ImplicitCell: Resolution Cell Modeling of Joint Implicit Volume Reconstruction and Pose Refinement in Freehand 3D Ultrasound
- URL: http://arxiv.org/abs/2503.06686v1
- Date: Sun, 09 Mar 2025 16:40:49 GMT
- Title: ImplicitCell: Resolution Cell Modeling of Joint Implicit Volume Reconstruction and Pose Refinement in Freehand 3D Ultrasound
- Authors: Sheng Song, Yiting Chen, Duo Xu, Songhan Ge, Yunqian Huang, Junni Shi, Man Chen, Hongbo Chen, Rui Zheng,
- Abstract summary: ImplicitCell is a novel framework that integrates Implicit Neural Representation (INR) with an ultrasound resolution cell model for joint optimization of volume reconstruction and pose refinement.<n> Experimental results demonstrate that ImplicitCell significantly reduces reconstruction artifacts and improves volume quality compared to existing methods.
- Score: 12.066225199232777
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Freehand 3D ultrasound enables volumetric imaging by tracking a conventional ultrasound probe during freehand scanning, offering enriched spatial information that improves clinical diagnosis. However, the quality of reconstructed volumes is often compromised by tracking system noise and irregular probe movements, leading to artifacts in the final reconstruction. To address these challenges, we propose ImplicitCell, a novel framework that integrates Implicit Neural Representation (INR) with an ultrasound resolution cell model for joint optimization of volume reconstruction and pose refinement. Three distinct datasets are used for comprehensive validation, including phantom, common carotid artery, and carotid atherosclerosis. Experimental results demonstrate that ImplicitCell significantly reduces reconstruction artifacts and improves volume quality compared to existing methods, particularly in challenging scenarios with noisy tracking data. These improvements enhance the clinical utility of freehand 3D ultrasound by providing more reliable and precise diagnostic information.
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) - NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild [11.047805165425256]
Current methods for performing 3D reconstruction and novel view synthesis (NVS) in ultrasound imaging data often face severe artifacts when training NeRF-based approaches.
In this work, we introduced NeRF-US, which incorporates 3D-geometry guidance for border probability and scattering density into NeRF training.
arXiv Detail & Related papers (2024-08-13T13:21:53Z) - UlRe-NeRF: 3D Ultrasound Imaging through Neural Rendering with Ultrasound Reflection Direction Parameterization [0.5837446811360741]
Traditional 3D ultrasound imaging methods have limitations such as fixed resolution, low storage efficiency, and insufficient contextual connectivity.
We propose a new model, UlRe-NeRF, which combines implicit neural networks and explicit ultrasound rendering architecture.
Experimental results demonstrate that the UlRe-NeRF model significantly enhances the realism and accuracy of high-fidelity ultrasound image reconstruction.
arXiv Detail & Related papers (2024-08-01T18:22:29Z) - CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images.
The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism.
We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
arXiv Detail & Related papers (2024-03-21T15:13:36Z) - FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos [79.50191812646125]
Reconstruction of endoscopic scenes is an important asset for various medical applications, from post-surgery analysis to educational training.
We adress the challenging setup of a moving endoscope within a highly dynamic environment of deforming tissue.
We propose an implicit scene separation into multiple overlapping 4D neural radiance fields (NeRFs) and a progressive optimization scheme jointly optimizing for reconstruction and camera poses from scratch.
This improves the ease-of-use and allows to scale reconstruction capabilities in time to process surgical videos of 5,000 frames and more; an improvement of more than ten times compared to the state of the art while being agnostic to external tracking information
arXiv Detail & Related papers (2024-03-18T19:13:02Z) - AiAReSeg: Catheter Detection and Segmentation in Interventional
Ultrasound using Transformers [75.20925220246689]
endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature.
This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
arXiv Detail & Related papers (2023-09-25T19:34:12Z) - Reslicing Ultrasound Images for Data Augmentation and Vessel
Reconstruction [22.336362581634706]
This paper introduces RESUS, a weak supervision data augmentation technique for ultrasound images based on slicing reconstructed 3D volumes from tracked 2D images.
We generate views which cannot be easily obtained in vivo due to physical constraints of ultrasound imaging, and use these augmented ultrasound images to train a semantic segmentation model.
We demonstrate that RESUS achieves statistically significant improvement over training with non-augmented images and highlight qualitative improvements through vessel reconstruction.
arXiv Detail & Related papers (2023-01-18T03:22:47Z) - OADAT: Experimental and Synthetic Clinical Optoacoustic Data for
Standardized Image Processing [62.993663757843464]
Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion.
OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues.
No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
arXiv Detail & Related papers (2022-06-17T08:11:26Z) - Deep Learning for Ultrasound Beamforming [120.12255978513912]
Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain.
Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing.
Deep learning methods can play a compelling role in the digital beamforming pipeline.
arXiv Detail & Related papers (2021-09-23T15:15:21Z) - Transducer Adaptive Ultrasound Volume Reconstruction [17.19369561039399]
3D volume reconstruction from freehand 2D scans is a very challenging problem, especially without the use of external tracking devices.
Recent deep learning based methods demonstrate the potential of directly estimating inter-frame motion between consecutive ultrasound frames.
We propose a novel domain adaptation strategy to adapt deep learning algorithms to data acquired with different transducers.
arXiv Detail & Related papers (2020-11-17T04:46:57Z) - Tattoo tomography: Freehand 3D photoacoustic image reconstruction with
an optical pattern [49.240017254888336]
Photoacoustic tomography (PAT) is a novel imaging technique that can resolve both morphological and functional tissue properties.
A current drawback is the limited field-of-view provided by the conventionally applied 2D probes.
We present a novel approach to 3D reconstruction of PAT data that does not require an external tracking system.
arXiv Detail & Related papers (2020-11-10T09:27:56Z)
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.