PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
Journal:
arXiv
Published Date:
Apr 10, 2025
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
$\href{https://github.com/TIO-IKIM/PhaseGen}{\text{available here}}$.
