Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition

• URL: http://arxiv.org/abs/2603.21806v2
• Date: Sun, 29 Mar 2026 07:42:40 GMT
• Title: Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition
• Abstract summary: Full data acquisition in MRI is inherently slow, which limits clinical throughput and increases patient discomfort.<n>CSMRI seeks to accelerate acquisition by reconstructing images from under-sampled kspace data.
• Score: 27.825869252861335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Full data acquisition in MRI is inherently slow, which limits clinical throughput and increases patient discomfort. Compressed Sensing MRI (CS-MRI) seeks to accelerate acquisition by reconstructing images from under-sampled k-space data, requiring both an optimal sampling trajectory and a high-fidelity reconstruction model. In this work, we propose a novel active sampling framework that leverages the inherent discrete structure of a pretrained medical image tokenizer and a latent transformer. By representing anatomy through a dictionary of quantized visual tokens, the model provides a well-defined probability distribution over the latent space. We utilize this distribution to derive a principled uncertainty measure via token entropy, which guides the active sampling process. We introduce two strategies to exploit this latent uncertainty: (1) Latent Entropy Selection (LES), projecting patch-wise token entropy into the $k$-space domain to identify informative sampling lines, and (2) Gradient-based Entropy Optimization (GEO), which identifies regions of maximum uncertainty reduction via the $k$-space gradient of a total latent entropy loss. We evaluate our framework on the fastMRI singlecoil Knee and Brain datasets at $\times 8$ and $\times 16$ acceleration. Our results demonstrate that our active policies outperform state-of-the-art baselines in perceptual metrics, and feature-based distances. Our code is available at https://github.com/levayz/TRUST-MRI.

Related papers

• MUMINS: Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis [12.80647654676651]
  We propose MUMINS (Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis), an efficient diffusion framework.<n>It jointly diffuses a baseline scan and its follow-up residual, summed to synthesize the follow-up scan, while concurrently predicting a spatial uncertainty map.<n>Designed without organ-specifics, the same architecture is reused across anatomies via separate, dataset-specific retraining.
  arXiv  Detail & Related papers  (2026-09-15T13:32:37Z)
• Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging [92.90858286761515]
  Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks.<n>We propose the first task-adapted CS-MRI from the information-theoretic perspective to simultaneously achieve probabilistic inference for uncertainty prediction.<n>We leverage amortized optimization and construct tractable variational bounds for mutual information to jointly optimize sampling, reconstruction, and task-inference models.
  arXiv  Detail & Related papers  (2026-04-14T13:23:19Z)
• Knowledge-Informed Neural Network for Complex-Valued SAR Image Recognition [51.03674130115878]
  We introduce the Knowledge-Informed Neural Network (KINN), a lightweight framework built upon a novel "compression-aggregation-compression" architecture.<n>KINN establishes a state-of-the-art in parameter-efficient recognition, offering exceptional generalization in data-scarce and out-of-distribution scenarios.
  arXiv  Detail & Related papers  (2025-10-23T07:12:26Z)
• Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations [8.781276186760962]
  We develop a novel generative-adversarially trained implicit neural representations (k-GINR) for de novo undersampled non-Cartesian k-space reconstruction.<n>k-GINR consistently outperformed the baselines with a larger performance advantage observed at very high accelerations.
  arXiv  Detail & Related papers  (2025-03-07T00:05:43Z)
• A Steerable Deep Network for Model-Free Diffusion MRI Registration [4.813333335683418]
  We present a novel, deep learning framework for model-free, nonrigid registration of raw diffusion MRI data.<n>This work establishes a foundation for data-driven, geometry-aware dMRI registration directly in the acquisition space.
  arXiv  Detail & Related papers  (2025-01-08T19:18:44Z)
• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process.<n>We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping [9.783361575598025]
  CoRRECT is a unified deep unfolding (DU) framework for Quantitative MRI (qMRI)<n>It consists of a model-based end-to-end neural network, a method for motion-artifact reduction, and a self-supervised learning scheme.<n>Our results on experimentally collected multi-Gradient-Recalled Echo (mGRE) MRI data show that CoRRECT recovers motion and inhomogeneity artifact-free R2* maps in highly accelerated acquisition settings.
  arXiv  Detail & Related papers  (2022-10-12T15:49:51Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• Complex-valued Federated Learning with Differential Privacy and MRI Applications [51.34714485616763]
  We introduce the complex-valued Gaussian mechanism, whose behaviour we characterise in terms of $f$-DP, $(varepsilon, delta)$-DP and R'enyi-DP. We present novel complex-valued neural network primitives compatible with DP. Experimentally, we showcase a proof-of-concept by training federated complex-valued neural networks with DP on a real-world task.
  arXiv  Detail & Related papers  (2021-10-07T14:03:00Z)
• Robust Compressed Sensing MRI with Deep Generative Priors [84.69062247243953]
  We present the first successful application of the CSGM framework on clinical MRI data. We train a generative prior on brain scans from the fastMRI dataset, and show that posterior sampling via Langevin dynamics achieves high quality reconstructions.
  arXiv  Detail & Related papers  (2021-08-03T08:52:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.