Correlation between internal fiducial tumor motion and external marker motion for liver tumors imaged with 4D-CT

2007 ◽  
Vol 67 (2) ◽  
pp. 630-638 ◽  
Author(s):  
A. Sam Beddar ◽  
Kristofer Kainz ◽  
Tina Marie Briere ◽  
Yoshikazu Tsunashima ◽  
Tinsu Pan ◽  
...  
Keyword(s):  
Liver Tumors ◽  
Tumor Motion ◽  
4D Ct

scholarly journals Estimating intrafraction tumor motion during fiducial-based liver stereotactic radiotherapy via an iterative closest point (ICP) algorithm

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Wu-zhou Li ◽  
Zhi-wen Liang ◽  
Yi Cao ◽  
Ting-ting Cao ◽  
Hong Quan ◽  
...  
Keyword(s):  
Stereotactic Radiotherapy ◽  
Imaging System ◽  
Liver Tumors ◽  
Three Dimensional ◽  
Rigid Motion ◽  
Iterative Closest Point ◽  
Tumor Motion ◽  
Tracking Accuracy ◽  
Icp Algorithm ◽  
Anterior Posterior

Abstract Background Tumor motion may compromise the accuracy of liver stereotactic radiotherapy. In order to carry out a precise planning, estimating liver tumor motion during radiotherapy has received a lot of attention. Previous approach may have difficult to deal with image data corrupted by noise. The iterative closest point (ICP) algorithm is widely used for estimating the rigid registration of three-dimensional point sets when these data were dense or corrupted. In the light of this, our study estimated the three-dimensional (3D) rigid motion of liver tumors during stereotactic liver radiotherapy using reconstructed 3D coordinates of fiducials based on the ICP algorithm. Methods Four hundred ninety-five pairs of orthogonal kilovoltage (KV) images from the CyberKnife stereo imaging system for 12 patients were used in this study. For each pair of images, the 3D coordinates of fiducial markers inside the liver were calculated via geometric derivations. The 3D coordinates were used to calculate the real-time translational and rotational motion of liver tumors around three axes via an ICP algorithm. The residual error was also investigated both with and without rotational correction. Results The translational shifts of liver tumors in left-right (LR), anterior-posterior (AP),and superior-inferior (SI) directions were 2.92 ± 1.98 mm, 5.54 ± 3.12 mm, and 16.22 ± 5.86 mm, respectively; the rotational angles in left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions were 3.95° ± 3.08°, 4.93° ± 2.90°, and 4.09° ± 1.99°, respectively. Rotational correction decreased 3D fiducial displacement from 1.19 ± 0.35 mm to 0.65 ± 0.24 mm (P<0.001). Conclusions The maximum translational movement occurred in the SI direction. Rotational correction decreased fiducial displacements and increased tumor tracking accuracy.

SU-GG-T-585: The Impact of Free Breathing versus Average 4D CT Image Data on External Beam Radiotherapy Planning for Liver Tumors

2010 ◽  
Vol 37 (6Part25) ◽  
pp. 3322-3322
Author(s):  
H Li ◽  
M Delclos ◽  
T Briere ◽  
S Beddar ◽  
P Das ◽  
...  
Keyword(s):  
Liver Tumors ◽  
External Beam Radiotherapy ◽  
Image Data ◽  
Free Breathing ◽  
Radiotherapy Planning ◽  
External Beam ◽  
Ct Image ◽  
4D Ct ◽  
The Impact

scholarly journals A Feasibility Study on Ribs as Anatomical Landmarks for Motion Tracking of Lung and Liver Tumors at External Beam Radiotherapy

2016 ◽  
Vol 16 (1) ◽  
pp. 99-111 ◽  
Author(s):  
Saber Nankali ◽  
Ahmad Esmaili Torshabi ◽  
Payam Samadi Miandoab
Keyword(s):  
Motion Tracking ◽  
Liver Tumors ◽  
External Beam Radiotherapy ◽  
External Beam ◽  
Anatomical Landmarks ◽  
Tumor Motion ◽  
Cage Structure ◽  
Position Information ◽  
Rib Cage ◽  
Using Data

At external beam radiotherapy for some tumors located at thorax region due to lack of information in gray scale fluoroscopic images tumor position determination is problematic. One of the clinical strategies is to implant clip as internal fiducial marker inside or near tumor to represent tumor position while the contrast of implanted clip is highly observable rather than tumor. As alternative, using natural anatomical landmarks located at thorax region of patient body is proposed to extract tumor position information without implanting clips that is invasive method with possible side effect. Among natural landmarks, ribs of rib-cage structure that result proper visualization at X-ray images may be optimal as representative for tumor motion. In this study, we investigated the existence of possible correlation between ribs as natural anatomical landmarks and various lung and liver tumors located at different sites as challenging issue. A simulation study was performed using data extracted from 4-dimensional extended cardiac-torso anthropomorphic phantom that is able to simulate motion effect of dynamic organs, as well. Several tumor sites with predefined distances originated from chosen ribs at anterior–posterior direction were simulated at 3 upper, middle, and lower parts of chest. Correlation coefficient between ribs and tumors was calculated to investigate the robustness of ribs as anatomical landmarks for tumor motion tracking. Moreover, a consistent correlation model was taken into account to track tumor motion with a rib as best candidate among selected ribs. Final results represent availability of using rib cage as anatomical landmark to track lung and liver tumors in a noninvasive way. Observations of our calculations showed a proper correlation between tumors and ribs while the degree of this correlation is changing depends on tumor site while lung tumors are more varied and complex with less correlation with ribs motion against liver tumors.

Lung tumor motion measured using retrospective 4D CT and correlated with tumor location and attachment

2004 ◽  
Vol 60 (1) ◽  
pp. S288-S289 ◽  
Author(s):  
E.D. Brandner ◽  
A. Wu ◽  
H. Chen ◽  
D. Heron ◽  
S. Kalnicki ◽  
...  
Keyword(s):  
Lung Tumor ◽  
Tumor Location ◽  
Tumor Motion ◽  
4D Ct

scholarly journals DATA PROCESSING USING ARTIFICIAL NEURAL NETWORKS TO IMPROVE THE SIMULATION OF LUNG MOTION

2012 ◽  
Vol 24 (06) ◽  
pp. 563-571
Author(s):  
R. Laurent ◽  
M. Salomon ◽  
J. Henriet ◽  
M. Sauget ◽  
R. Gschwind ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Augmentation ◽  
Original Data ◽  
Tumor Motion ◽  
4D Ct ◽  
Normal Tissues ◽  
Lung Motion ◽  
Artificial Neural

To optimize the delivery in lung radiation therapy, a better understanding of the tumor motion is required, on one hand, to have a better tumor-targeting efficiency, and on the other hand to avoid as much as possible normal tissues. The four-dimensional computed tomography (4D-CT) allows to quantify tumor motion, but due to artifacts, it introduces biases and errors in tumor localization. Despite this disadvantage, we propose a method to simulate lung motion based on data provided by the 4D-CT for several patients. To reduce uncertainties introduced by the 4D-CT scan, we conveniently treated data using artificial neural networks. More precisely, our approach consists of a data augmentation technique. The data resulting from this processing step are then used to build a training set for another artificial neural network that learns the lung motion. To improve the learning accuracy, we have studied the number of phases required to precisely describe the displacement of each point. Thus, from 1118 points scattered across five patients and defined over 8 or 10 phases, we obtained 5800 points from 50 phases. After training, the network is used to compute the positions of 40 points from five other patients on 10 phases. These points allow to quantify the prediction performance. In comparison with the original data, the ones issued from our treatment process provide a significant increase of the prediction accuracy: an average improvement of 16% can be observed. The motion computed for several points by the neural network that has learnt the lung one exhibits an hysteresis near the one given by the 4D-CT, with an error smaller than 1 mm in the cranio-caudal axis.

scholarly journals Assessment of tumor motion reproducibility with audio-visual coaching through successive 4D CT sessions

2014 ◽  
Vol 15 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Samuel Goossens ◽  
Frédéric Senny ◽  
John Aldo Lee ◽  
Guillaume Janssens ◽  
Xavier Geets
Keyword(s):  
Tumor Motion ◽  
4D Ct
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