Results 251 to 260 of about 1,906,510 (288)

The impact of MRI-CT registration errors on deep learning-based synthetic CT generation

Medical Imaging 2019: Image Processing, 2019
Purpose To investigate the impact of image registration on deep learning-based synthetic CT (sCT) generation. Methods Paired MR images and CT scans of the pelvic region of radiotherapy patients were obtained and non-rigidly registered. After a manual verification of the registrations, the dataset was split into two groups containing either well ...
Mateusz C. Florkow, Frank Zijlstra, Linda G. W. Kerkmeijer, Matteo Maspero, Cornelis A. T. van den Berg, Marijn van Stralen, Peter R. Seevinck   +6 more
openaire   +2 more sources

Synthetic dual-energy CT reconstruction from single-energy CT Using artificial intelligence

Abdominal Radiology, 2023
To develop and assess the utility of synthetic dual-energy CT (sDECT) images generated from single-energy CT (SECT) using two state-of-the-art generative adversarial network (GAN) architectures for artificial intelligence-based image translation.In this retrospective study, 734 patients (389F; 62.8 years ± 14.9) who underwent enhanced DECT of the chest,
Jiwoong Jeong, Andrew Wentland, Domenico Mastrodicasa, Ghaneh Fananapazir, Adam Wang, Imon Banerjee, Bhavik N. Patel   +6 more
openaire   +2 more sources

Synthetic CT-based multi-organ segmentation in cone beam CT for adaptive pancreatic radiotherapy

Medical Imaging 2021: Image Processing, 2021
The inter-fraction motion management of pancreatic radiotherapy remains a challenge in current clinical practice. CBCT-based adaptive radiotherapy is an emerging technique for either offline or online plan adaptations. Accurately delineating tumor targets and organs-at-risk (OARs) is an important step in adaptive re-planning process; however, manual ...
Xianjin Dai, Yang Lei 0002, James Janopaul-Naylor, Tonghe Wang, Justin Roper, Jun Zhou 0025, Walter J. Curran, Tian Liu 0004, Pretesh Patel, Xiaofeng Yang 0005   +9 more
openaire   +2 more sources

Pulmonary Effects of Synthetic Marijuana: Chest Radiography and CT Findings

American Journal of Roentgenology, 2015
The purpose of this article is to present the first chest radiographic and CT descriptions of organizing pneumonia in response to smoking synthetic marijuana.Chest radiographs showed a diffuse miliary-micronodular pattern. Chest CT images showed diffuse centrilobular nodules and tree-in-bud pattern and a histopathologic pattern of organizing pneumonia ...
Eugene A, Berkowitz, Travis S, Henry, Srihari, Veeraraghavan, Gerald W, Staton, Anthony A, Gal   +4 more
openaire   +2 more sources

Evaluating the Impact of Training Loss on MR to Synthetic CT Conversion

2020
Radiation therapy is one of the most important strategies for treating patients with tumor. The rationale is to deliver high radiation doses to the tumor in order to damage its DNA while sparing, at the same time, healthy tissues. In order to optimize such a process, biomedical images play a fundamental role; in particular, Magnetic Resonance (MR ...
Moiz Khan Sherwani, Paolo Zaffino, Pierangela Bruno, Maria Francesca Spadea, Francesco Calimeri   +4 more
openaire   +1 more source

Synthetic CT Generation Using MRI with Deep Learning: How Does the Selection of Input Images Affect the Resulting Synthetic CT?

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Synthetic x-ray computed tomography (CT) images derived from magnetic resonance imaging (MRI) is a recent area of focus for medical imaging researchers for applications in attenuation correction in simultaneous PET/MRI systems and MRI-guided radiotherapy planning.
Andrew P. Leynes, Peder E. Z. Larson
openaire   +1 more source

mDixon-Based Synthetic CT Generation via Patch Learning

2020
We proposed a new method for generating synthetic CT on abdomen from modified Dixon (mDixon) MR data of abdomens to address the challenges of PET/MR attenuation correction (AC). AC is necessary in process of PET/MR but MR data lack photon attenuation, thus multiple methods are proposed to generate synthetic CT.
Xin Song, Jiamin Zheng, Chao Fan, Hongbin Yu   +3 more
openaire   +2 more sources

Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions

2020
MR-only radiation treatment planning is attractive due to the superior soft tissue definition of MRI as compared to CT, and the elimination of the uncertainty introduced by CT-MRI registration. To facilitate MR-only radiation therapy planning, synthetic-CT (sCT) algorithms (for electron density correction) are required for dose calculation. Deep neural
Matt Hemsley, Brige Chugh, Mark Ruschin, Young Lee, Chia-Lin Tseng, Greg J. Stanisz, Angus Lau   +6 more
openaire   +1 more source

Morphometric and clinical comparison of MRI-based synthetic CT to conventional CT of the hip in children

Pediatric Radiology
MRI-based synthetic CT (sCT) generates CT-like images from MRI data.To evaluate equivalence, inter- and intraobserver reliability, and image quality of sCT compared to conventional (cCT) for assessing hip morphology and maturity in pediatric patients.We prospectively enrolled patients 
Jade Iwasaka-Neder, M. Alejandra Bedoya, James Connors, Simon Warfield, Sarah D. Bixby   +4 more
openaire   +2 more sources

The synthetic localizer radiograph – A new CT scan planning method

Physica Medica, 2019
To investigate if the conventional localizer radiograph (LR) can be replaced by a synthetic LR (SLR), generated from a low-dose spiral CT scan, for CT scan planning with minimal changes to current clinical workflows.A dosimetric comparison of SLRs and LRs was made using Monte Carlo methods.
Patrik Nowik, Gavin Poludniowski, Anders Svensson, Robert Bujila, Fabian Morsbach, Torkel B. Brismar   +5 more
openaire   +2 more sources