Generative Modeling of Complex-Valued Brain MRI Data

• URL: http://arxiv.org/abs/2604.14800v1
• Date: Thu, 16 Apr 2026 09:24:26 GMT
• Title: Generative Modeling of Complex-Valued Brain MRI Data
• Abstract summary: The aim of this study is to build a generative framework capable of jointly modeling magnitude and phase information of complex-valued MRI scans.<n>The proposed generative framework combines a conditional variational autoencoder, which compresses complex-valued MRI scans into compact latent representations.
• Score: 5.046906491246382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 a flow-matching-based generative model. Synthetic sample quality is assessed via a real-versus-synthetic classifier and by training downstream classifiers on synthetic data for abnormal tissue detection. Main results. The autoencoder preserves phase coherence above 0.997. Real-versus-synthetic classification yields low AUROC values between 0.50 and 0.66 across all acquisition sequences, indicating generated samples are nearly indistinguishable from real data. In downstream normal-versus-abnormal classification, classifiers trained entirely on synthetic data achieve an AUROC of 0.880, surpassing the real-data baseline of 0.842 on a publicly available dataset (fastMRI). This advantage persists on an independent external test set from a different institution with biopsy-confirmed labels. Significance. The proposed framework demonstrates the feasibility of jointly modeling magnitude and phase information for normal and abnormal complex-valued brain MRI data. Beyond synthetic data generation, it establishes a foundation for the usage of complete brain MRI information in future diagnostic applications and enables systematic investigation of how magnitude and phase jointly encode pathology-specific features.

Related papers

• Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning [0.0]
  Multimodal MRI provides complementary information for neuroimaging analysis, where different imaging modalities capture distinct anatomical, tissue, and pathological features.<n>Large-scale structural MRI resources are increasingly available, but their modality coverage is often uneven across public and pooled neuroimaging datasets.<n>Our approach combines a Wavelet-Fusion variational autoencoder (WF-VAE) latent compressor with a conditional 3D U-Net diffusion model trained in the learned latent space.
  arXiv  Detail & Related papers  (2026-05-30T11:57:27Z)
• A physics-informed foundation model for quantitative diffusion MRI [46.74037857287099]
  A physics-informed generative microstructure network (PIGMENT) learns a universal generative prior to human brain microstructure and adapts it zero-shot to each participant's measured data to recover subject-specific maps.<n>PIGMENT estimates demonstrated strong biological validity, preserving submillimeter cortical microarchitectural patterns and early-childhood white matter developmental trajectories from 10-fold accelerated scans.<n>These results establish PIGMENT as a physics-informed foundation model that extends quantitative diffusion MRI into regimes traditionally too sparse, heterogeneous, or clinically constrained for reliable analysis.
  arXiv  Detail & Related papers  (2026-05-29T07:26:35Z)
• Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma [0.0]
  Recent developments in artificial intelligence (AI) enable conditional multimodal image generation from clinical data.<n>In this study, we investigate AI-driven generation of follow-up MRI in patients with in- tracranial tumors.
  arXiv  Detail & Related papers  (2026-03-09T13:43:58Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• Assessment of Using Synthetic Data in Brain Tumor Segmentation [0.3222802562733786]
  This study investigates, as a proof of concept, the impact of incorporating synthetic MRI data, generated using a pre-trained GAN model, into training a U-Net segmentation network.<n>Experiments were conducted using real data from the BraTS 2020 dataset, synthetic data generated with the medigan library, and hybrid datasets combining real and synthetic samples in varying proportions.
  arXiv  Detail & Related papers  (2025-08-16T05:56:38Z)
• Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation [2.4345008922715756]
  We employ head and neck cancer CT scans and brain glioma MRI scans from two large datasets.<n>Synthetic data were generated using generative adversarial networks and diffusion models.<n>We evaluate the quality of the synthetic data using MAE, MS-SSIM, Radiomics and a Visual Turing Test (VTT) performed by 5 radiologists.
  arXiv  Detail & Related papers  (2025-06-13T08:17:48Z)
• PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation [1.683019219727036]
  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.
  arXiv  Detail & Related papers  (2025-04-10T08:44:19Z)
• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process.<n>We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds [49.34500499203579]
  We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics. We generate high-quality synthetic fMRI data based on user-supplied demographics.
  arXiv  Detail & Related papers  (2024-05-13T17:49:20Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers [0.0]
  We introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adrial TransformERs (SLATER) A zero-shot reconstruction is performed on undersampled test data, where inference is performed by optimizing network parameters. Experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against several state-of-the-art unsupervised methods.
  arXiv  Detail & Related papers  (2021-05-15T02:01:21Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.