scholarly journals Accuracy evaluation of surface registration algorithm using normal distribution transform in stereotactic body radiotherapy/radiosurgery: A phantom study

2022 ◽  
Author(s):  
Haenghwa Lee ◽  
Jeong‐Mee Park ◽  
Kwang Hyeon Kim ◽  
Dong‐Hoon Lee ◽  
Moon‐Jun Sohn
Keyword(s):  
Normal Distribution ◽  
Stereotactic Body Radiotherapy ◽  
Surface Registration ◽  
Phantom Study ◽  
Accuracy Evaluation ◽  
Registration Algorithm

PO-0865 A PHANTOM STUDY FOR THE VALIDATION OF AN IN-HOUSE DEFORMABLE REGISTRATION ALGORITHM FOR DOSE ACCUMULATION

2012 ◽  
Vol 103 ◽  
pp. S338-S339 ◽  
Author(s):  
V. Zambrano ◽  
D. Fabri ◽  
M. Stock ◽  
W. Birkfellner ◽  
D. Georg
Keyword(s):  
Phantom Study ◽  
Deformable Registration ◽  
Dose Accumulation ◽  
Registration Algorithm

Dosimetric impact of translational and rotational setup errors for spine stereotactic body radiotherapy: A phantom study

2018 ◽  
Vol 43 (4) ◽  
pp. 320-326 ◽  
Author(s):  
Makoto Sasaki ◽  
Mitsuhiro Nakamura ◽  
Nobutaka Mukumoto ◽  
Manabu Nakata ◽  
Masahiro Hiraoka
Keyword(s):  
Stereotactic Body Radiotherapy ◽  
Phantom Study ◽  
Setup Errors

scholarly journals A Novel Surface Registration Algorithm With Biomedical Modeling Applications

2007 ◽  
Vol 11 (4) ◽  
pp. 474-482 ◽  
Author(s):  
Heng Huang ◽  
Li Shen ◽  
Rong Zhang ◽  
Fillia Makedon ◽  
Andrew Saykin ◽  
...  
Keyword(s):  
Surface Registration ◽  
Registration Algorithm ◽  
Biomedical Modeling

Robust and fast 3-D scan registration using normal distributions transform with supervoxel segmentation

Robotica ◽  
2014 ◽  
Vol 34 (7) ◽  
pp. 1630-1658 ◽  
Author(s):  
Ji W. Kim ◽  
Beom H. Lee
Keyword(s):  
Normal Distribution ◽  
Euclidean Distance ◽  
Three Dimensional ◽  
Surface Structures ◽  
Regular Grid ◽  
Local Surface ◽  
Normal Distributions ◽  
Registration Algorithm ◽  
Scan Registration ◽  
Real World Datasets

SUMMARYThis paper presents what is termed as the supervoxel normal distributions transform (SV-NDT), a novel three-dimensional (3-D) registration algorithm which improves the performance of the three-dimensional normal distributions transform (3-D NDT) significantly. The 3-D NDT partitions a model scan using a 3-D regular grid. Generating normal distributions using the 3-D regular grid causes considerable information loss because the 3-D regular grid does not use any information pertaining to the local surface structures of the model scan. The best type of surface (the constituent unit of each scan) for modeling with one normal distribution is known to be the plane. The SV-NDT reduces the loss of information using a supervoxel-generating algorithm at the partitioning stage. In addition, it uses the information of the local surface structures from the data scan by replacing the Euclidean distance with a function that uses local geometries as well as the Euclidean distance when each point in the data scan is matched to the corresponding normal distribution. Experiments demonstrate that the use of the supervoxel-generating algorithm increases the modeling accuracy of the normal distributions and that the proposed 3-D registration algorithm outperforms the 3-D NDT and other widely used 3-D registration algorithms in terms of robustness and speed on both synthetic and real-world datasets. Additionally, the effect of changing the function to create correspondences is also verified.

Accuracy evaluation of the optical surface monitoring system on EDGE linear accelerator in a phantom study

2016 ◽  
Vol 41 (2) ◽  
pp. 173-179 ◽  
Author(s):  
Pietro Mancosu ◽  
Antonella Fogliata ◽  
Antonella Stravato ◽  
Stefano Tomatis ◽  
Luca Cozzi ◽  
...  
Keyword(s):  
Monitoring System ◽  
Linear Accelerator ◽  
Phantom Study ◽  
Accuracy Evaluation ◽  
Optical Surface ◽  
Surface Monitoring

scholarly journals The Iterative Closest Point Registration Algorithm Based on the Normal Distribution Transformation

2019 ◽  
Vol 147 ◽  
pp. 181-190 ◽  
Author(s):  
Xiuying Shi ◽  
Jianjun Peng ◽  
Jiping Li ◽  
Pitao Yan ◽  
Hangyu Gong
Keyword(s):  
Normal Distribution ◽  
Iterative Closest Point ◽  
Registration Algorithm ◽  
Distribution Transformation
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