NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction
- URL: http://arxiv.org/abs/2209.14540v1
- Date: Thu, 29 Sep 2022 04:06:00 GMT
- Title: NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction
- Authors: Ruyi Zha, Yanhao Zhang, Hongdong Li
- Abstract summary: This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction.
The desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network.
A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details.
- Score: 79.13750275141139
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper proposes a novel and fast self-supervised solution for sparse-view
CBCT reconstruction (Cone Beam Computed Tomography) that requires no external
training data. Specifically, the desired attenuation coefficients are
represented as a continuous function of 3D spatial coordinates, parameterized
by a fully-connected deep neural network. We synthesize projections discretely
and train the network by minimizing the error between real and synthesized
projections. A learning-based encoder entailing hash coding is adopted to help
the network capture high-frequency details. This encoder outperforms the
commonly used frequency-domain encoder in terms of having higher performance
and efficiency, because it exploits the smoothness and sparsity of human
organs. Experiments have been conducted on both human organ and phantom
datasets. The proposed method achieves state-of-the-art accuracy and spends
reasonably short computation time.
Related papers
- Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models.
We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
arXiv Detail & Related papers (2024-01-11T19:07:58Z) - SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World [19.696976370895907]
We propose SpikingNeRF, which aligns the temporal dimension of spiking neural networks (SNNs) with the radiance rays.
The computation turns into a spike-based, multiplication-free manner, reducing energy consumption and making high-quality 3D rendering accessible to neuromorphic hardware.
arXiv Detail & Related papers (2023-09-20T01:04:57Z) - Masked Wavelet Representation for Compact Neural Radiance Fields [5.279919461008267]
Using a multi-layer perceptron to represent a 3D scene or object requires enormous computational resources and time.
We present a method to reduce the size without compromising the advantages of having additional data structures.
With our proposed mask and compression pipeline, we achieved state-of-the-art performance within a memory budget of 2 MB.
arXiv Detail & Related papers (2022-12-18T11:43:32Z) - Variable Bitrate Neural Fields [75.24672452527795]
We present a dictionary method for compressing feature grids, reducing their memory consumption by up to 100x.
We formulate the dictionary optimization as a vector-quantized auto-decoder problem which lets us learn end-to-end discrete neural representations in a space where no direct supervision is available.
arXiv Detail & Related papers (2022-06-15T17:58:34Z) - Parameter estimation for WMTI-Watson model of white matter using
encoder-decoder recurrent neural network [0.0]
In this study, we evaluate the performance of NLLS, the RNN-based method and a multilayer perceptron (MLP) on datasets rat and human brain.
We showed that the proposed RNN-based fitting approach had the advantage of highly reduced computation time over NLLS.
arXiv Detail & Related papers (2022-03-01T16:33:15Z) - Learning Wave Propagation with Attention-Based Convolutional Recurrent
Autoencoder Net [0.0]
We present an end-to-end attention-based convolutional recurrent autoencoder (AB-CRAN) network for data-driven modeling of wave propagation phenomena.
We employ a denoising-based convolutional autoencoder from the full-order snapshots given by time-dependent hyperbolic partial differential equations for wave propagation.
The attention-based sequence-to-sequence network increases the time-horizon of prediction by five times compared to the plain RNN-LSTM.
arXiv Detail & Related papers (2022-01-17T20:51:59Z) - TCTN: A 3D-Temporal Convolutional Transformer Network for Spatiotemporal
Predictive Learning [1.952097552284465]
We propose an algorithm named 3D-temporal convolutional transformer (TCTN), where a transformer-based encoder with temporal convolutional layers is employed to capture short-term and long-term dependencies.
Our proposed algorithm can be easy to implement and trained much faster compared with RNN-based methods thanks to the parallel mechanism of Transformer.
arXiv Detail & Related papers (2021-12-02T10:05:01Z) - Dynamic Neural Representational Decoders for High-Resolution Semantic
Segmentation [98.05643473345474]
We propose a novel decoder, termed dynamic neural representational decoder (NRD)
As each location on the encoder's output corresponds to a local patch of the semantic labels, in this work, we represent these local patches of labels with compact neural networks.
This neural representation enables our decoder to leverage the smoothness prior in the semantic label space, and thus makes our decoder more efficient.
arXiv Detail & Related papers (2021-07-30T04:50:56Z) - SignalNet: A Low Resolution Sinusoid Decomposition and Estimation
Network [79.04274563889548]
We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples.
We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions.
In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
arXiv Detail & Related papers (2021-06-10T04:21:20Z) - Deep Cellular Recurrent Network for Efficient Analysis of Time-Series
Data with Spatial Information [52.635997570873194]
This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information.
The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
arXiv Detail & Related papers (2021-01-12T20:08:18Z)
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.