<ul><li>A novel complex-valued diffusion model, PhaseGen, has been introduced for generating synthetic MRI raw data conditioned on magnitude images.</li><li>PhaseGen allows pretraining for models that require k-Space information, enabling downstream tasks such as tumor segmentation and classification.</li><li>The evaluation of PhaseGen on skull-stripping and MRI reconstruction tasks showed significant improvements in segmentation accuracy and reconstruction when combined with limited real-world data.</li><li>This approach bridges the gap between magnitude-based datasets and the complex-valued nature of MRI raw data, enabling more accurate and efficient diagnostic tasks.</li></ul>

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

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