A multi-channel cycleGAN for CBCT to CT synthesis
- URL: http://arxiv.org/abs/2312.02017v1
- Date: Mon, 4 Dec 2023 16:40:53 GMT
- Title: A multi-channel cycleGAN for CBCT to CT synthesis
- Authors: Chelsea A. H. Sargeant, Edward G. A. Henderson, D\'onal M. McSweeney,
Aaron G. Rankin, Denis Page
- Abstract summary: Image synthesis is used to generate synthetic CTs from on-treatment cone-beam CTs (CBCTs)
Our contribution focuses on the second task, CBCT-to-sCT synthesis.
By leveraging a multi-channel input to emphasize specific image features, our approach effectively addresses some of the challenges inherent in CBCT imaging.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Image synthesis is used to generate synthetic CTs (sCTs) from on-treatment
cone-beam CTs (CBCTs) with a view to improving image quality and enabling
accurate dose computation to facilitate a CBCT-based adaptive radiotherapy
workflow. As this area of research gains momentum, developments in sCT
generation methods are difficult to compare due to the lack of large public
datasets and sizeable variation in training procedures. To compare and assess
the latest advancements in sCT generation, the SynthRAD2023 challenge provides
a public dataset and evaluation framework for both MR and CBCT to sCT
synthesis. Our contribution focuses on the second task, CBCT-to-sCT synthesis.
By leveraging a multi-channel input to emphasize specific image features, our
approach effectively addresses some of the challenges inherent in CBCT imaging,
whilst restoring the contrast necessary for accurate visualisation of patients'
anatomy. Additionally, we introduce an auxiliary fusion network to further
enhance the fidelity of generated sCT images.
Related papers
- Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT
(CBCT) Enhancement with Multi-task Customized Perceptual Loss [9.59233136691378]
Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy.
Recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts.
We propose a novel feature-oriented deep learning framework that translates low-quality CBCT images into high-quality CT-like imaging.
arXiv Detail & Related papers (2023-11-01T10:09:01Z) - Energy-Guided Diffusion Model for CBCT-to-CT Synthesis [8.888473799320593]
Cone Beam CT (CBCT) plays a crucial role in Adaptive Radiation Therapy (ART) by accurately providing radiation treatment when organ anatomy changes occur.
CBCT images suffer from scatter noise and artifacts, making relying solely on CBCT for precise dose calculation and accurate tissue localization challenging.
We propose an energy-guided diffusion model (EGDiff) and conduct experiments on a chest tumor dataset to generate synthetic CT (sCT) from CBCT.
arXiv Detail & Related papers (2023-08-07T07:23:43Z) - Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation [4.43162303545687]
Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging.
procuring appropriate training data for these Super-Resolution (SR) models is challenging.
Previous SR research has simulated thick-slice CT images from thin-slice CT images to create training pairs.
We introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms.
arXiv Detail & Related papers (2023-07-02T11:09:08Z) - Enhanced Sharp-GAN For Histopathology Image Synthesis [63.845552349914186]
Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection.
We propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization.
The proposed approach outperforms Sharp-GAN in all four image quality metrics on two datasets.
arXiv Detail & Related papers (2023-01-24T17:54:01Z) - InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal
Artifact Reduction in CT Images [53.4351366246531]
We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded.
We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
arXiv Detail & Related papers (2021-12-23T15:52:37Z) - Incremental Cross-view Mutual Distillation for Self-supervised Medical
CT Synthesis [88.39466012709205]
This paper builds a novel medical slice to increase the between-slice resolution.
Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy.
Our method outperforms state-of-the-art algorithms by clear margins.
arXiv Detail & Related papers (2021-12-20T03:38:37Z) - CT-SGAN: Computed Tomography Synthesis GAN [4.765541373485143]
We propose the CT-SGAN model that generates large-scale 3D synthetic CT-scan volumes when trained on a small dataset of chest CT-scans.
We show that CT-SGAN can significantly improve lung detection accuracy by pre-training a nodule on a vast amount of synthetic data.
arXiv Detail & Related papers (2021-10-14T22:20:40Z) - CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for
Non-Contrast to Contrast CT Translation [56.622832383316215]
We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans.
Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran.
Our empirical results show that CyTran outperforms all competing methods.
arXiv Detail & Related papers (2021-10-12T23:25:03Z) - Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation
Using Physics-Based Data Augmentation [4.3971310109651665]
In current clinical practice, noisy and artifact-ridden weekly cone-beam computed tomography (CBCT) images are only used for patient setup during radiotherapy.
Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures.
If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment and for deriving biomarkers for treatment response.
arXiv Detail & Related papers (2021-03-09T19:51:44Z) - COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for
Detection of COVID-19 Cases from Chest CT Images [75.74756992992147]
We introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images.
We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation.
arXiv Detail & Related papers (2020-09-08T15:49:55Z) - Pathological Retinal Region Segmentation From OCT Images Using Geometric
Relation Based Augmentation [84.7571086566595]
We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape.
The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
arXiv Detail & Related papers (2020-03-31T11:50:43Z)
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.