Advanced Neuroimaging Techniques and Applications

Latest papers

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

  Yuqian Chen, R. Jarrett Rushmore, Guikun Chen, Fan Zhang, Edward Yeterian, Nikos Makris, Yogesh Rathi, Lauren J. O'Donnell · 5 October 2026

  The superficial white matter (SWM), a critical brain region for cognition across the lifespan and brain disease, contains abundant short-range association fibers whose organization remains incompletely characterized, in part because the short trajectories and highly variable cortical folding make co…

• Fiber-Resolved Microstructure Quantification from Multi-Shell Diffusion MRI using Detection Transformers

  Sebastian Endt, Marcus Wirth, Johannes Reinhold Schlund, Marion Irene Menzel · 1 October 2026

  Fiber orientation and compartmental microstructure are central to the characterization of white matter tissue in diffusion MRI, yet existing methods either resolve fiber orientations without quantifying microstructure, or quantify microstructure while assuming a fixed number of compartments and a si…

• Is your uncertainty map wrong, or is its target? Exact diagnostics for the Tweedie diagonal, and a gradient-free alternative

  Vicent Ribas, Anna Oliveras Tous · 29 September 2026

  A diffusion model can predict a follow-up medical scan from a baseline, but a clinician needs a per-voxel map of where that prediction can be trusted. Many such maps approximate the diagonal of the Tweedie posterior covariance, and are evaluated against another approximation of it, so whether the es…

• Atlases Are Already Inside: Recovering Population Templates from Pretrained Diffusion Models

  Jian Shi, John Femiani, Peter Wonka · 28 September 2026

  We present a new inference-time sampler for diffusion models that gives a pretrained model a capability it was never trained for: constructing the atlas of the population it synthesizes. The sampler converges from every random seed to the population's central anatomy, which we call the \emph{intrins…

• CATCH: Counterfactual Anatomical Tissue Inpainting with Conditional Haar Diffusion

  Simon Winther Albertsen, Hjalte Bjoernstrup, Said Djafar Said, Mostafa Mehdipour Ghazi · 25 September 2026

  BraTS local synthesis replaces masked regions in T1-weighted brain MRI with plausible tumor-free tissue while preserving observed anatomy. We present CATCH, conditional 3D diffusion in an invertible Haar-wavelet domain. Its denoiser receives noisy target coefficients, voided-image coefficients, and …

• FB-GDM: Fully-Bayesian Guided Diffusion Models for High-Dimensional Linear Inverse Problems via Unsupervised Variational Inference

  Gatien S\'eguy (SATIE), Thomas Rodet (SATIE) · 25 September 2026

  Diffusion models are powerful priors for linear inverse problems, but the reference guidance methods, Diffusion Posterior Sampling (DPS) and Pseudoinverse-Guided Diffusion Models ($\Pi$GDM), rely on scalar hyperparameters tuned per task, usually against the ground truth. We introduce FB-GDM, a fully…

• MGRD: Compact morphology-gated residual diffusion for variance-aware cross-domain neurite forecasting

  Tsung Yeh Hsieh, Cosmin Anitescu, Chunghwan Kim, Victoria A. Webster-Wood, Yongjie Jessica Zhang · 22 September 2026

  Tracking neurite morphology over time helps characterize structural changes during neuronal development and deterioration, but long-term time-lapse imaging is resource-intensive and difficult to scale. Forecasting future morphology could reduce this burden. Existing neurite digital-twin models such …

• GraphSVR: q-Space--Aware Graph-Based Slice-to-Volume Registration for Diffusion MRI

  Noga Kertes, Daphna Link Sourani, Alex M. Bronstein, Moti Freiman · 22 September 2026

  Diffusion-weighted imaging (DWI) remains highly vulnerable to subject motion, particularly in time-efficient protocols and in motion-prone populations. While slice-to-volume registration (SVR) can mitigate inter-slice and inter-stack misalignment, diffusion MRI introduces additional complexity due t…

• Geometry-Aware Diffusion Guidance via Curvature-Adaptive Tubular Correction

  Enze Jiang, Jinwei He, Zheng Ma · 21 September 2026

  Gradient-guided diffusion samplers provide flexible priors for inverse problems and conditional generation, but strong guidance can move the sampling trajectory into regions where the learned score is poorly supported. Existing tangent-projection strategies limit first-order departure from an iso-de…

• MUMINS: Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis

  Anna Oliveras, Roger Mar\'i, Rafael Redondo, Oriol Guardi\`a, Cynthia Ifeyinwa Ugwu, Ana Tost, Bhalaji Nagarajan, Carolina Migliorelli, Vicent Ribas, Petia Radeva · 16 September 2026

  Forecasting anatomical changes such as tumor growth and neurodegeneration is a challenging generative vision task. Morphological evolution is subtle relative to static anatomy, highly patient-specific, and inherently stochastic. Existing methods struggle with several issues: deterministic networks i…

• DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning

  Wenrui Xu, Anas Enanaa, Keshab K. Parhi · 14 September 2026

  Learning robust representations for time-series signals under noise and distribution shifts remains challenging, especially in clinical applications such as electroencephalogram (EEG) and electrocardiogram (ECG) analysis. We propose Diffusion-Conditioned Representation Alignment (DCRA), a training f…

• RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation

  Ramiro Valdes Jara, David Chapman, Adam Meyers · 11 September 2026

  Multivariate time series imputation (MTSI) aims to recover missing values in temporal data composed of multiple interdependent variables. This problem is central to real-world applications such as healthcare monitoring, traffic networks, and energy systems. Recent diffusion-based approaches have sho…

• Topology-Aware Training and Spatial Diagnostics for Fiber Bundle Segmentation in Tracer Histology

  Joselyn Romero Avila, Kyriaki-Margarita Bintsi, Ermias Habte, Julia F. Lehman, Suzanne N. Haber, Anastasia Yendiki · 7 September 2026

  Anatomic tracer studies reveal how axon bundles project from an injection site, branch into smaller groups of axons, and course through the brain to reach their destinations. Histological data from such studies provide anatomical reference information for validating diffusion MRI tractography. Howev…

• Prior-Guided Implicit Neural Representations for Single-Subject Diffusion MRI Super-Resolution

  Abdulkader Ghandoura, Marsil Zakour, William Consagra, Yogesh Rathi · 2 September 2026

  Resolving complex fiber geometries in brain white matter requires high-resolution diffusion MRI at the cost of long acquisition times. This leads many clinical protocols to opt for low-resolution scans, making downstream microstructure estimation and tractography challenging. Implicit neural represe…

• When Denoising Hurts: Rethinking the Terminal Step of Diffusion Time Series Forecasters -- Extended Version

  Dat Nguyen-Cong, Luong Tran, Tung Kieu · 17 August 2026

  Diffusion models offer a natural way to model uncertainty in time series forecasting, yet their iterative sampling process is often treated as a uniformly beneficial refinement procedure. Our study challenges this view by examining how forecast quality evolves throughout reverse diffusion. We find t…

• A Deep RL based Framework for Targeted White Matter Tractography

  Ankita Joshi · 14 August 2026

  Fiber tractography's ability to reconstruct the brain's structural pathways, has made it a crucial component of modern neuroimaging, enabling detailed, non-invasive mapping of structural connectivity and supporting a wide range of neurological research and clinical applications. However, despite its…

• MRI super-resolution in ten sampling steps using a diffusion bridge model

  Mojtaba Safari, Hang Yu, Zach Eidex, Mingzhe Hu, Ryan J. Sanford, Alexandru Florea, Shansong Wang, Chih-Wei Chang, Erik H Middlebrooks, Aditya Juloori, Stanley L. Liauw, Ralph Weichselbaum, Xiaofeng Yang · 11 August 2026

  Objective. MRI provides excellent soft-tissue contrast, but long acquisition times can cause patient discomfort and lead to motion artifacts, forcing a trade-off between spatial resolution and scan time. Diffusion-based super-resolution (SR) reconstructs high-resolution (HR) images from low-resoluti…

• SDDBMs: Soft Denoising Diffusion Bridge Models

  Shiyi Qi, Kun He, Mingmou Liu · 11 August 2026

  Diffusion bridge models leverage Doob's \(h\)-transform to construct stochastic transports between arbitrary endpoint distributions, and have shown strong potential in image-to-image translation and restoration. However, most existing bridge models rely on hard endpoint conditioning, which forces th…

• Rethinking Pixel Mean Flows via Interval Denoiser

  Alexander Zaytsev, Dmitry Baranchuk, Alexander Korotin, Aibek Alanov · 6 August 2026

  Modern diffusion and flow-based models are increasingly moving toward few-step, latent-free generation to bypass the computational overhead of multi-step sampling and the reconstruction bottlenecks of external autoencoders. We propose the Interval Denoiser, a theoretically rigorous framework for lat…

• Heteroscedasticity of Denoising Score Matching with Generalised Smooth Noise

  Juyan Zhang, Rhys Newbury, Xinyang Zhang, Tin Tran, Dana Kulic, Michael Burke · 5 August 2026

  Score Matching (SM) is a powerful framework for estimating the log-density derivatives of a distribution without calculating its normalizing constants. This capability has made it a cornerstone across multiple domains, from classical sta- tistical estimation and energy-based models to modern diffusi…

• Protocol generalisation for brain tissue microstructure estimation via hypernetwork-controlled geometric deep learning

  Andrea Brigliadori, Leevi Kerkela, Hui Zhang · 4 August 2026

  Brain tissue microstructure estimation with machine learning provides higher computational efficiency than conventional fitting. However, machine learning still presents important limitations that hamper its clinical utility. Specifically, current models typically lack generalisation across diffusio…

• Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Quality

  Shengzhi Deng, Chenqi Ye, Yanze Guo · 4 August 2026

  Diffusion models rely on stochastic inputs, yet on finite-precision hardware, the "randomness" they consume is realized as deterministic numerical orbits generated by pseudorandom rules. Accessible orbit structure can become a learnable input and affect both training and generation because the reali…

• Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI

  Antoine Legouhy, Ross Callaghan, Yuchuan Qiao, Whitney Stee, Philippe Peigneux, Hojjat Azadbakht, Hui Zhang · 30 July 2026

  Diffusion MRI (dMRI) relies on diffusion-weighted echo-planar imaging, which is highly susceptible to eddy-current-induced geometric distortions. These distortions vary across diffusion volumes according to gradient strength and direction, causing between-volume misalignment that can bias downstream…

• Manifold-Constrained Noise Optimization for Diverse Diffusion Sampling

  Qitan Shi, Cheng Jin, Ziyuan Liu, Yuantao Gu · 28 July 2026

  Few-step distilled diffusion models generate high-quality images quickly, but often lose per-prompt diversity, producing near-identical samples across random seeds. Optimizing the initial noise at inference time offers an appealing way to recover this diversity, yet existing methods directly update …

• MXAttention: Data-Free Optimal Scaling and Pre-Normalization Quantization for MXFP4 Attention

  Jianlin Yu, Jing Lin, Linghui Kong, Aiyue Chen, Weiyi Sun, Chenyu Zeng, Wangli Lan, Jinxi Li, Zhuo Zheng, Ziyang Yue, Danning Ke, Fei Yi, Tianchi Hu, Yuan Ding, Yiwu Yao, Junsong Wang · 28 July 2026

  The quadratic cost of attention is a major bottleneck in diffusion-based video generation models. MXFP4 attention provides a promising path toward efficient inference, but direct MXFP4 quantization often degrades generation quality due to two numerical issues: the clipping-underflow trade-off from p…