Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization

• URL: http://arxiv.org/abs/2312.01689v2
• Date: Wed, 17 Jan 2024 01:29:23 GMT
• Title: Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization
• Authors: Heejun Shin, Taehee Kim, Jongho Lee, Se Young Chun, Seungryung Cho, Dongmyung Shin
• Abstract summary: Cone beam computed tomography (CBCT) is an emerging medical imaging technique to visualize the internal anatomical structures of patients. reducing the number of projections in a CBCT scan while preserving the quality of a reconstructed image is challenging. We propose a fast and accurate sparse-view CBCT reconstruction (FACT) method to provide better reconstruction quality and faster optimization speed.
• Score: 13.01191568245715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cone beam computed tomography (CBCT) is an emerging medical imaging technique to visualize the internal anatomical structures of patients. During a CBCT scan, several projection images of different angles or views are collectively utilized to reconstruct a tomographic image. However, reducing the number of projections in a CBCT scan while preserving the quality of a reconstructed image is challenging due to the nature of an ill-posed inverse problem. Recently, a neural attenuation field (NAF) method was proposed by adopting a neural radiance field algorithm as a new way for CBCT reconstruction, demonstrating fast and promising results using only 50 views. However, decreasing the number of projections is still preferable to reduce potential radiation exposure, and a faster reconstruction time is required considering a typical scan time. In this work, we propose a fast and accurate sparse-view CBCT reconstruction (FACT) method to provide better reconstruction quality and faster optimization speed in the minimal number of view acquisitions ($<$ 50 views). In the FACT method, we meta-trained a neural network and a hash-encoder using a few scans (= 15), and a new regularization technique is utilized to reconstruct the details of an anatomical structure. In conclusion, we have shown that the FACT method produced better, and faster reconstruction results over the other conventional algorithms based on CBCT scans of different body parts (chest, head, and abdomen) and CT vendors (Siemens, Phillips, and GE).

Related papers

• Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning [13.17680480211064]
  We propose a hybrid supervised-unsupervised learning framework for X-ray computed tomography (CT) image reconstruction. Each proposed trained block consists of a deterministic MBIR solver and a neural network. We demonstrate the efficacy of this learned hybrid model for low-dose CT image reconstruction with limited training data.
  arXiv  Detail & Related papers  (2023-11-19T20:23:59Z)
• 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 Field Learning for Sparse-View CBCT Reconstruction [61.48254686722434]
  Cone Beam Computed Tomography (CBCT) is the most widely used imaging method in dentistry. sparse-view CBCT reconstruction has become a main focus to reduce radiation dose. This paper proposes a novel attenuation field encoder-decoder framework by first encoding the volumetric feature from multi-view X-ray projections.
  arXiv  Detail & Related papers  (2023-03-26T14:38:42Z)
• SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields [71.84366290195487]
  We propose SNAF for sparse-view CBCT reconstruction by learning the neural attenuation fields. Our approach achieves superior performance in terms of high reconstruction quality (30+ PSNR) with only 20 input views.
  arXiv  Detail & Related papers  (2022-11-30T14:51:14Z)
• REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition [75.64791080418162]
  REGAS proposes a self-supervised method to synthesize the undersampled tomographic views and mitigate aliasing artifacts in reconstructed images. To address the large memory cost of deep neural networks on high resolution 4D data, REGAS introduces a novel Ray Path Transformation (RPT) that allows for distributed, differentiable forward projections.
  arXiv  Detail & Related papers  (2022-08-17T03:42:19Z)
• Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data [7.670270099306413]
  Recent works have shown significant promise for reconstructing MR images from sparsely sampled k-space data using deep learning. In this work, we propose a technique that rapidly estimates deep neural networks directly at reconstruction time by fitting them on small adaptively estimated neighborhoods of a training set. Our results demonstrate that our proposed locally-trained method produces higher-quality reconstructions compared to models trained globally on larger datasets as well as other scan-adaptive methods.
  arXiv  Detail & Related papers  (2022-06-01T21:37:03Z)
• Is Deep Image Prior in Need of a Good Education? [57.3399060347311]
  Deep image prior was introduced as an effective prior for image reconstruction. Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques. We develop a two-stage learning paradigm to address the computational challenge.
  arXiv  Detail & Related papers  (2021-11-23T15:08:26Z)
• The Application of Convolutional Neural Networks for Tomographic Reconstruction of Hyperspectral Images [0.0]
  A novel method, utilizing convolutional neural networks (CNNs), is proposed to reconstruct hyperspectral cubes from computed imaging spectrometer (CTIS) images. CNNs deliver higher precision and shorter reconstruction time than a standard expectation algorithm.
  arXiv  Detail & Related papers  (2021-08-30T18:11:08Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks [0.0]
  We introduce the Neural Network Feldkamp-Davis-Kress (NN-FDK) algorithm. It adds a machine learning component to the FDK algorithm to improve its reconstruction accuracy while maintaining its computational efficiency. We show that the training time of an NN-FDK network is orders of magnitude lower than the considered deep neural networks, with only a slight reduction in reconstruction accuracy.
  arXiv  Detail & Related papers  (2020-10-01T14:10:23Z)

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.