RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans

• URL: http://arxiv.org/abs/2404.10766v1
• Date: Tue, 16 Apr 2024 17:50:09 GMT
• Title: RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans
• Authors: Mark C. Eid, Pak-Hei Yeung, Madeleine K. Wyburd, João F. Henriques, Ana I. L. Namburete,
• Abstract summary: We propose RapidVol: a neural representation framework to speed up slice-to-volume ultrasound reconstruction. A set of 2D ultrasound scans, with their ground truth (or estimated) 3D position and orientation (pose) is all that is required to form a complete 3D reconstruction. When compared to prior approaches, our method is over 3x quicker, 46% more accurate, and if given inaccurate poses is more robust.
• Score: 12.837508334426529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Two-dimensional (2D) freehand ultrasonography is one of the most commonly used medical imaging modalities, particularly in obstetrics and gynaecology. However, it only captures 2D cross-sectional views of inherently 3D anatomies, losing valuable contextual information. As an alternative to requiring costly and complex 3D ultrasound scanners, 3D volumes can be constructed from 2D scans using machine learning. However this usually requires long computational time. Here, we propose RapidVol: a neural representation framework to speed up slice-to-volume ultrasound reconstruction. We use tensor-rank decomposition, to decompose the typical 3D volume into sets of tri-planes, and store those instead, as well as a small neural network. A set of 2D ultrasound scans, with their ground truth (or estimated) 3D position and orientation (pose) is all that is required to form a complete 3D reconstruction. Reconstructions are formed from real fetal brain scans, and then evaluated by requesting novel cross-sectional views. When compared to prior approaches based on fully implicit representation (e.g. neural radiance fields), our method is over 3x quicker, 46% more accurate, and if given inaccurate poses is more robust. Further speed-up is also possible by reconstructing from a structural prior rather than from scratch.

Related papers

• NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields [57.617972778377215]
  We show how to generate effective 3D representations from posed RGB images. We pretrain this representation at scale on our proposed curated posed-RGB data, totaling over 1.8 million images. Our novel self-supervised pretraining for NeRFs, NeRF-MAE, scales remarkably well and improves performance on various challenging 3D tasks.
  arXiv  Detail & Related papers  (2024-04-01T17:59:55Z)
• Denoising Diffusion via Image-Based Rendering [54.20828696348574]
  We introduce the first diffusion model able to perform fast, detailed reconstruction and generation of real-world 3D scenes. First, we introduce a new neural scene representation, IB-planes, that can efficiently and accurately represent large 3D scenes. Second, we propose a denoising-diffusion framework to learn a prior over this novel 3D scene representation, using only 2D images.
  arXiv  Detail & Related papers  (2024-02-05T19:00:45Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction [53.93674177236367]
  Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging. Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image. This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses. We introduce a novel geometry-aware encoder-decoder framework to solve this problem.
  arXiv  Detail & Related papers  (2023-03-26T14:38:42Z)
• Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images [18.997300579859978]
  We propose AdLocUI, a framework that Adaptively Localizes 2D Ultrasound Images in the 3D anatomical atlas. We first train a convolutional neural network with 2D slices sampled from co-aligned 3D ultrasound volumes to predict their locations. We fine-tune it with 2D freehand ultrasound images using a novel unsupervised cycle consistency.
  arXiv  Detail & Related papers  (2022-09-12T17:59:41Z)
• Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis [0.0]
  We present a simple yet effective 2D method to handle 3D data while efficiently embedding the 3D knowledge during training. Our method generates a super-resolution image by stitching slices side by side in the 3D image. While attaining equal, if not superior, results to 3D networks utilizing only 2D counterparts, the model complexity is reduced by around threefold.
  arXiv  Detail & Related papers  (2022-05-05T09:59:03Z)
• ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation [13.71137201718831]
  The objective of this work is to achieve sensorless reconstruction of a 3D volume from a set of 2D freehand ultrasound images with deep implicit representation. In contrast to the conventional way that represents a 3D volume as a discrete voxel grid, we do so by parameterizing it as the zero level-set of a continuous function. Our proposed model, as ImplicitVol, takes a set of 2D scans and their estimated locations in 3D as input, jointly re?fing the estimated 3D locations and learning a full reconstruction of the 3D volume.
  arXiv  Detail & Related papers  (2021-09-24T17:59:18Z)
• FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [78.00922683083776]
  It is non-trivial to make a general adapted 2D detector work in this 3D task. In this technical report, we study this problem with a practice built on fully convolutional single-stage detector. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020.
  arXiv  Detail & Related papers  (2021-04-22T09:35:35Z)
• 3D-to-2D Distillation for Indoor Scene Parsing [78.36781565047656]
  We present a new approach that enables us to leverage 3D features extracted from large-scale 3D data repository to enhance 2D features extracted from RGB images. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data.
  arXiv  Detail & Related papers  (2021-04-06T02:22:24Z)
• 3D Probabilistic Segmentation and Volumetry from 2D projection images [10.32519161805588]
  X-Ray imaging is quick, cheap and useful for front-line care assessment and intra-operative real-time imaging. It suffers from projective information loss and lacks vital information on which many essential diagnostic biomarkers are based. In this paper we explore probabilistic methods to reconstruct 3D volumetric images from 2D imaging modalities.
  arXiv  Detail & Related papers  (2020-06-23T08:00:51Z)

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.