arXiv Machine Learning By Pushpendra Singh (School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK), Joshua R. Astley (School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK), Roman Rodionov (Department of Epilepsy, Queen Square Institute of Neurology, University College London, London, UK), John Duncan (Department of Epilepsy, Queen Square Institute of Neurology, University College London, London, UK), Tom Vercauteren (School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK), Rachel Sparks (School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK)

Spatial Feature-wise Linear Modulation (SpFiLM) for Contrast Agent-Aware Brain Parcellation

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

arXiv Computer Vision
Oct 23

AA-ViT: Anatomically Aware Vision Transformer with Structural and Frequency Guidance for Contrast Enhanced Brain MRI Synthesis

The paper introduces AA‑ViT, an anatomically aware vision transformer that synthesizes contrast‑enhanced brain MRI from pre‑contrast modalities (T1, T2, FLAIR) by incorporating structural and frequency guidance. Experiments on the BraTS 2021 dataset show that AA‑ViT preserves anatomical and lesion boundaries, achieving higher PSNR and SSIM than existing methods. A clinical evaluation by four experts on 19 glioma cases yielded a mean score of 3.94/5, indicating promising clinical relevance.

By Talha Meraj, Tom Flannery, Charlie Cummins, Matt Townend, Thomas C Booth, Peter Crossley, Michael McCann, Ian Overton, Saritha Unnikrishnan
arXiv AI
Aug 3

Dense Temporal Contrast Synthesis via Conditioned Latent Transport

arXiv:2607. 29394v1 Announce Type: cross Abstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast agents (GBCAs) restricts use in contraindicated populations, prolongs scan protocols, and presents environmental toxicity concerns.

By Smriti Joshi, Apostolia Tsirikoglou, Daniel M. Lang, Richard Osuala, Noah M\'arquez Varaa, Alejandro Guzman, Grzegorz Skorupko, Sebastian Ibarra Arregui, Lidia Garrucho, Akane Ohashi, Dimitra Ntoula, Eugen Divjak, O\u{g}uz Lafc{\i}, Jan C. Peeken, Julia A. Schnabel, Fredrik Strand, Oliver Diaz, Karim Lekadir
arXiv Machine Learning
Sep 22

Forecasting Intrathecal Tracer Enhancement from Pre-Contrast Brain MRI: Direct Regression versus Flow Matching

arXiv:2609.22377v1 Announce Type: cross Abstract: Intrathecal contrast-enhanced MRI tracks how a cerebrospinal-fluid tracer spreads through the brain, but requires repeated scans over 24--48\,h. Fore...

By Qinghui Liu, Jon Andr\'e Ottesen, Thu Nguyen, Geir Ringstad, Ingrid Mossige, Siri Fl{\o}gstad Svensson, Atle Bj{\o}rnerud, Per Kristian Eide, Kyrre Eeg Emblem
arXiv AI
Oct 15

Forecasting Brain Tracer Distribution from Pre-Contrast MRI: A Comparison of Direct Regression and Flow Matching

arXiv:2609.22377v2 Announce Type: replace-cross Abstract: Intrathecal contrast-enhanced MRI shows how a tracer injected into the cerebrospinal fluid spreads through the brain, but requires repeated s...

By Qinghui Liu, Jon Andr\'e Ottesen, Thu Nguyen, Geir Ringstad, Ingrid Mossige, Siri Fl{\o}gstad Svensson, Atle Bj{\o}rnerud, Per Kristian Eide, Kyrre Eeg Emblem
arXiv Computer Vision
Sep 14

Learning Sparse Latent Predictive Foundation Model for Multimodal Neuroimaging

The paper introduces Neuro‑JEPA, a sparse multimodal foundation model that learns unified representations of brain MRI across T1w, T2w, and FLAIR sequences using a latent predictive objective and a Mixture‑of‑Experts architecture. It was pretrained on over 1.5 million scans from 428,647 studies and systematically evaluates architectural, masking, objective, and sparsity choices for robust multimodal representation learning. Across 47 tasks from three health systems and 12 public datasets, Neuro‑JEPA consistently outperforms a simple CNN baseline, demonstrating its effectiveness for both clinical and research applications.

By Haoxu Huang, Long Chen, Jingyun Chen, Jinu Hyun, James Ryan Loftus, Kara Melmed, Daniel Orringer, Jennifer Frontera, Seena Dehkharghani, Arjun Masurkar, Narges Razavian
arXiv AI
Oct 15

Generalizable field of view extension for diffusion MRI via conditional flow matching

arXiv:2610.17401v1 Announce Type: new Abstract: Incomplete field-of-view (FOV) diffusion MRI (dMRI) acquisitions compromise whole-brain white matter analysis by excluding brain regions from tractogra...

By Gaurav Rudravaram, Lianrui Zuo, Ema Topolnjak, Michael E. Kim, Adam M. Saunders, Elyssa M. McMaster, Paula Trujillo, Ely E. Wesley, Colin D. McKnight, Pratik P. Pandharipande, Mayur B. Patel, Cayce Strength, Brenda T. Pund, Amelia W Maiga, Robert S. Dittus, David A. Bennett, Julie A. Schneider, Konstantinos Arfanakis, The HABS-HD Study Team, Derek B. Archer, Timothy J. Hohman, Yelena Bodien, Daniel C. Moyer, Kurt G. Schilling, Bennett A. Landman