Application of the gamma evaluation method in Gamma Knife film dosimetry

2011 ◽  
Vol 38 (10) ◽  
pp. 5778-5787 ◽  
Author(s):  
Jeong-Hoon Park ◽  
Jung Ho Han ◽  
Chae-Yong Kim ◽  
Chang Wan Oh ◽  
Do-Heui Lee ◽  
...  
2004 ◽  
Vol 31 (5) ◽  
pp. 1243-1248 ◽  
Author(s):  
M. Yamauchi ◽  
T. Tominaga ◽  
O. Nakamura ◽  
R. Ueda ◽  
M. Hoshi

2019 ◽  
Vol 20 (7) ◽  
pp. 193-200 ◽  
Author(s):  
Liting Yu ◽  
Tanya Kairn ◽  
Jamie Trapp ◽  
Scott B. Crowe

2009 ◽  
Vol 14 (5) ◽  
pp. 162-168 ◽  
Author(s):  
Janusz WINIECKI ◽  
Tomasz MORGAŚ ◽  
Karolina MAJEWSKA ◽  
Barbara DRZEWIECKA

2009 ◽  
Vol 36 (5) ◽  
pp. 1768-1774 ◽  
Author(s):  
Josef Novotny ◽  
Jagdish P. Bhatnagar ◽  
Mubina A. Quader ◽  
Greg Bednarz ◽  
L. Dade Lunsford ◽  
...  

2021 ◽  
Vol 10 ◽  
Author(s):  
Daisuke Kawahara ◽  
Xueyan Tang ◽  
Chung K. Lee ◽  
Yasushi Nagata ◽  
Yoichi Watanabe

PurposeThe current study proposed a model to predict the response of brain metastases (BMs) treated by Gamma knife radiosurgery (GKRS) using a machine learning (ML) method with radiomics features. The model can be used as a decision tool by clinicians for the most desirable treatment outcome.Methods and MaterialUsing MR image data taken by a FLASH (3D fast, low-angle shot) scanning protocol with gadolinium (Gd) contrast-enhanced T1-weighting, the local response (LR) of 157 metastatic brain tumors was categorized into two groups (Group I: responder and Group II: non-responder). We performed a radiomics analysis of those tumors, resulting in more than 700 features. To build a machine learning model, first, we used the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of radiomics features to the minimum number of features useful for the prediction. Then, a prediction model was constructed by using a neural network (NN) classifier with 10 hidden layers and rectified linear unit activation. The training model was evaluated with five-fold cross-validation. For the final evaluation, the NN model was applied to a set of data not used for model creation. The accuracy and sensitivity and the area under the receiver operating characteristic curve (AUC) of the prediction model of LR were analyzed. The performance of the ML model was compared with a visual evaluation method, for which the LR of tumors was predicted by examining the image enhancement pattern of the tumor on MR images.ResultsBy the LASSO analysis of the training data, we found seven radiomics features useful for the classification. The accuracy and sensitivity of the visual evaluation method were 44 and 54%. On the other hand, the accuracy and sensitivity of the proposed NN model were 78 and 87%, and the AUC was 0.87.ConclusionsThe proposed NN model using the radiomics features can help physicians to gain a more realistic expectation of the treatment outcome than the traditional method.


2015 ◽  
Vol 67 (10) ◽  
pp. 1859-1867 ◽  
Author(s):  
Kyeong-Hyeon Kim ◽  
Dong-Su Kim ◽  
Tae-Ho Kim ◽  
Seong-Hee Kang ◽  
Min-Seok Cho ◽  
...  

2008 ◽  
Vol 35 (6Part16) ◽  
pp. 2828-2828 ◽  
Author(s):  
J Novotny ◽  
J Bhatnagar ◽  
M Quader ◽  
M Huq

2018 ◽  
Vol 18 (1) ◽  
pp. 82-87 ◽  
Author(s):  
Atefeh Mahmoudi ◽  
Alireza Shirazi ◽  
Ghazale Geraily ◽  
Tahereh Hadisi nia ◽  
Masoume Bakhshi ◽  
...  

AbstractBackgroundOne of the stereotactic radiosurgery techniques is Gamma Knife radiosurgery, in which intracranial lesions that are inaccessible or inappropriate for surgery are treated using 201 cobalt-60 sources in one treatment session. In this conformal technique, the penumbra width, which results in out-of-field dose in tumour-adjacent normal tissues should be determined accurately. The aim of this study is to calculate the penumbra widths of single and 201 beams for different collimator sizes of Gamma Knife machine model 4C using EGSnrc/BEAMnrc Monte Carlo simulation code and comparison the results with EBT3 film dosimetry data.Methods and materialsIn this study, simulation of Gamma Knife machine model 4C was performed based on the Monte Carlo codes of EGSnrc/BEAMnrc. To investigate the physical penumbra width (80−20%), the single beam and 201 beams profiles were obtained using EGSnrc/DOSXYZnrc code and EBT3 films located at isocentre point in a spherical Plexiglas head phantom.ResultsBased on the results, the single beam penumbra widths obtained from simulation data for 4, 8, 14 and 18 mm collimator sizes alongXaxis were 0·75, 0·77, 0·90 and 0·92 mm, respectively. The data for 201 beams obtained from simulation were 2·61, 4·80, 7·92 and 9·81 mm alongXaxis and 1·31, 1·60, 1·91 and 2·14 mm alongZaxis and from film dosimetry were 3·21, 4·90, 8·00 and 10·61 mm alongXaxis and 1·22, 1·69, 2·01 and 2·25 mm alongZaxis, respectively.ConclusionThe differences between measured and simulated penumbra widths are in an acceptable range. However, for more precise measurement in the penumbra region in which dose gradient is high, Monte Carlo simulation is recommended.


Author(s):  
T. Oikawa ◽  
H. Kosugi ◽  
F. Hosokawa ◽  
D. Shindo ◽  
M. Kersker

Evaluation of the resolution of the Imaging Plate (IP) has been attempted by some methods. An evaluation method for IP resolution, which is not influenced by hard X-rays at higher accelerating voltages, was proposed previously by the present authors. This method, however, requires truoblesome experimental preperations partly because specially synthesized hematite was used as a specimen, and partly because a special shape of the specimen was used as a standard image. In this paper, a convenient evaluation method which is not infuenced by the specimen shape and image direction, is newly proposed. In this method, phase contrast images of thin amorphous film are used.Several diffraction rings are obtained by the Fourier transformation of a phase contrast image of thin amorphous film, taken at a large under focus. The rings show the spatial-frequency spectrum corresponding to the phase contrast transfer function (PCTF). The envelope function is obtained by connecting the peak intensities of the rings. The evelope function is offten used for evaluation of the instrument, because the function shows the performance of the electron microscope (EM).


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