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PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation by Rempe, Moritz; Hörst, Fabian; Becker, Helmut; Schlimbach, Marco; Rotkopf, Lukas; Kröninger, Kevin; Kleesiek, Jens is a scholarly article available to read on EtoBox.

What is PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation about?

Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information. However, clinical and existing Artificial Intelligence (AI)-based methods focus only on magnitude images, discarding the phase data despite its potential for downstream tasks, such as tumor segmentation and classification. In this work, we introduce $\textit{PhaseGen}$, a novel complex-valued diffusion model for generating synthetic MRI raw data conditioned on magnitude images, commonly used in clinical practice. This enables the creation of artificial complex-valued raw data, allowing pretraining for models that require k-Space information. We evaluate PhaseGen on two tasks: skull-stripping directly in k-Space and MRI reconstruction using the publicly available FastMRI dataset. Our results show that training with synthetic phase data significantly improves generalization for skull-stripping on real-world data, with an increased segmentation accuracy from $41.1\%$ to $80.1\%$, and enhances MRI reconstruction when combined with limited real-world data. This work presents a step forward in utilizing generative AI to bridge the gap between magnitude-based dat

Author

Rempe, Moritz; Hörst, Fabian; Becker, Helmut; Schlimbach, Marco; Rotkopf, Lukas; Kröninger, Kevin; Kleesiek, Jens

Published

2025

Language

EN