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Session

Imaging acquisition and processing
7006
Digital Poster
Physics
The dosimetric impact of deep learning-based organs at risk auto-segmentation
Hongbo Guo, China
PO-1651
Clinical evaluation of deep learning for auto-segmentation of CT images in RT for lung cancer
Noémie Johnston, Belgium
PO-1652
Measuring tissue thickness variation using Tomotherapy sinograms for H&N replanning
Marco Parisotto, Italy
PO-1653
Quality Assurance of C-Arm CBCT Angiography for Stereotactic Radiation Surgery
José Antonio Fermoso Gutiérrez, Spain
PO-1654
Tuning deep learning models for automatic segmentation of head and neck cancers in PET/CT images
Bao Ngoc Huynh, Norway
PO-1655
Quantitative evaluation of DWI sequence in a low T magnetic resonance guided radiotherapy system
Lorenzo Placidi, Italy
PO-1656
Generation of synthetic CT with 3D deep convolutional neural networks for brain MR-only radiotherapy
souha aouadi, Qatar
PO-1657
Inverse Consistency Error for quantifying uncertainty in DIR: validation on three different sites
Marco Fusella, Italy
PO-1658
Clinical validation of an automatic atlas-based segmentation tool for male pelvis CT images
Marta Casati, Italy
PO-1659
Investigating the generation of synthetic CT for abdominal tumors treated with particle therapy
Chiara Paganelli, Italy
PO-1660
Investigation into the impact of AI-enhanced CBCTs on CBCT-CT deformable image registration
Daniel Balfour, United Kingdom
PO-1661
Analysis of different transfer learning approaches when applying AI on small datasets
Jennifer Dhont, Belgium
PO-1662
Interactive deep-learning based tumor segmentation
Zixiang Wei, Denmark
PO-1663
Direct Density algorithm evaluation
Miguel Garcia Cutillas, Spain
PO-1664
Comparison of mid-position CT reconstruction systems
Mariana Pereira, Portugal
PO-1665
Streamlining the use of PET/MRI in an MR-only radiotherapy workflow
René Winter, Norway
PO-1666
Statistical limitations in particle imaging tomography
Charles-Antoine Collins-Fekete, United Kingdom
PO-1667
Impact of immobilization devices on planning target volume for pelvic radiotherapy patients
Priyanka Agarwal, India
PO-1668
Commissionning of iterative model reconstruction (IMR) on Philips BigBore CT Scanner
Gregory Bolard, Switzerland
PO-1669
Clinical implementation of MRI-only radiotherapy workflow for prostate cancer with a standard linac
Marco Felisi, Italy
PO-1670
Comparison between DIR of Eclipse and Velocity in PET/CT studies
Laura Cardoso Rubio, Spain
PO-1671
Characterization of the geometric distortion and the quality of MR images used for spine SBRT
Tarraf Torfeh, Qatar
PO-1672
Improving data collection for deep-learning auto-segmentation models
Donal McSweeney, United Kingdom
PO-1673
An MRI sequence independent Convolutional Neural Network for head sCT generation in proton therapy
Barbara Knäusl, Austria
PO-1674
Automated delineation for MR-only prostate radiotherapy using a 2.5D convolutional neural network
Christopher Holland, United Kingdom
PO-1675
Validating CBCT to CT registration QC using an AI generated dataset.
John Sage, United Kingdom
PO-1676
cGAN-based pseudo-CT generation for prostate MRI-only radiotherapy
Safaa Tahri, France
PO-1677
simulation of low-dose cone beam CT for paediatric image-guided proton beam therapy
Josh Lindsay, United Kingdom
PO-1678
Semiautomatic contouring of dominant intraprostatic lesions in prostate using diffusion weighted MRI
Valentina Giacometti, United Kingdom
PO-1679
Synthetic-CT generation from T1w brain MRIs with a cascaded GANs ensemble approach
Kumar Shreshtha, France
PO-1680
Effectiveness of a Cranial Distortion Correction Software Using A Novel Measurement Method
Thierry Gevaert, Belgium
PO-1681
Towards a clinical helium ion imaging system
Lennart Volz, Germany
PO-1682
Spatial Characterization of errors in pseudo-CT generation for MRI-only radiotherapy
Hilda Chourak, France
PO-1683
Correlations of tumour permeability parameters from DCE-MRI with ADC in nasopharyngeal carcinoma
Wing Lun Mui, Hong Kong (SAR) China
PO-1684
Application of radiomics feature captured from MRI for prediction of recurrence for glioma patients
Canyu Liu, China
PO-1685
A novel semi auto-segmentation method for head and neck adaptive radiotherapy
Yong Gan, The Netherlands
PO-1686
Evaluation of the CT eFOV image distortion and the effect on VMAT plan dosimetry
Styliani Savva, Cyprus
PO-1687
Automatic Detection of Circular Contour Errors Using Convolutional Neural Networks
Natsumi Futakami, Japan
PO-1688
Sample pairing (mixup) as a data augmentation technique for deep medical image segmentation networks
Lars Johannes Isaksson, Italy
PO-1689
Synthetic generation of pulmonary nodules using super resolution generative adversarial model
Zhixiang Wang, The Netherlands
PO-1690
A deeply supervised convolutional neural network ensemble for multilabel segmentation of pelvic OARs
Michael Lempart, Sweden
PO-1691
Principal curve-based and AI-based method for ultrasound prostate delineation
Tao Peng, Hong Kong (SAR) China
PO-1692
4D CT analysis of organs at risk (OARs) in stereotactic radiotherapy
Valerio Nardone, Italy
PO-1693
Target localization uncertainty in Gamma Knife SRS: Comparison of three frame-based workflows
Eleftherios Pappas, Greece
PO-1694
Accurate H&N 3D segmentation with limited training data using 2-stage CNNs
Edward Henderson, United Kingdom
PO-1695