arXiv Machine Learning 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

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

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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 Machine Learning
Sep 10

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

arXiv:2609.07718v1 Announce Type: cross Abstract: Most automated brain parcellation tools are developed and validated on T1-weighted (T1w) MRI. Yet, some clinical workflows for which parcellation is...

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)
arXiv AI
Sep 1

Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images

arXiv:2608.29348v1 Announce Type: new Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but...

By Jakob Wasserthal, Joshy Cyriac, Michael Bach, Kimia Mozahheb Yousefi, Minh-Son To, M\'at\'e Sik, C\'edric H\'emon, Thomas Weikert, Martin Segeroth
arXiv AI
Sep 25

CATCH: Counterfactual Anatomical Tissue Inpainting with Conditional Haar Diffusion

CATCH is a conditional 3D diffusion model operating in an invertible Haar-wavelet domain designed to inpaint masked regions in T1‑weighted brain MRI with plausible, tumor‑free tissue while preserving observed anatomy. The model’s denoiser uses noisy target coefficients, voided‑image coefficients, and a signed mask, guided by tumor‑excluded wavelet reconstruction and a hole‑focused loss, and hard compositing ensures observed voxels remain unchanged. Experiments on BraTS data show that a weighted mixture of tumor‑derived, irregular‑blob, and ellipsoidal masks yields the best performance, achieving higher SSIM, PSNR, and lower MSE compared to fixed or random augmentation baselines.

By Simon Winther Albertsen, Hjalte Bjoernstrup, Said Djafar Said, Mostafa Mehdipour Ghazi
arXiv Computer Vision
Oct 27

Spectrally optimized implicit neural representations mitigate acquisition-related image quality trade-offs in intracranial 4D Flow MRI

arXiv:2610.33124v1 Announce Type: cross Abstract: 4D Flow MRI promises opportunities for non-invasive mapping of neurovascular hemodynamics, but its clinical utility faces trade-offs between resoluti...

By Oliver Welin Odeback, Nerea Gonz\'alez-Aranceta, Sebasti\'an Jofre, Javier Bisbal, Edward Ferdian, Alistair A. Young, Pia Callmer, Sameer A. Ansari, Mia Bonini, David Nordsletten, Patrick Winter, Susanne Schnell, Tobias Granberg, Alexander Fyrdahl, David Marlevi