MO-FF-A3-02: In-Vivo Correspondence Validation of a Deformable Image Registration Method

2009 ◽  
Vol 36 (6Part21) ◽  
pp. 2711-2711
Author(s):  
J Lian ◽  
M Foskey ◽  
S Wang ◽  
D Shen ◽  
S Chang
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Registration Method

scholarly journals LungRegNet: An unsupervised deformable image registration method for 4D‐CT lung

2020 ◽  
Vol 47 (4) ◽  
pp. 1763-1774 ◽  
Author(s):  
Yabo Fu ◽  
Yang Lei ◽  
Tonghe Wang ◽  
Kristin Higgins ◽  
Jeffrey D. Bradley ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Registration Method ◽  
4D Ct

Dose accumulation with deformable image registration method using helical tomotherapy images for prostate cancer

2019 ◽  
Vol 19 (1) ◽  
pp. 25-29
Author(s):  
Warit Thongsuk ◽  
Imjai Chitapanarux ◽  
Somsak Wanwilairat ◽  
Wannapha Nobnop
Keyword(s):  
Prostate Cancer ◽  
Computed Tomography ◽  
Image Registration ◽  
Cancer Patients ◽  
Helical Tomotherapy ◽  
Deformable Image Registration ◽  
High Dose ◽  
Registration Method ◽  
Dose Accumulation ◽  
Dose Volume

AbstractPurpose:To evaluate changes of accumulated doses from an initial plan in each fraction by deformable image registration (DIR) with daily megavoltage computed tomography (MVCT) images from helical tomotherapy for prostate cancer patients.Materials and methods:The MVCT images of five prostate cancer patients were acquired by using a helical tomotherapy unit before the daily treatment fraction began. All images data were exported to DIR procedures by MIM software, in which the planned kilovoltage computed tomography (kVCT) images were acting as the source images with the daily MVCT acquired as the target images for registration. The automatic deformed structure was used to access the volume variation and daily dose accumulation to each structure. All dose-volume parameters were compared to the initial planned dose.Results:The actual median doses of the planning target volume (PTV) received 70 Gy and 50.4 Gy were decreased at the end of the treatment with an average 1·0 ± 0·67% and 2·1 ± 1·54%, respectively. As regards organs at risk (OARs), the bladder and rectum dose-volume parameters tended to increase from the initial plan. The high-dose regions of the bladder and rectum, however, were decreased from the initial plan at the end of the treatment.Conclusions:The daily actual dose differs from the initial planned dose. The accumulated dose of target tends to be lower than the initial plan, but tends to be higher than the initial plan for the OARs. Therefore, inter-fractional anatomic changes should be considered by the DIR methods, which would be useful as clinically informative and beneficial for adaptive treatment strategies.

SU-GG-J-185: Towards in Vivo Dose Validation Using Real-Time Physiologically Constrained Deformable Image Registration

2008 ◽  
Vol 35 (6Part8) ◽  
pp. 2722-2722
Author(s):  
J Xia ◽  
P Muyan-Özçelik ◽  
J Owens ◽  
J Palta ◽  
S Samant
Keyword(s):  
Image Registration ◽  
Real Time ◽  
Deformable Image Registration

A deformable image registration method to handle distended rectums in prostate cancer radiotherapy

2006 ◽  
Vol 33 (9) ◽  
pp. 3304-3312 ◽  
Author(s):  
Song Gao ◽  
Lifei Zhang ◽  
He Wang ◽  
Renaud de Crevoisier ◽  
Deborah D. Kuban ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Cancer Radiotherapy ◽  
Registration Method ◽  
Prostate Cancer Radiotherapy

scholarly journals A GPU-based Implementation of Multimodal Deformable Image Registration Based on Mutual Information or Bhattacharyya Distance

2011 ◽  
Author(s):  
Yifei Lou ◽  
Xun Jia ◽  
Xuejun Gu ◽  
Allen Tannenbaum
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Input Data ◽  
Fluid Model ◽  
Source Code ◽  
Deformable Image Registration ◽  
Bhattacharyya Distance ◽  
Output Data ◽  
Registration Method ◽  
Viscous Fluid Model

This paper describes a multimodal deformable image registration method on the GPU. It is a CUDA-based implementation of a paper by E. D’Agostino et. al, ‘’A viscous fluid model for multimodal non-rigid image registration using mutual information’’. In addition, we incorporate an alternative metric as opposed to mutual information, called Bhattacharyya Distance, in the recent work of Lou and Tannenbaum. This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the algorithm.

scholarly journals Modeling lung deformation: A combined deformable image registration method with spatially varying Young's modulus estimates

2013 ◽  
Vol 40 (8) ◽  
pp. 081902 ◽  
Author(s):  
Min Li ◽  
Edward Castillo ◽  
Xiao-Lin Zheng ◽  
Hong-Yan Luo ◽  
Richard Castillo ◽  
...  
Keyword(s):  
Image Registration ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Deformable Image Registration ◽  
Registration Method ◽  
Lung Deformation ◽  
Spatially Varying
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