Now You Have My Healthy Attention: A U-DiT for Brain-MRI Inpainting

• URL: http://arxiv.org/abs/2607.27974v1
• Date: Thu, 30 Jul 2026 10:20:01 GMT
• Title: Now You Have My Healthy Attention: A U-DiT for Brain-MRI Inpainting
• Abstract summary: Inpainting task asks for anatomically plausible completion of healthy brain tissue within a masked region of a T1-weighted MRI.<n>We build a deterministic regression model and focus on giving it inductive biases tailored to inpainting.<n>We discuss the residual smoothness inherent to distortion-optimal regression and discuss its implications for anatomical realism.
• Score: 8.544231829756123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ASNR-MICCAI BraTS Local Synthesis (Inpainting) task asks for the anatomically plausible completion of healthy brain tissue within a masked region of a T1-weighted MRI, providing a tumor-free anatomical reference for downstream analysis. As the task is scored by distortion metrics (SSIM, PSNR, MSE), we build a deterministic regression model and focus on giving it inductive biases tailored to inpainting. Our network follows the U-DiT principle of performing self-attention on a downsampled token grid: a volumetric encoder-decoder imports long-range context through a downsampled global self-attention block with three-dimensional rotary position embeddings, while convolutions and skip connections preserve high-frequency detail. Two ideas drive our results. First, we constrain the attention so that occluded ("void") tokens attend only to known-healthy tokens of the same volume, with a learned bias toward each query's contralateral homologue, forcing the completion to be inferred from observed anatomy rather than from other unknown regions. Second, we add a contralateral-symmetry input that supplies the mirrored healthy hemisphere as a patient-specific prior; since the brain is approximately bilaterally symmetric and lesions are typically unilateral, this prior improves the distortion metrics at matched structural similarity. On the official BraTS-2026 validation leaderboard our submission reaches a mean healthy-region SSIM of $0.864$, PSNR of $24.7$\,dB and MSE of $4.6{\times}10^{-3}$ over $219$ cases. We further analyse the residual smoothness inherent to distortion-optimal regression and discuss its implications for anatomical realism.

Related papers

• ReMAP: Self-supervised learning to unveil brain representations and vulnerability [3.3233194140188687]
  General anesthesia offers a rare opportunity to observe the human brain under a standardized, controlled perturbation.<n>EEG is almost always reduced to a single proprietary depth index, collapsing a rich trajectory into one number.<n>Here we ask whether the geometry of that trajectory, not merely the depth it reaches, carries clinically meaningful information.
  arXiv  Detail & Related papers  (2026-08-22T17:09:49Z)
• PRISM-Net: Patient-specific reference-guided inter-breast symmetry matching for three-class breast DCE-MRI classification [4.086901280540239]
  We propose PRISM-Net, a registration-free bilateral framework that leverages contralateral breast features as patient-specific references for background-aware representation learning.<n> PRISM-Net integrates bilateral feature matching and asymmetry-aware attention to establish adaptive inter-breast correspondence and enhance representations of discriminative asymmetric patterns.
  arXiv  Detail & Related papers  (2026-07-29T11:38:59Z)
• Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution [28.92727853844715]
  Super-resolution reconstructs isotropic volumes from anisotropic clinical acquisitions by synthesizing intermediate slices at arbitrary scales.<n>Treating this ill-posed inverse problem as unconstrained residual-based regression risks hallucinating implausible structures.<n>This paper presents the Dual-Prior Null-space Learning framework, which reformulates the task as a constrained recovery process guided by two complementary priors.
  arXiv  Detail & Related papers  (2026-06-25T07:53:36Z)
• Brain-WM: Brain Glioblastoma World Model [56.45393372490125]
  We present Brain-WM, a pioneering GBM world model that unifies next-step treatment prediction and future MRI generation.<n>Brain-WM encodes latent dynamics into a shared latent space for joint autoregressive treatment prediction and flow-based future MRI generation.<n>Ultimately, Brain-WM offers a robust clinical sandbox for optimizing patient healthcare.
  arXiv  Detail & Related papers  (2026-03-08T09:54:08Z)
• Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT [68.09387763135236]
  We introduce a weakly supervised method trained solely on vertebra-level healthy/malignant labels, without any lesion masks.<n>We achieve strong blastic/lytic performance despite no mask supervision.
  arXiv  Detail & Related papers  (2025-12-07T14:03:28Z)
• Synthesizing Accurate and Realistic T1-weighted Contrast-Enhanced MR Images using Posterior-Mean Rectified Flow [3.024548464833694]
  Contrast-enhanced (CE) T1-weighted MRI is central to neuro-oncologic diagnosis but requires gadolinium-based agents.<n>We propose a two-stage Posterior-Mean Rectified Flow pipeline for synthesizing CE brain MRI from non-contrast inputs.
  arXiv  Detail & Related papers  (2025-08-18T05:55:57Z)
• Individualized Mapping of Aberrant Cortical Thickness via Stochastic Cortical Self-Reconstruction [5.7864523838262025]
  Cortical Self-Reconstruction (SCSR) generates highly individualized cortical reconstructions that can detect subtle thickness deviations.<n>SCSR achieved significantly lower reconstruction errors and identified atrophy patterns that enabled better disease discrimination than established methods.
  arXiv  Detail & Related papers  (2024-03-11T15:59:35Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification [48.4619620405991]
  Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior, we can explore a counterfactual problem that how would the features have behaved if there were no lesions in the image. We derive a new theoretical result for counterfactual generation based on the symmetric prior.
  arXiv  Detail & Related papers  (2020-09-30T03:15:30Z)
• Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging [39.5958976939981]
  We present a novel methodology to detect imperfect bilateral symmetry in CT of human anatomy. The structurally symmetric nature of the pelvic bone is explored and is used to provide interventional image augmentation for treatment of unilateral fractures in patients with traumatic injuries.
  arXiv  Detail & Related papers  (2020-03-04T19:08:55Z)

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.