← Back to all news
arXiv AI By Julia Huang, Camila Gonzalez, Rydham Goyal, Aja Zou, Sasha Alexander, Michael Moseley, Moss Y. Zhao, Gary K. Steinberg

Synthesizing Post-Acetazolamide Cerebral Blood Flow Maps from Baseline MRI in Moyamoya Using 3D Generative AI

Read the original on arXiv AI →

arXiv:2608. 14758v1 Announce Type: cross Abstract: For patients with Moyamoya disease, impaired cerebrovascular reserve (CVR) is an important hemodynamic criterion for recommending extracranial-to-intracranial bypass surgery.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

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
safety
More like this →
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
diffusion
More like this →
arXiv Machine Learning
Sep 3

Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps

The study presents a multimodal BrainAGE framework that fuses structural T1-weighted MRI with DeepCBV maps—vascular information synthesized from non‑contrast MRI—to estimate brain age more accurately. Using two separate 3D convolutional neural networks, the combined model achieved a mean absolute error of 3.95 years on cognitively normal controls, outperforming single‑modality models. Saliency analyses showed MRI highlighted white matter and cortical atrophy, while DeepCBV emphasized vascular‑rich regions, and the integrated approach revealed stronger differentiation between stable and progressive MCI, indicating sensitivity to early vascular changes.

By Jordan Jomsky, Zongyu Li, Kay C. Igwe, Yiren Zhang, Max Lashley, Tal Nuriel, Andrew Laine, Scott A. Small, Jia Guo, for the Frontotemporal Lobar Degeneration Neuroimaging Initiative, for the Alzheimer's Disease Neuroimaging Initiative
multimodal
More like this →