Heterogeneity-Adaptive Diffusion Schrodinger Bridge for PET-Guided Whole-Body MRI Translation

• URL: http://arxiv.org/abs/2607.07401v1
• Date: Wed, 08 Jul 2026 13:35:22 GMT
• Title: Heterogeneity-Adaptive Diffusion Schrodinger Bridge for PET-Guided Whole-Body MRI Translation
• Abstract summary: Deep learning-based MRI translation provides a potential solution to reduce scan duration.<n>Current models often focus on specific anatomical regions and face challenges for whole-body scans.<n>This paper tackles the challenges through a novel Heterogeneity-aware-language framework.
• Score: 5.215825299661144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acquisition time in PET-MR scanning is a major obstacle in more efficient clinical practice. Deep learning-based MRI translation provides a potential solution to reduce scan duration. However, current models often focus on specific anatomical regions and face challenges for whole-body scans that consists of highly heterogeneous feature distributions mainly due to (1) different anatomical regions across whole-body, and (2) lesions or pathological tissues. This paper tackles the challenges through a novel Heterogeneity-Adaptive Diffusion Schrodinger Bridge (HA-DSB) framework. By explicitly modeling translation as stochastic transport between source and target distributions, HA-DSB incorporates region context embeddings derived from a vision-language model (VLM) to enable region-specific modeling. To enhance fidelity of the pathological tissue, lesion-aware metabolic prior from PET is integrated directly into the bridge dynamics through a dual-stage guidance mechanism. Specifically, a PET-guided noise modulation module adaptively scales spatial diffusion perturbations during the forward process, while PET features are leveraged during the reverse process to selectively amplify lesion-relevant structures via an attention mechanism. Experiments demonstrate the superiority of our method across different body regions in whole-body MRI translation and show improved translation quality in lesion areas under PET guidance. Our code is available at Github.

Related papers

• Delta-Diffusion: Modeling Longitudinal Brain Amyloid-PET Trajectories via Conditional Poisson Diffusion Bridge [2.4844989305098997]
  We propose Delta-Diffusion, a novel progression-aware framework that redefines longitudinal PET synthesis as a conditional Poisson Diffusion Bridge (PDB) process.<n>Delta-Diffusion demonstrates superior performance in capturing variations in amyloid deposition compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-06-20T20:34:48Z)
• Translating MRI to PET through Conditional Diffusion Models with Enhanced Pathology Awareness [9.229807776105504]
  We propose PASTA, a novel image translation framework built on conditional diffusion models with enhanced pathology awareness.<n>For Alzheimer's diagnosis, the performance of these synthesized scans improves over MRI by 4%, almost reaching the performance of actual PET.
  arXiv  Detail & Related papers  (2026-03-19T13:36:19Z)
• A Generalist Foundation Model for Total-body PET/CT Enables Diagnostic Reporting and System-wide Metabolic Profiling [17.709593152803695]
  SDF-HOLO is a multimodal foundation model for holistic total-body PET/CT, pre-trained on more than 10,000 patients.<n>It decouples CT and PET representation learning with dual-stream encoders and couples them through a cross-modal interaction module.<n>The model enables system-wide metabolic profiling and reveals tumor-associated fingerprints of inter-organ metabolic network interactions.
  arXiv  Detail & Related papers  (2026-01-19T08:30:09Z)
• Unveiling and Bridging the Functional Perception Gap in MLLMs: Atomic Visual Alignment and Hierarchical Evaluation via PET-Bench [48.60251555171943]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in tasks such as abnormality detection and report generation for anatomical modalities.<n>In this work, we quantify a fundamental functional perception gap: the inability of current vision encoders to decode functional tracer biodistribution independent of morphological priors.<n>We introduce PET-Bench, the first large-scale functional imaging benchmark comprising 52,308 hierarchical QA pairs from 9,732 multi-site, multi-tracer PET studies.<n>Our results demonstrate that AVA effectively bridges the perception gap, transforming CoT from a source of hallucination into a robust inference tool and improving diagnostic
  arXiv  Detail & Related papers  (2026-01-06T05:58:50Z)
• Meta-information Guided Cross-domain Synergistic Diffusion Model for Low-dose PET Reconstruction [9.752203949076216]
  We introduce a meta-information guided cross-domain synergistic diffusion model (MiG-DM)<n>MiG-DM integrates comprehensive cross-modal priors to generate high-quality PET images.<n>Experiments on the UDPET public dataset and clinical datasets with varying dose levels demonstrate that MiG-DM outperforms state-of-the-art methods in enhancing PET image quality and preserving physiological details.
  arXiv  Detail & Related papers  (2025-12-23T13:02:18Z)
• FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising [55.04342933312839]
  We propose FoundDiff, a foundational diffusion model for unified and generalizable low-dose computed tomography (CT) denoising.<n>FoundDiff employs a two-stage strategy: (i) dose-anatomy perception and (ii) adaptive denoising.<n>First, we develop a dose- and anatomy-aware contrastive language image pre-training model (DA-CLIP) to achieve robust dose and anatomy perception.<n>Second, we design a dose- and anatomy-aware diffusion model (DA-Diff) to perform adaptive and generalizable denoising.
  arXiv  Detail & Related papers  (2025-08-24T11:03:56Z)
• Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks [40.986069119392944]
  We propose MX-ARM, a multimodal MiXture-of-experts Alignment Reconstruction and Model. It is modality detachable and exchangeable, allocating different multi-layer perceptrons dynamically ("mixture of experts") through learnable weights to learn respective representations from different modalities.
  arXiv  Detail & Related papers  (2024-03-29T08:47:49Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction [62.29541106695824]
  This paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM) By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process.
  arXiv  Detail & Related papers  (2023-08-20T04:10:36Z)
• Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System [69.40329819373954]
  The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. We propose a boundary guided semantic learning network (BSNet) in this paper.
  arXiv  Detail & Related papers  (2022-09-07T05:01:38Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)

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.