Results 41 to 50 of about 1,906,510 (288)

Advances in generating sCT images from brain MR images based on deep learning

生物医学转化
Radiotherapy is a crucial modality in the treatment of malignant tumors. Precision radiotherapy planning typically requires the integration of computed tomography (CT) and magnetic resonance imaging (MRI).
Yang Zhihui, Li Hao, Li Fengsen
doaj   +1 more source

Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT

Frontiers in Oncology, 2023
ObjectiveTo develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR)
Na Li, Na Li, Na Li, Xuanru Zhou, Xuanru Zhou, Shupeng Chen, Jingjing Dai, Tangsheng Wang, Chulong Zhang, Wenfeng He, Yaoqin Xie, Xiaokun Liang   +11 more
doaj   +1 more source

A note on feature selection for polyp detection in CT colonography [PDF]

, 2006
In this paper we describe a computer aided detection (CAD) algorithm for robust detection of polyps in computed tomography (CT) colonography. The devised algorithm identifies suspicious polyp candidate surfaces using the surface normal intersection ...
Chowdhury, Tarik A., Miranda, Abhilash A., Abhilash Mir, Ovidiu Ghita, P.F. Whelan, Paul F. Whelan, A. Miranda, Ghita, Ovidiu, O. Ghita, Tarik A. Chowdhury, T.A. Chowdhury, Whelan, Paul F.   +11 more
core   +2 more sources

Synthetic contrast enhancement in cardiac CT with Deep Learning

CoRR, 2018
In Europe the 20% of the CT scans cover the thoracic region. The acquired images contain information about the cardiovascular system that often remains latent due to the lack of contrast in the cardiac area. On the other hand, the contrast enhanced computed tomography (CECT) represents an imaging technique that allows to easily assess the cardiac ...
Gianmarco Santini, Lorena M. Zumbo, Nicola Martini, Gabriele Valvano, Andrea Leo, Andrea Ripoli, Francesco Avogliero, Dante Chiappino, Daniele Della Latta   +8 more
openaire   +3 more sources

Synthetic COVID-19

, 2020
Synthetic COVID-19 dataset, including 1186 chest CT images These data come from ‘Deep Learning for COVID-19 chest CT (computed tomography) image analysis’ The deep learning model used in the paper is CycleGAN, and the classification experiment is used to
jiang, H
core   +2 more sources

CT synthesis from MRI with an improved multi-scale learning network

Frontiers in Physics, 2023
Introduction: Using MRI to synthesize CT and substitute its function in radiation therapy has drawn wide research interests. Currently, deep learning models have become the first choice for MRI—CT synthesis because of their ability to study complex non ...
Yan Li, Sisi Xu, Yao Lu, Zhenyu Qi
doaj   +1 more source

Evaluating synthetic computed tomography images for adaptive radiotherapy decision making in head and neck cancer

Physics and Imaging in Radiation Oncology, 2023
Background and purpose: Adaptive radiotherapy (ART) decision-making benefits from dosimetric information to supplement image inspection when assessing the significance of anatomical changes.
Caitlin Allen, Adam U. Yeo, Nicholas Hardcastle, Rick D. Franich   +3 more
doaj   +1 more source

Synthetic CT in Musculoskeletal Disorders

, 2023
AbstractRepeated computed tomography (CT) examinations increase patients' ionizing radiation exposure and health costs, making an alternative method desirable. Cortical and trabecular bone, however, have short T2 relaxation times, causing low signal intensity on conventional magnetic resonance (MR) sequences.
Lombardi, Alecio F, Ma, Ya-Jun, Jang, Hyungseok, Jerban, Saeed, Du, Jiang, Chang, Eric Y, Chung, Christine B   +6 more
openaire   +2 more sources

Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge [PDF]

, 2020
PurposeTo develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.Methods and materialsTwenty-five head-and-neck patients received magnetic resonance (MR)
Minsong Cao, Sheng, Ke, Dan Ruan, Cao, Minsong, Santhanam, Anand, Ke Sheng, McKenzie, Elizabeth M, Daniel O'Connor, O'Connor, Daniel, Anand Santhanam, Elizabeth M. McKenzie, Ruan, Dan   +11 more
core   +1 more source

Prospecting the protein design landscape

FEBS Letters, EarlyView.
This review outlines the current state of various protein design approaches. We discuss the current possibilities enabled by recently released tools, highlight future avenues to pursue in protein design, and underscore the crucial role of key databases and resources for successful protein design workflows.
Jakob R. Riccabona, Katharina T. Stonig, Jens Meiler, Clara T. Schoeder, Monica L. Fernández‐Quintero   +4 more
wiley   +1 more source