3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
- URL: http://arxiv.org/abs/2410.00404v1
- Date: Tue, 1 Oct 2024 05:00:47 GMT
- Title: 3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
- Authors: Xueming Fu, Yingtai Li, Fenghe Tang, Jun Li, Mingyue Zhao, Gao-Jun Teng, S. Kevin Zhou,
- Abstract summary: Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation.
Traditional reconstruction techniques often require many projections, while reconstruction from sparse-view X-ray projections is a potential way of reducing radiation dose.
We propose 3DGR-CAR, a 3D Gaussian Representation for Coronary Artery Reconstruction from ultra-sparse X-ray projections.
- Score: 13.829610843207746
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional reconstruction techniques often require many projections, while reconstruction from sparse-view X-ray projections is a potential way of reducing radiation dose. However, the extreme sparsity of coronary arteries in a 3D volume and ultra-limited number of projections pose significant challenges for efficient and accurate 3D reconstruction. To this end, we propose 3DGR-CAR, a 3D Gaussian Representation for Coronary Artery Reconstruction from ultra-sparse X-ray projections. We leverage 3D Gaussian representation to avoid the inefficiency caused by the extreme sparsity of coronary artery data and propose a Gaussian center predictor to overcome the noisy Gaussian initialization from ultra-sparse view projections. The proposed scheme enables fast and accurate 3D coronary artery reconstruction with only 2 views. Experimental results on two datasets indicate that the proposed approach significantly outperforms other methods in terms of voxel accuracy and visual quality of coronary arteries. The code will be available in https://github.com/windrise/3DGR-CAR.
Related papers
- NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections by Neural Implicit Representation [2.1771042711033997]
2D x-ray invasive coronary angiography remains as the most widely adopted imaging modality for CVDs diagnosis.
Due to the radiation limit, in general only two angiographic projections are acquired, providing limited information of the vessel geometry.
We propose a self-supervised deep learning method called NeCA, which is based on implicit neural representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer.
arXiv Detail & Related papers (2024-09-06T20:08:21Z) - Deep Learning-based 3D Coronary Tree Reconstruction from Two 2D Non-simultaneous X-ray Angiography Projections [1.9929038355503754]
Cardiovascular diseases (CVDs) are the most common cause of death worldwide.
Invasive x-ray coronary angiography (ICA) is one of the most important imaging modalities for the diagnosis of CVDs.
ICA typically acquires only two 2D projections, which makes the 3D geometry of coronary vessels difficult to interpret.
We propose a novel deep learning pipeline to correct the non-rigid cardiac and respiratory motions between non-simultaneous projections.
arXiv Detail & Related papers (2024-07-19T18:18:17Z) - Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction [9.848266253196307]
Cone-Beam Computed Tomography (CBCT) is an indispensable technique in medical imaging, yet the associated radiation exposure raises concerns in clinical practice.
We propose a novel reconstruction framework, namely DIF-Gaussian, which leverages 3D Gaussians to represent the feature distribution in the 3D space.
We evaluate DIF-Gaussian on two public datasets, showing significantly superior reconstruction performance than previous state-of-the-art methods.
arXiv Detail & Related papers (2024-07-01T08:48:04Z) - R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction [53.19869886963333]
3D Gaussian splatting (3DGS) has shown promising results in rendering image and surface reconstruction.
This paper introduces R2$-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction.
arXiv Detail & Related papers (2024-05-31T08:39:02Z) - Deep learning network to correct axial and coronal eye motion in 3D OCT
retinal imaging [65.47834983591957]
We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan.
The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
arXiv Detail & Related papers (2023-05-27T03:55:19Z) - 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) - Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models [52.529394863331326]
We propose a novel approach using two perpendicular pre-trained 2D diffusion models to solve the 3D inverse problem.
Our method is highly effective for 3D medical image reconstruction tasks, including MRI Z-axis super-resolution, compressed sensing MRI, and sparse-view CT.
arXiv Detail & Related papers (2023-03-15T08:28:06Z) - 3D Coronary Vessel Reconstruction from Bi-Plane Angiography using Graph
Convolutional Networks [1.8244763402770727]
3DAngioNet is a novel deep learning (DL) system that enables rapid 3D vessel mesh reconstruction using 2D XCA images from two views.
Our approach learns a coarse mesh template using an EfficientB3-UNet segmentation network and projection geometries, and deforms it using a graph convolutional network.
arXiv Detail & Related papers (2023-02-28T17:46:25Z) - XraySyn: Realistic View Synthesis From a Single Radiograph Through CT
Priors [118.27130593216096]
A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane.
To the best of our knowledge, this is the first work on radiograph view synthesis.
We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without groundtruth bone labels.
arXiv Detail & Related papers (2020-12-04T05:08:53Z) - 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) - Weakly-supervised 3D coronary artery reconstruction from two-view
angiographic images [4.722039838364292]
We propose an adversarial and generative way to reconstruct three dimensional coronary artery models.
With 3D fully supervised learning and 2D weakly supervised learning schemes, we obtained reconstruction accuracies that outperform state-of-art techniques.
arXiv Detail & Related papers (2020-03-26T11:41:38Z)
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.