MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction

• URL: http://arxiv.org/abs/2506.15835v1
• Date: Mon, 16 Jun 2025 04:57:34 GMT
• Title: MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction
• Authors: Mingyuan Luo, Xin Yang, Zhongnuo Yan, Yan Cao, Yuanji Zhang, Xindi Hu, Jin Wang, Haoxuan Ding, Wei Han, Litao Sun, Dong Ni,
• Abstract summary: We propose an enhanced motion network (MoNetV2) to enhance the accuracy and generalizability of reconstruction under diverse scanning velocities and tactics.<n>MoNetV2 surpasses existing methods in both reconstruction quality and generalizability performance across three large datasets.
• Score: 11.531888235029445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Three-dimensional (3D) ultrasound (US) aims to provide sonographers with the spatial relationships of anatomical structures, playing a crucial role in clinical diagnosis. Recently, deep-learning-based freehand 3D US has made significant advancements. It reconstructs volumes by estimating transformations between images without external tracking. However, image-only reconstruction poses difficulties in reducing cumulative drift and further improving reconstruction accuracy, particularly in scenarios involving complex motion trajectories. In this context, we propose an enhanced motion network (MoNetV2) to enhance the accuracy and generalizability of reconstruction under diverse scanning velocities and tactics. First, we propose a sensor-based temporal and multi-branch structure that fuses image and motion information from a velocity perspective to improve image-only reconstruction accuracy. Second, we devise an online multi-level consistency constraint that exploits the inherent consistency of scans to handle various scanning velocities and tactics. This constraint exploits both scan-level velocity consistency, path-level appearance consistency, and patch-level motion consistency to supervise inter-frame transformation estimation. Third, we distill an online multi-modal self-supervised strategy that leverages the correlation between network estimation and motion information to further reduce cumulative errors. Extensive experiments clearly demonstrate that MoNetV2 surpasses existing methods in both reconstruction quality and generalizability performance across three large datasets.

Related papers

• Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using Deep Learning [3.8426872518410997]
  This study introduces a motion-based learning network with a global-local self-attention module (MoGLo-Net) to enhance 3D reconstruction in handheld photoacoustic and ultrasound (PAUS) imaging.<n>MoGLo-Net exploits the critical regions, such as fully-developed speckle area or high-echogenic tissue area within successive ultrasound images to accurately estimate motion parameters.
  arXiv  Detail & Related papers  (2025-02-05T11:59:23Z)
• Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass [68.78222900840132]
  We propose Fast 3D Reconstruction (Fast3R), a novel multi-view generalization to DUSt3R that achieves efficient and scalable 3D reconstruction by processing many views in parallel.<n>Fast3R demonstrates state-of-the-art performance, with significant improvements in inference speed and reduced error accumulation.
  arXiv  Detail & Related papers  (2025-01-23T18:59:55Z)
• T-3DGS: Removing Transient Objects for 3D Scene Reconstruction [83.05271859398779]
  Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions.<n>We propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting.
  arXiv  Detail & Related papers  (2024-11-29T07:45:24Z)
• MVSBoost: An Efficient Point Cloud-based 3D Reconstruction [4.282795945742752]
  Efficient and accurate 3D reconstruction is crucial for various applications, including augmented and virtual reality, medical imaging, and cinematic special effects. Traditional Multi-View Stereo (MVS) systems have been fundamental in these applications, but implicit 3D scene modeling has introduced new possibilities for handling complex topologies and continuous surfaces.
  arXiv  Detail & Related papers  (2024-06-19T13:02:17Z)
• CMAR-Net: Accurate Cross-Modal 3D SAR Reconstruction of Vehicle Targets with Sparse-Aspect Multi-Baseline Data [6.046251485192556]
  Sparse-aspect multi-baseline Synthetic Aperture Radar (SAR) 3D tomography is a crucial remote sensing technique.<n>Deep learning (DL) revolutionizes this field through its powerful data-driven representation capabilities and efficient inference characteristics.<n>We propose a Cross-Modal 3D-SAR Reconstruction Network (CMAR-Net) that enhances 3D SAR imaging by fusing heterogeneous information.
  arXiv  Detail & Related papers  (2024-06-06T15:18:59Z)
• 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning [79.60829508459753]
  Current commercial Digital Subtraction Angiography (DSA) systems typically demand hundreds of scanning views to perform reconstruction. The dynamic blood flow and insufficient input of sparse-view DSA images present significant challenges to the 3D vessel reconstruction task. We propose to use a time-agnostic vessel probability field to solve this problem effectively.
  arXiv  Detail & Related papers  (2024-05-17T11:23:33Z)
• Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction [89.53963284958037]
  We propose a novel motion-aware enhancement framework for dynamic scene reconstruction. Specifically, we first establish a correspondence between 3D Gaussian movements and pixel-level flow. For the prevalent deformation-based paradigm that presents a harder optimization problem, a transient-aware deformation auxiliary module is proposed.
  arXiv  Detail & Related papers  (2024-03-18T03:46:26Z)
• Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems [7.074380879971194]
  We propose a novel two-and-a-half order score-based model (TOSM) for 3D volumetric reconstruction. During the training phase, our TOSM learns data distributions in 2D space, which reduces the complexity of training. In the reconstruction phase, the TOSM updates the data distribution in 3D space, utilizing complementary scores along three directions.
  arXiv  Detail & Related papers  (2023-08-16T17:07:40Z)
• Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction [95.5735805072852]
  We propose a novel density-variational learning framework to improve the robustness of the image dehzing model. Specifically, the dehazing network is optimized under the consistency-regularized framework. Our method significantly surpasses the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-03-29T08:11:04Z)
• Stable Optimization for Large Vision Model Based Deep Image Prior in Cone-Beam CT Reconstruction [6.558735319783205]
  Large Vision Model (LVM) has recently demonstrated great potential for medical imaging tasks. Deep Image Prior (DIP) effectively guides an untrained neural network to generate high-quality CBCT images without any training data. We propose a stable optimization method for the forward-model-free DIP model for sparse-view CBCT.
  arXiv  Detail & Related papers  (2022-03-23T15:16:29Z)
• Neural Computed Tomography [1.7188280334580197]
  Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions. We propose a novel reconstruction framework, NeuralCT, to generate time-resolved images free from motion artifacts.
  arXiv  Detail & Related papers  (2022-01-17T18:50:58Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)

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.