scholarly journals Pengukuran Nilai CT Number Pada Phantom CIRS 062m Sebagai Data Input Kalkulasi Dosis Program ISIS 3d Di Treatment Planning System

2019 ◽  
Vol 17 (2) ◽  
pp. 70
Author(s):  
Purwantiningsi Purwantiningsi ◽  
Hadi Lesmana
Keyword(s):  
Ct Scan ◽  
Treatment Planning ◽  
Region Of Interest ◽  
Planning System ◽  
Treatment Planning System ◽  
Data Input ◽  
Dose Volume Histogram ◽  
Ct Number ◽  
Dose Volume

Telah dilakukan pengukuran nilai CT number pada pesawat CT Scan Simulator<br />General Elektrik di RSPAD Gatot Soebroto Jakarta dengan menggunakan phanthom CIRS<br />062 dengan berbagai variasi nilai jaringan ( lung inhale, lung exhale, adipose, breast, water,<br />muscle, liver, bone 200, bone 800 ), dilakukan scanning phantom dengan ketebalan slices<br />10 mm, kV 100, mAs 190. Pengukuran CT number dilakukan dengan memberi tanda<br />lingkaran pada setiap objek jaringan dengan diameter lingkaran Region Of Interest (ROI)<br />yang sama, maka didapatkan hasil CT number ( Lung inhale ; -800.9, Lung exhale ; -492.1,<br />Adipose ; -66.8, Breast ; -30.2, Water ; -7.7, Muscle ; 47.5, Liver ; 56.2, Bone 200 ; 255.3,<br />bone 800 ; 929 ). Hasil nilai pengukuran CT number jaringan di input kedalam program iSis<br />3D yang terdapat di TPS (Treatment Planning Sistem). Hasil kalkulasi dosis dilakukan<br />menggunakan sampel planning pada organ paru pada program iSis 3D dengan<br />menggunakan energi elektron 8 MeV dan didapatkan hasil perbedaan sebelum dan sesudah<br />dimasukan nilai densitas elektron dalam kurva DVH (dose volume histogram). hasil yang<br />didapat dari bacaan kurva DVH antara sebelum dan sesudah dimasukan nilai densitas<br />menyatakan bahwa selisih perbedaan pada daerah jaringan paru sebelah kanan akumulasi<br />dosis rata-rata (Dmean) 3.6 % pada volume 2080 cm3, pada daerah jaringan paru sebelah<br />kiri dosis rata-rata (Dmean) 2.4% pada volume 1271 cm3, dan pada organ jantung dosis<br />rata-rata (Dmean) 1,36% pada volume 199,4 cm3.

scholarly journals Application of dose-volume histogram prediction in biologically related models for nasopharyngeal carcinomas treatment planning

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Wufei Cao ◽  
Yongdong Zhuang ◽  
Lixin Chen ◽  
Xiaowei Liu
Keyword(s):  
Spinal Cord ◽  
At Risk ◽  
Treatment Planning ◽  
Organs At Risk ◽  
Planning System ◽  
Treatment Planning System ◽  
Dose Volume Histogram ◽  
Parotid Glands ◽  
Dose Volume ◽  
Dose Sparing

Abstract Purpose In this study, we employed a gated recurrent unit (GRU)-based recurrent neural network (RNN) using dosimetric information induced by individual beam to predict the dose-volume histogram (DVH) and investigated the feasibility and usefulness of this method in biologically related models for nasopharyngeal carcinomas (NPC) treatment planning. Methods and materials One hundred patients with NPC undergoing volumetric modulated arc therapy (VMAT) between 2018 and 2019 were randomly selected for this study. All the VMAT plans were created using the Monaco treatment planning system (Elekta, Sweden) and clinically approved: > 98% of PGTVnx received the prescribed doses of 70 Gy, > 98% of PGTVnd received the prescribed doses of 66 Gy and > 98% of PCTV received 60 Gy. Of these, the data from 80 patients were used to train the GRU-RNN, and the data from the other 20 patients were used for testing. For each NPC patient, the DVHs of different organs at risk were predicted by a trained GRU-based RNN using the information given by individual conformal beams. Based on the predicted DVHs, the equivalent uniform doses (EUD) were calculated and applied as dose constraints during treatment planning optimization. The regenerated VMAT experimental plans (EPs) were evaluated by comparing them with the clinical plans (CPs). Results For the 20 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved good consistency relative to the CPs. The EPs showed better consistency in PTV dose distribution and better dose sparing for many organs at risk, and significant differences were found in the maximum/mean doses to the brainstem, brainstem PRV, spinal cord, lenses, temporal lobes, parotid glands and larynx with P-values < 0.05. On average, compared with the CPs, the maximum/mean doses to these OARs were altered by − 3.44 Gy, − 1.94 Gy, − 1.88 Gy, 0.44 Gy, 1.98 Gy, − 1.82 Gy and 2.27 Gy, respectively. In addition, significant differences were also found in brainstem and spinal cord for the dose received by 1 cc volume with 4.11 and 1.67 Gy dose reduction in EPs on average. Conclusion The GRU-RNN-based DVH prediction method was capable of accurate DVH prediction. The regenerated plans guided by the predicted EUDs were not inferior to the manual plans, had better consistency in PTVs and better dose sparing in critical OARs, indicating the usefulness and effectiveness of biologically related model in knowledge-based planning.

scholarly journals Application of Dose-Volume Histogram Prediction in Biologically Related Models for Nasopharyngeal Carcinomas Treatment Planning

2020 ◽  
Author(s):  
Wufei Cao ◽  
Yongdong Zhuang ◽  
Lixin Chen ◽  
Xiaowei Liu
Keyword(s):  
Spinal Cord ◽  
At Risk ◽  
Treatment Planning ◽  
Organs At Risk ◽  
Planning System ◽  
Treatment Planning System ◽  
Dose Volume Histogram ◽  
Parotid Glands ◽  
Dose Volume ◽  
Dose Sparing

Abstract Purpose: In this study, we employed a gated recurrent unit (GRU)-based recurrent neural network (RNN) using dosimetric information induced by individual beam to predict the dose-volume histogram (DVH) and investigated the feasibility and usefulness of this method in biologically related models for nasopharyngeal carcinomas (NPC) treatment planning.Methods and Materials: One hundred patients with NPC undergoing volumetric modulated arc therapy (VMAT) between 2018 and 2019 were randomly selected for this study. All the VMAT plans were created using the Monaco treatment planning system (Elekta, Sweden) and clinically approved: >98% of PGTVnx received the prescribed doses of 70 Gy, >98% of PGTVnd received the prescribed doses of 66 Gy and >98% of PCTV received 60 Gy. Of these, the data from 80 patients were used to train the GRU-RNN, and the data from the other 20 patients were used for testing. For each NPC patient, the DVHs of different organs at risk were predicted by a trained GRU-based RNN using the information given by individual conformal beams. Based on the predicted DVHs, the equivalent uniform doses (EUD) were calculated and applied as dose constraints during treatment planning optimization. The regenerated VMAT experimental plans (EPs) were evaluated by comparing them with the clinical plans (CPs).Results: For the 20 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved good consistency relative to the CPs. The EPs showed better consistency in PTV dose distribution and better dose sparing for many organs at risk, and significant differences were found in the maximum/mean doses to the brainstem, brainstem PRV, spinal cord, lenses, temporal lobes, parotid glands and larynx with P-values <0.05. On average, compared with the CPs, the maximum/mean doses to these OARs were altered by -3.44 Gy, -1.94 Gy, -1.88 Gy, 0.44 Gy, 1.98 Gy, -1.82 Gy and 2.27 Gy, respectively. In addition, significant differences were also found in brainstem and spinal cord for the dose received by 1cc volume with 4.11 and 1.67 Gy dose reduction in EPs on average.Conclusion: The GRU-RNN-based DVH prediction method was capable of accurate DVH prediction. The regenerated plans guided by the predicted EUDs were not inferior to the manual plans, had better consistency in PTVs and better dose sparing in critical OARs, indicating the usefulness and effectiveness of biologically related model in knowledge-based planning.

Determination of computed tomography number of high-density materials in 12-bit, 12-bit extended and 16-bit depth for dosimetric calculation in treatment planning system

2019 ◽  
Vol 18 (03) ◽  
pp. 285-294
Author(s):  
Jayapramila Jayamani ◽  
Noor Diyana Osman ◽  
Abdul Aziz Tajuddin ◽  
Zaker Salehi ◽  
Mohd Hanafi Ali ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Treatment Planning ◽  
Region Of Interest ◽  
High Density ◽  
Planning System ◽  
Treatment Planning System ◽  
Tube Voltage ◽  
Ct Number ◽  
Significant Difference

AbstractAimThe main aim was to examine the effect of bit depth on computed tomography (CT) number for high-density materials. Analysis of the CT number for high-density materials using 16-bit scanners will extend the CT scale that currently exists for 12-bit scanners and thus will be beneficial for use in CT–electron density (ED) curve in radiotherapy treatment planning system (TPS). Implementation of this extended CT scale will compensate for tissue heterogeneity during CT–ED conversion in treatment planning.Materials and methodsAn in-house built phantom with 10 different metal samples was scanned using 80, 100 and 120 kVp in two different CT scanners. A region of interest was set at the centre of the material and the mean CT numbers together with data deviation were determined. Dosimetry calculation was performed by applying a direct anterior beam on 12-bit, 12-bit extended and 16-bit.ResultsHigh-density materials (&gt;4·34 g cm−3) in 16-bit depth provide disparities up to 44% compared to Siemens’ 12-bit extended. Influence of tube voltage showed a significant difference (p&lt;0·05) in both bit depth and CT number of the gold and amalgam saturated in 16-bit depth. A 120 kVp energy illustrated a low variation on CT number for different scanners, but dosimetry calculation showed significant disparities at the metal interface in 12-bit, 12-bit extended and 16-bit.FindingsHigh-density materials require 16-bit scanners to obtain CT number to be implemented in treatment planning in radiotherapy. This also suggests that proper tube voltage together with correct CT–ED resulted in accurate TPS algorithm calculation.

Assessing the accuracy of treatment planning system based radiotherapy structure volumes

2020 ◽  
pp. 1-5
Author(s):  
Richa Sharma ◽  
Sunil Dutt Sharma ◽  
Devesh Kumar Avasthi
Keyword(s):  
Computed Tomography ◽  
Treatment Planning ◽  
Linear Correlation ◽  
Planning System ◽  
Treatment Planning System ◽  
Dose Volume Histogram ◽  
Volume Table ◽  
Dose Volume ◽  
Sharp Corners ◽  
Practical Impact

Abstract Aim: The purpose of the present study was to assess the accuracy of radiotherapy (RT) structure volume generated by the Monaco treatment planning system (TPS) for three different computed tomography (CT) slice thicknesses. Further, this study addressed the important issue of ‘different volumes of the same RT structure shown at different places’ in the Monaco TPS. Also, the practical impact of this difference in structure volumes has been studied for brain or head and neck patients. Materials and Methods: Objects of known volumes were scanned with different CT slice thicknesses and contoured as an RT structure in Monaco TPS and two different volumes provided by the TPS for each RT structure were noted and compared with the real volumes of these objects. In addition, correlation was also assessed between TPS provided volumes and real volumes of these objects. The study was further extended to obtain correlation of volumes in cases of organs that exist in pairs (e.g., eye) in the human body. Results: Monaco TPS overestimates structure volumes except for objects with sharp corners. Although, volumes shown at different places of the same structure have nearly a linear correlation, volumes under structure table are more accurate than those shown under dose–volume histogram (DVH) statistics (total volume) table. Difference in magnitude between these two volumes has no correlation if this difference is analysed for paired organs. Findings: This study confirmed that Monaco TPS provides ‘different value at different places’ of the volume of a given contoured structure. It is recommended that this issue should be reviewed and resolved by the supplier.

scholarly journals A data-driven method to predict achievability of clinical objectives in IMRT

2021 ◽  
Author(s):  
Vaitheeswaran Ranganathan
Keyword(s):  
Head And Neck ◽  
Treatment Planning ◽  
Region Of Interest ◽  
Target Volume ◽  
Data Driven ◽  
Planning System ◽  
Treatment Planning System ◽  
Geometric Complexity ◽  
Imrt Planning ◽  
Novel Method

Abstract When specifying a clinical objective for a target volume and normal organs/tissues in IMRT planning, the user may not be sure if the defined clinical objective could be achieved by the optimizer. To this end, we propose a novel method to predict the achievability of clinical objectives upfront before invoking the optimization. A new metric called “Geometric Complexity (GC)” is used to estimate the achievability of clinical objectives. Essentially GC is the measure of the number of “unmodulated” beamlets or rays that intersect the Region-of-interest (ROI) and the target volume. We first compute the geometric complexity ratio (GCratio) between the GC of a ROI in a reference plan and the GC of the same ROI in a given plan. The GCratio of a ROI indicates the relative geometric complexity of the ROI as compared to the same ROI in the reference plan. Hence GCratio can be used to predict if a defined clinical objective associated with the ROI can be met by the optimizer for a given case. We have evaluated the proposed method on six Head and Neck cases using Pinnacle3 (version 9.10.0) Treatment Planning System (TPS). Out of total of 42 clinical objectives from six cases accounted in the study, 37 were in agreement with the prediction, which implies an agreement of about 88% between predicted and obtained results. The results indicate the feasibility of using the proposed method in head and neck cases for predicting the achievability of clinical objectives.

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