Results 31 to 40 of about 37,523 (330)
Development of a Deformable Lung Phantom for the Evaluation of Deformable Registration
Journal of Applied Clinical Medical Physics, 2009 A deformable lung phantom was developed to simulate patient breathing motion and to evaluate a deformable image registration algorithm. The phantom consisted of an acryl cylinder filled with water and a latex balloon located in the inner space of the cylinder. A silicon membrane was attached to the inferior end of the phantom.
Chang, Jina +2 more
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Noncoplanar Radiation using Tomotherapy: A Phantom Study
Technology in Cancer Research & Treatment, 2020 Background: There are very few studies on noncoplanar radiation in tomotherapy because deformable image registration is not implemented in the TomoTherapy Planning Station, a treatment planning device used in tomotherapy.
Masahiro Yuasa BS +1 more
doaj +1 more source
Registration of brain tumor images using hyper-elastic regularization [PDF]
, 2012 In this paper, we present a method to estimate a deformation field between two instances of a brain volume having tumor. The novelties include the assessment of the disease progress by observing the healthy tissue deformation and usage of the Neo ...
Hamamci, Andac +3 more
core +1 more source
Recurrent Registration Neural Networks for Deformable Image Registration
CoRR, 2019 Parametric spatial transformation models have been successfully applied to image registration tasks. In such models, the transformation of interest is parameterized by a fixed set of basis functions as for example B-splines. Each basis function is located on a fixed regular grid position among the image domain, because the transformation of interest is
Robin Sandkühler +5 more
openaire +3 more sources
Deformable image registration in radiation therapy [PDF]
Journal of Medical Radiation Sciences, 2020 Deformable image registration is an increasingly important method to account for soft tissue deformation between image acquisitions. This editorial discusses the clinical need and current status of deformable image registration.
Jason A Dowling, Laura M O’Connor
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A comparative evaluation of 3 different free-form deformable image registration and contour propagation methods for head and neck MRI : the case of parotid changes radiotherapy [PDF]
, 2017 Purpose: To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approachesthe commercial MIM, the open-source ...
Belli, Maria Luisa +10 more
core +1 more source
Random Walks for Deformable Image Registration [PDF]
, 2011 We introduce a novel discrete optimization method for non-rigid image registration based on the random walker algorithm. We discretize the space of deformations and formulate registration using a Gaussian MRF where continuous labels correspond to the probability of a point having a certain discrete deformation.
Dana Cobzas, Abhishek Sen
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On voxel-by-voxel accumulated dose for prostate radiation therapy using deformable image registration. [PDF]
, 2015 Since delivered dose is rarely the same with planned, we calculated the delivered total dose to ten prostate radiotherapy patients treated with rectal balloons using deformable dose accumulation (DDA) and compared it with the planned dose.
Bender, Edward T. +5 more
core +2 more sources
F3RNet: full-resolution residual registration network for deformable image registration [PDF]
International Journal of Computer Assisted Radiology and Surgery, 2021 Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream "high-to-low, low-to-high" network structure, and can achieve satisfactory overall registration results. However, accurate
Zhe Xu 0012 +4 more
openaire +3 more sources
Deform-GAN:An Unsupervised Learning Model for Deformable Registration
CoRR, 2020 Deformable registration is one of the most challenging task in the field of medical image analysis, especially for the alignment between different sequences and modalities. In this paper, a non-rigid registration method is proposed for 3D medical images leveraging unsupervised learning.
Xiaoyue Zhang +3 more
openaire +2 more sources