mlsanityData to 7 Oct 2026

Field-of-View Extension for Brain Diffusion MRI via Deep Generative Models

arXiv 2405.03652 ↗First posted 6 May 2024cs.CVcited by 4

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Abstract (arXiv)

Purpose: In diffusion MRI (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field-of-view (FOV). This work aims to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with complete FOV can improve the whole-brain tractography for corrupted data with incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data. Approach: We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWI) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWI outside of incomplete FOV. Results: For evaluating the imputed slices, on the WRAP dataset the proposed framework achieved PSNRb0=22.397, SSIMb0=0.905, PSNRb1300=22.479, SSIMb1300=0.893; on the NACC dataset it achieved PSNRb0=21.304, SSIMb0=0.892, PSNRb1300=21.599, SSIMb1300= 0.877. The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts (p < 0.001) on both the WRAP and NACC datasets. Conclusions: Results suggest that the proposed framework achieved sufficient imputation performance in dMRI data with incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's Disease.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 60 references that are in the map. First posted on arXiv 6 May 2024.

Builds on

10 of 60 references.

References · 10

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  3. 2021In&Out : Diverse Image Outpainting via GAN Inversioncited by 55
  4. 2018TractSeg - Fast and accurate white matter tract segmentationcited by 50
  5. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Modelscited by 50
  6. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learningcited by 28
  7. 2024Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approachcited by 18
  8. 2021SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRIcited by 16
  9. 2020Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registrationcited by 7
  10. 2022TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imagingcited by 3

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imagingcited by 1
  2. 2024Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRIcited by 1
  3. 2025Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potentialcited by 1
  4. 2025TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of Viewcited by 1