ARTInp: CBCT-to-CT Image Inpainting and Image Translation in Radiotherapy

• URL: http://arxiv.org/abs/2502.04898v1
• Date: Fri, 07 Feb 2025 13:04:25 GMT
• Title: ARTInp: CBCT-to-CT Image Inpainting and Image Translation in Radiotherapy
• Authors: Ricardo Coimbra Brioso, Leonardo Crespi, Andrea Seghetto, Damiano Dei, Nicola Lambri, Pietro Mancosu, Marta Scorsetti, Daniele Loiacono,
• Abstract summary: ARTInp is a novel deep-learning framework combining image inpainting and CBCT-to-CT translation. We trained ARTInp on a dataset of paired CBCT and CT images from the SynthRad 2023 challenge.
• Score: 1.70645147263353
• License:
• Abstract: A key step in Adaptive Radiation Therapy (ART) workflows is the evaluation of the patient's anatomy at treatment time to ensure the accuracy of the delivery. To this end, Cone Beam Computerized Tomography (CBCT) is widely used being cost-effective and easy to integrate into the treatment process. Nonetheless, CBCT images have lower resolution and more artifacts than CT scans, making them less reliable for precise treatment validation. Moreover, in complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), where full-body visualization of the patient is critical for accurate dose delivery, the CBCT images are often discontinuous, leaving gaps that could contain relevant anatomical information. To address these limitations, we propose ARTInp (Adaptive Radiation Therapy Inpainting), a novel deep-learning framework combining image inpainting and CBCT-to-CT translation. ARTInp employs a dual-network approach: a completion network that fills anatomical gaps in CBCT volumes and a custom Generative Adversarial Network (GAN) to generate high-quality synthetic CT (sCT) images. We trained ARTInp on a dataset of paired CBCT and CT images from the SynthRad 2023 challenge, and the performance achieved on a test set of 18 patients demonstrates its potential for enhancing CBCT-based workflows in radiotherapy.

Related papers

• Initial Study On Improving Segmentation By Combining Preoperative CT And Intraoperative CBCT Using Synthetic Data [0.21847754147782888]
  Cone-beam computed tomography (CBCT) can be used to facilitate computer-assisted interventions. The availability of high quality, preoperative scans offers potential for improvements. We propose a multimodal learning method that fuses roughly aligned CBCT and CT scans.
  arXiv  Detail & Related papers  (2024-12-03T09:08:38Z)
• Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings [6.157230849293829]
  Daily cone-beam CT (CBCT) imaging, pivotal for therapy adjustment, falls short in tissue density accuracy. We maximize CBCT data during therapy, complemented by sparse paired fan-beam CTs. Our approach shows promise in generating high-quality CT images from CBCT scans in RT.
  arXiv  Detail & Related papers  (2024-09-19T07:56:06Z)
• Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation [0.21847754147782888]
  Cone-beam computed tomography (CBCT) is an important tool facilitating computer aided interventions. While the degraded image quality can affect downstream segmentation, the availability of high quality, preoperative scans represents potential for improvements. We propose a multimodal learning method that fuses roughly aligned CBCT and CT scans and investigate the effect of CBCT quality and misalignment on the final segmentation performance.
  arXiv  Detail & Related papers  (2024-06-17T15:31:54Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• A multi-channel cycleGAN for CBCT to CT synthesis [0.0]
  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.
  arXiv  Detail & Related papers  (2023-12-04T16:40:53Z)
• 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)
• 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)
• 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)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)

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.