PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation

• URL: http://arxiv.org/abs/2504.07560v1
• Date: Thu, 10 Apr 2025 08:44:19 GMT
• Title: PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
• Authors: Moritz Rempe, Fabian Hörst, Helmut Becker, Marco Schlimbach, Lukas Rotkopf, Kevin Kröninger, Jens Kleesiek,
• Abstract summary: Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information.<n>We introduce $textitPhaseGen$, a novel complex-valued diffusion model for generating synthetic MRI raw data conditioned on magnitude images.<n>Our results show that training with synthetic phase data significantly improves generalization for skull-stripping on real-world data.
• Score: 1.683019219727036
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 datasets and the complex-valued nature of MRI raw data. This approach allows researchers to leverage the vast amount of avaliable image domain data in combination with the information-rich k-Space data for more accurate and efficient diagnostic tasks. We make our code publicly $\href{https://github.com/TIO-IKIM/PhaseGen}{\text{available here}}$.

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