DMD-augmented Unpaired Neural Schrödinger Bridge for Ultra-Low Field MRI Enhancement

• URL: http://arxiv.org/abs/2603.03769v2
• Date: Thu, 05 Mar 2026 04:07:26 GMT
• Title: DMD-augmented Unpaired Neural Schrödinger Bridge for Ultra-Low Field MRI Enhancement
• Abstract summary: As paired 64 mT - 3 T scans are scarce, we propose an unpaired 64 mT $rightarrow$ 3 T translation framework.<n>Our method builds upon the Unpaired Neural Schrdinge Bridge (UNSB) with multi-step refinement.
• Score: 45.02203416553436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultra Low Field (64 mT) brain MRI improves accessibility but suffers from reduced image quality compared to 3 T. As paired 64 mT - 3 T scans are scarce, we propose an unpaired 64 mT $\rightarrow$ 3 T translation framework that enhances realism while preserving anatomy. Our method builds upon the Unpaired Neural Schrödinge Bridge (UNSB) with multi-step refinement. To strengthen target distribution alignment, we augment the adversarial objective with DMD2-style diffusion-guided distribution matching using a frozen 3T diffusion teacher. To explicitly constrain global structure beyond patch-level correspondence, we combine PatchNCE with an Anatomical Structure Preservation (ASP) regularizer that enforces soft foreground background consistency and boundary aware constraints. Evaluated on two disjoint cohorts, the proposed framework achieves an improved realism structure trade-off, enhancing distribution level realism on unpaired benchmarks while increasing structural fidelity on the paired cohort compared to unpaired baselines.

Related papers

• Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images [48.646448077284276]
  We propose Spatial-FAD, a framework for effective few-shot medical anomaly detection.<n>It improves lesion localization by combining VFM spatial priors with CLIP semantics.<n>Our method achieves an average improvement of over 11.4% in Dice score and 1.8% in AUC.
  arXiv  Detail & Related papers  (2026-09-11T05:21:01Z)
• A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery [5.486895610501164]
  Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy.<n>We propose NA-UNETR, a 3D transformer-based segmentation model whose Neighborhood Attention (NA) and Dilated NA (DiNA) blocks jointly capture fine structural detail and long-range context.<n>A composite Dice-Focal and Hausdorff loss, dynamically balanced via homoscedastic uncertainty, improves overlap and boundary accuracy.
  arXiv  Detail & Related papers  (2026-08-12T17:13:59Z)
• SpikeDS: Dual Sparsity Spikformer for Perineural Invasion Prediction in 3D MRI [9.308617762081804]
  Perineural invasion (PNI) is associated with poor prognosis in cholangiocarcinoma (CCA)<n>Existing deep learning approaches incur prohibitive computational costs on volumetric medical images, limiting their clinical deployment.<n>We propose Dual Sparsity Spikformer (SpikeDS), a spiking neural network architecture that jointly exploits activation sparsity from binary spike communication and spatial sparsity from window pruning based on firing rates.
  arXiv  Detail & Related papers  (2026-07-13T14:03:55Z)
• Spatio-Temporal Mixture-of-Modality-Experts Diffusion for Quantitative DCE-MRI Synthesis from Incomplete MR Sequences [2.4827516038838064]
  ST-MoME is a conditional diffusion framework that synthesizes 3D DCE parameter maps from diverse subsets multimodal MRI.<n>It performs diffusion directly in image space with 3D patch-based training and a Swin-based backbone.<n>It achieves the lowest mean Normalized Mean Square Error (NMSE) aggregated across all three DCE parameters, with leading performance on $v_p$ and $v_e$, competitive results on $Kmathrmtrans$, and the lowest reconstruction error within the clinically critical tumor region.
  arXiv  Detail & Related papers  (2026-06-24T08:10:27Z)
• CoDMD: Copula-aware Distribution Matching Distillation for Fast Video Generation [50.353919095724315]
  Few-step distillation for video diffusion models has attracted significant attention, driven by the urgent demand for efficient deployment in real-world scenarios.<n>We propose Copula-aware DMD (CoDMD), a lightweight relational regularizer that reuses score estimates already produced by the frozen teacher and the online fake model to construct pairwise relation matrices.<n>On the Wan-2.1-T2V model series at 1.3B & 14B scales, CoDMD distills 50-step teachers into 4-step students, achieving an approximate 25$times$ speed-up while attaining VBench scores of 84.46 & 84.87,
  arXiv  Detail & Related papers  (2026-06-20T10:33:14Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities [54.87947751720332]
  We propose a novel segmentation model D3Seg to maintain stable performance under missing-modality settings.<n>The proposed model achieves approximately 1.5-2.0% Dice improvement on enhancing tumor (ET) and around 1.0% on tumor core (TC) across multiple missing modality configurations.
  arXiv  Detail & Related papers  (2026-05-21T09:55:42Z)
• Align then Refine: Text-Guided 3D Prostate Lesion Segmentation [5.284052813431228]
  Volumetric methods must combine multiple modalities while ensuring anatomical consistency.<n>Current models struggle to integrate cross-modal information reliably.<n>We propose a new multi-encoder U-Net architecture incorporating three key innovations.
  arXiv  Detail & Related papers  (2026-04-20T18:13:52Z)
• UniField: A Unified Field-Aware MRI Enhancement Framework [45.03230466428695]
  methodname is a unified framework integrating multiple modalities and enhancement tasks.<n>We exploit comprehensive 3D volumetric information by leveraging pre-trained 3D foundation models.<n>We organize and publicly release a comprehensive paired multi-field MRI dataset.
  arXiv  Detail & Related papers  (2026-03-10T05:45:12Z)
• Preoperative-to-intraoperative Liver Registration for Laparoscopic Surgery via Latent-Grounded Correspondence Constraints [51.7011449975586]
  Land-Reg is a deformable registration framework that learns latent-grounded 2D-3D landmark correspondences.<n>For rigid registration, Land-Reg embraces a Cross-modal Latent Alignment module.<n>An Uncertainty-enhanced Overlap Landmark Detector with similarity matching is proposed to robustly estimate explicit 2D-3D landmark correspondences.
  arXiv  Detail & Related papers  (2026-03-02T10:44:03Z)
• Specializing Foundation Models via Mixture of Low-Rank Experts for Comprehensive Head CT Analysis [6.04562866374803]
  We propose a Mixture of Low-Rank Experts (MoLRE) framework that extends LoRA with multiple specialized low-rank adapters and unsupervised soft routing.<n>We present a benchmark of MoLRE across six state-of-the-art medical imaging foundation models spanning 2D and 3D architectures, general-domain, medical-domain, and head CT-specific pretraining, and model sizes ranging from 7M to 431M parameters.
  arXiv  Detail & Related papers  (2026-02-28T14:32:38Z)
• Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling [10.62087466710015]
  Anatomically Guided Latent Diffusion Model (AG-LDM) is a segmentation-guided framework that enforces anatomically consistent progression.<n>A lightweight 3D tissue segmentation model (WarpSeg) provides explicit anatomical supervision during both autoencoder fine-tuning and diffusion model training.
  arXiv  Detail & Related papers  (2026-01-21T01:45:36Z)
• T3: Test-Time Model Merging in VLMs for Zero-Shot Medical Imaging Analysis [15.624549727053475]
  Existing model-merging techniques fail to deliver consistent gains across diverse medical modalities.<n>We introduce Test-Time Task adaptive merging (T3), a backpropagation-free framework that computes per-sample coefficients.<n>We present a rigorous cross-evaluation protocol spanning in-domain, base-to-novel, and corruptions across four modalities.
  arXiv  Detail & Related papers  (2025-10-31T08:05:40Z)
• MR2US-Pro: Prostate MR to Ultrasound Image Translation and Registration Based on Diffusion Models [7.512221808783586]
  We present a novel framework that addresses the challenges through a two-stage process: TRUS 3D reconstruction followed by cross-modal registration.<n>We propose a totally probe-location-independent approach that leverages the natural correlation between sagittal and transverse TRUS views.<n>For the registration stage, we introduce an unsupervised diffusion-based framework guided by modality translation.
  arXiv  Detail & Related papers  (2025-05-31T14:55:03Z)
• Schrödinger Diffusion Driven Signal Recovery in 3T BOLD fMRI Using Unmatched 7T Observations [1.8091533096543726]
  We introduce a new computational approach designed to enhance the quality of 3T BOLD fMRI acquisitions.<n>We employ a lightweight, unsupervised Schr"odinger Bridge framework to infer a high-SNR, high-resolution counterpart of the 3T data.<n>Our findings suggest that it is feasible to computationally approximate 7T-level quality from standard 3T acquisitions.
  arXiv  Detail & Related papers  (2025-04-01T17:41:24Z)
• Guided MRI Reconstruction via Schrödinger Bridge [15.448360566887407]
  Cross-contrast priors can be exploited to improve image reconstruction from undersampled data.<n>$mathbfI2$SB-Inversion is a guided reconstruction framework based on the Schr"odinger Bridge (SB)
  arXiv  Detail & Related papers  (2024-11-21T16:25:56Z)
• Inducing and Using Alignments for Transition-based AMR Parsing [51.35194383275297]
  We propose a neural aligner for AMR that learns node-to-word alignments without relying on complex pipelines. We attain a new state-of-the art for gold-only trained models, matching silver-trained performance without the need for beam search on AMR3.0.
  arXiv  Detail & Related papers  (2022-05-03T12:58:36Z)
• Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling [67.75095378830694]
  We develop a two-stage odometry estimation network, where we obtain the ego-motion by estimating a set of sub-region transformations. In this paper, we aim to alleviate the influence of unreliable structures in training, inference and mapping phases. Our two-frame odometry outperforms the previous state of the arts by 16%/12% in terms of translational/rotational errors.
  arXiv  Detail & Related papers  (2022-02-27T12:52:27Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• PR-RRN: Pairwise-Regularized Residual-Recursive Networks for Non-rigid Structure-from-Motion [58.75694870260649]
  PR-RRN is a novel neural-network based method for Non-rigid Structure-from-Motion. We propose two new pairwise regularizations to further regularize the reconstruction. Our approach achieves state-of-the-art performance on CMU MOCAP and PASCAL3D+ dataset.
  arXiv  Detail & Related papers  (2021-08-17T08:39:02Z)

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.