Generative Modeling of Complex-Valued Brain MRI Data
Objective. Standard Magnetic Resonance Imaging (MRI) reconstruction pipelines discard phase information captured during acquisition, despite evidence that it encodes tissue properties relevant to tumor diagnosis. Current machine learning approaches inherit this limitation by operating exclusively on reconstructed magnitude images. The aim of this study is to build a generative framework which is capable of jointly modeling magnitude and phase information of complex-valued MRI scans. Approach. The proposed generative framework combines a conditional variational autoencoder, which compresses complex-valued MRI scans into compact latent representations while preserving phase coherence, with ...
| Authors | Marco Schlimbach, Moritz Rempe, Jessica Mnischek et al. |
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| Primary category | eess.IV |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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