Equivariant Symmetry-Aware Head Pose Estimation for Fetal MRI

• URL: http://arxiv.org/abs/2512.04890v2
• Date: Mon, 08 Dec 2025 22:59:53 GMT
• Title: Equivariant Symmetry-Aware Head Pose Estimation for Fetal MRI
• Authors: Ramya Muthukrishnan, Borjan Gagoski, Aryn Lee, P. Ellen Grant, Elfar Adalsteinsson, Polina Golland, Benjamin Billot,
• Abstract summary: We present E(3)-Pose, a novel fast pose estimation method.<n>We aim to enable automatic adaptive prescription of 2D diagnostic MRI slices with 6-DoF head pose estimation.<n>Our experiments on publicly available and representative clinical fetal MRI datasets demonstrate the superior robustness and generalization of our method.
• Score: 5.003133209582619
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present E(3)-Pose, a novel fast pose estimation method that jointly and explicitly models rotation equivariance and object symmetry. Our work is motivated by the challenging problem of accounting for fetal head motion during a diagnostic MRI scan. We aim to enable automatic adaptive prescription of 2D diagnostic MRI slices with 6-DoF head pose estimation, supported by 3D MRI volumes rapidly acquired before each 2D slice. Existing methods struggle to generalize to clinical volumes, due to pose ambiguities induced by inherent anatomical symmetries, as well as low resolution, noise, and artifacts. In contrast, E(3)-Pose captures anatomical symmetries and rigid pose equivariance by construction, and yields robust estimates of the fetal head pose. Our experiments on publicly available and representative clinical fetal MRI datasets demonstrate the superior robustness and generalization of our method across domains. Crucially, E(3)-Pose achieves state-of-the-art accuracy on clinical MRI volumes, paving the way for clinical translation. Our implementation is available at github.com/ramyamut/E3-Pose.

Related papers

• 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)
• Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray [6.716465799201301]
  FemoroAcetabular Impingement (FAI) screening relies on angles traditionally measured on X-rays.<n> assessing the height and span of the impingement area requires also a 3D view through an MRI scan.<n>In this work, we conduct a matched-cohort validation study (89 patients, paired MRI/X-ray) using standard heatmap regression architectures to assess cross-modality clinical equivalence.
  arXiv  Detail & Related papers  (2026-01-26T15:04:21Z)
• Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning [0.15749416770494706]
  Self-supervised deep learning has accelerated 2D natural image analysis but remains difficult to translate into 3D MRI.<n>We present a emph-sequence-invariant self-supervised framework leveraging quantitative MRI (qMRI)<n> Experiments on healthy brain segmentation (IXI), stroke lesion segmentation (ARC), and MRI denoising show significant gains over baseline SSL approaches.
  arXiv  Detail & Related papers  (2025-01-21T11:27:54Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data [1.980639720136382]
  Traditional MRI scans often yield anisotropic data due to technical constraints.<n>Super-resolution techniques aim to address these limitations by reconstructing isotropic high-resolution images from anisotropic data.<n>We introduce SIMPLE,'' a Simultaneous Multi-Plane Self-Supervised Learning approach for isotropic MRI restoration from anisotropic data.
  arXiv  Detail & Related papers  (2024-08-23T13:48:11Z)
• Extraction of 3D trajectories of mandibular condyles from 2D real-time MRI [2.1001649486621137]
  Real-time MRI enables the extraction of condylar trajectories with sufficient accuracy for evaluating clinically relevant parameters. The segmentation of the sagittal slices required some fine-tuning. The difference in the superior-inferior coordinate of the condyles in the closed jaw position was 1.7 mm on average.
  arXiv  Detail & Related papers  (2024-06-21T07:35:40Z)
• X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models [9.97747208739885]
  X-Diffusion is a novel cross-sectional diffusion model that reconstructs detailed 3D MRI volumes from extremely sparse spatial-domain inputs.<n>A key aspect of X-Diffusion is that it models MRI data as holistic 3D volumes during the cross-sectional training and inference.<n>Our results demonstrate that X-Diffusion not only surpasses state-of-the-art methods in quantitative accuracy (PSNR) on unseen data but also preserves critical anatomical features.
  arXiv  Detail & Related papers  (2024-04-30T14:53:07Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• On the Localization of Ultrasound Image Slices within Point Distribution Models [84.27083443424408]
  Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US) Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. We present a framework for automated US image slice localization within a 3D shape representation.
  arXiv  Detail & Related papers  (2023-09-01T10:10:46Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Enhanced 3D Myocardial Strain Estimation from Multi-View 2D CMR Imaging [0.0]
  We propose an enhanced 3D myocardial strain estimation procedure, which combines complementary displacement information from multiple orientations of a single imaging modality (untagged CMR SSFP images) We register the sets of short-axis, four-chamber and two-chamber views via a 2D non-rigid registration algorithm implemented in a commercial software (Segment, Medviso) We then create a series of interpolating functions for the three directions of motion and use them to deform a tetrahedral mesh representation of a patient-specific left ventricle.
  arXiv  Detail & Related papers  (2020-09-25T22:47:50Z)
• Cephalometric Landmark Regression with Convolutional Neural Networks on 3D Computed Tomography Data [68.8204255655161]
  Cephalometric analysis performed on lateral radiographs doesn't fully exploit the structure of 3D objects due to projection onto the lateral plane. We present a series of experiments with state of the art 3D convolutional neural network (CNN) based methods for keypoint regression. For the first time, we extensively evaluate the described methods and demonstrate their effectiveness in the estimation of Frankfort Horizontal and cephalometric points locations.
  arXiv  Detail & Related papers  (2020-07-20T12:45:38Z)

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.