mlsanityData to 7 Oct 2026

TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIs

arXiv 2606.25545 ↗First posted 24 Jun 2026cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

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Abstract (arXiv)

Reconstructing diffusion tensors from sparse DWIs is critical for accelerating Diffusion Tensor Imaging (DTI) in clinical settings, yet current deep learning approaches frequently yield anatomically inconsistent or physically implausible tensors. We introduce TensorLDM, a component-wise latent diffusion model that processes the six tensor components through two group-specific encoders (for diagonal and off-diagonal elements) while maintaining anatomical consistency via shared DWI conditioning. TensorLDM uses an Anatomy-Conditioned Autoencoder that encourages the latent to focus on tensor properties rather than re-encoding structural information. A shared Cross-Component Attention (CCA) mechanism, applied in both autoencoder refinement and diffusion fine-tuning, models inter-component dependencies, while a Mixture-of-Experts (MoE) DWI conditioner provides component-adaptive conditioning. On the Human Connectome Project (HCP) dataset under a single-shell, four-volume sparse acquisition, TensorLDM produces the most accurate downstream tractography and tensors with near-ground-truth physical validity (SPD-violation rate 1.54% vs. 1.40%), with the best or comparable voxel-wise reconstruction accuracy. Geodesic tensor error measured by the Log-Euclidean Metric (LEM) corroborates these gains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 24 of its 53 references that are in the map. First posted on arXiv 24 Jun 2026.

Builds on

24 of 53 references.

Closest ancestors · 4

Same direction, newest first

  1. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generation17 Dec
  2. 2024Diff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model24 May
  3. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models6 Feb
  4. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 20

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2020Denoising Diffusion Implicit Modelscited by 9,655
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2017Neural Discrete Representation Learningcited by 4,650
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  9. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  10. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  11. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  12. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
8 more
  1. 2021Scaling Vision with Sparse Mixture of Expertscited by 740
  2. 2024Deep Compression Autoencoder for Efficient High-Resolution Diffusion Modelscited by 209
  3. 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
  4. 2017Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolutioncited by 29
  5. 2020SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learningcited by 14
  6. 2024Diffusion MRI with Machine Learningcited by 11
  7. 2025Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinementcited by 6
  8. 2023FlexDTI: Flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learningcited by 2

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  3. 2024DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging
  4. 2023CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis
  5. 20253D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging
  6. 2026Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis
  7. 2025Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks
  8. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning