over the parameters and the simulation process to support a wide range of research and development applications.

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