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arXiv Computer Vision

Tensor-based Brain Surface Modeling and Analysis

The paper introduces a unified computational framework for tensor-based morphometry that integrates surface modeling, smoothing, and statistical analysis to detect brain surface shape differences between clinical groups using MRI data. It focuses on the cerebral cortex’s 2‑D convoluted topology, measuring local surface area and curvature variations that may differ across groups. Diffusion smoothing via explicit Laplace‑Beltrami operator estimation enhances signal‑to‑noise, and the method is illustrated by localizing gray‑matter growth and loss in longitudinal pediatric brain scans.

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arXiv AI
Sep 18

Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge

The paper introduces DB‑SUiT, a surface‑based diffusion bridge that translates cortical MRI to PET images directly on the cortical manifold. It employs a conditional spherical U‑shaped vision Transformer to capture multi‑scale surface features and long‑range dependencies while incorporating demographic and subcortical information. Evaluations on two dementia datasets show that the synthesized PET surfaces outperform MRI and PET volumes in automated classification and achieve high diagnostic accuracy in a blinded reader study.

By Yitong Li, Alexandra Samoylova, Fabian Bongratz, Timo Grimmer, Dennis M. Hedderich, Igor Yakushev, Christian Wachinger
llmsdiffusioncomputer-visionmultimodal
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arXiv Computer Vision
Sep 15

DTI-Guided Volumetric Spherical Harmonics Regression for Single-to-Multi-Shell dMRI Synthesis

arXiv:2609.14312v1 Announce Type: new Abstract: Multi-shell diffusion MRI (dMRI) unlocks more expressive microstructural modeling than single-shell scans, yet its longer acquisition time hinders depl...

By Binghua Li, Christina Andica, Tong Liang, Ziqing Chang, Chao Li, Wataru Uchida, Kaito Takabayashi, Qibin Zhao, Toshihisa Tanaka, Zhe Sun, Shigeki Aoki
diffusion
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arXiv AI
Jun 2

A physics-informed foundation model for quantitative diffusion MRI

arXiv:2606. 00156v1 Announce Type: cross Abstract: Understanding the human brain requires access to its microscopic tissue architecture.

By Zihan Li, Jialan Zheng, Ziyu Li, Xun Yuan, Kasidit Anmahapong, Ziang Wang, Mingxuan Liu, Hongjia Yang, Yifei Chen, Zhuhao Wang, Yuhang He, Fang Chen, Rui Li, Huaiqiang Sun, Yi Liao, Congyu Liao, Yang Yang, Haibo Qu, Xue Zhang, Hongen Liao, Qiyuan Tian
diffusion
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arXiv AI
Aug 24

SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges

This review discusses how neuroimaging data can be represented as symmetric positive-definite (SPD) matrices and analyzed using the Riemannian geometry of the SPD manifold. It surveys the evolution from modality-specific SPD representations to geometric shallow and deep learning methods, emphasizing how these approaches maintain structural constraints while integrating modern AI techniques. The paper frames SPD matrix learning as a bridge between classical geometric statistics and contemporary machine learning in neuroimaging and brain‑computer interface research.

By Ce Ju, Reinmar Kobler, Antoine Collas, Motoaki Kawanabe, Cuntai Guan, Bertrand Thirion
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arXiv Machine Learning
Jun 18

Structural MRI Synthesis for Alzheimer's Disease via Conditional Diffusion on Anatomical Masks

arXiv:2606. 18354v1 Announce Type: cross Abstract: Recent advances in generative machine learning models have significantly improved medical imaging, offering promising solutions for data augmentation, privacy preservation, and improved model generalization.

By Muge Zhang, Muhammad Ali Khaliq, Jamal Alsakran, Byeong Kil Lee, Jeeho Ryoo
diffusioncomputer-visionsafety
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arXiv Machine Learning
Jun 16

Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis

arXiv:2606. 15457v1 Announce Type: cross Abstract: 3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns.

By Weidong Zhang, Yongchan Jung, Shafayat Mowla Anik, Furen Xiao, Vasudevan Janarthanan, Enkhzaya Chuluunbaatar, Byeong Kil Lee, Jeeho Ryoo
diffusioncomputer-visionbenchmarks
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arXiv Computer Vision
Oct 5

FiberGeoText: A Vision-Language Model for Population- Level Organization of Superficial White Matter

FiberGeoText (FGT) is a vision‑language model that clusters short‑range superficial white matter streamlines from ultra‑high‑resolution diffusion MRI into population‑level groups. It jointly encodes each streamline’s 3‑D trajectory, cortical anatomical context (via text from multiple parcellation schemes), and shape, using a pretrained large language model to unify heterogeneous anatomical descriptions. Evaluations on 0.76 mm diffusion data show that FGT outperforms state‑of‑the‑art methods in cortical parcel coherence, shape consistency, cluster‑size consistency, and cross‑subject correspondence, and it generalizes well to unseen subjects, recovering 96.7 % of learned clusters.

By Yuqian Chen, R. Jarrett Rushmore, Guikun Chen, Fan Zhang, Edward Yeterian, Nikos Makris, Yogesh Rathi, Lauren J. O'Donnell
llmsragdiffusionmultimodalbenchmarks
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