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Pengaruh Penambahan SiO2 dan PTFE Terhadap Respon Termolumi-nesensi TLD CaSO4:Dy Nuraeni, Nunung; Kartikasari, Dewi; Iskandar, Ferry; Haryanto, Freddy; Waris, Abdul; Hiswara, Eri
Jurnal Matematika dan Sains Vol 22 No 1 (2017)
Publisher : Institut Teknologi Bandung

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Thermoluminescence response of TLD CaSO4: Dy with a variation of the addition of SiO2 and PTFE materials has been observed. TLD CaSO4:Dy derived through co-precipitation method and then added by SiO2 and PTFE. Thermoluminescence intensity for CaSO4:Dy added by SiO2 obtained 9.41, 5.32; and 13.93 nC for the temperature at 400 °C, 600 °C and 700 °C. As for CaSO4:Dy with the addition of PTFE obtained 33.10;  336.89; and 1191.11 nC for the temperature at 400 oC temperature, 600 °C and 700 °C. Thermoluminscence intensity for CaSO4:Dy without the addition of SiO2 and PTFE at a temperature of 700 °C is 75.15 nC. There’s a significant increasing in the thermoluminescence intensity on CaSO4:Dy added by PTFE.

AUTOMATED UNIVERSAL IMAGE QUALITY INDEX MEASUREMENT VS. AUTOMATED NOISE MEASUREMENT: WHICH METHOD IS BETTER TO DEFINE CT IMAGE QUALITY? Lestari, Fauzia Puspa; Anam, Choirul; Hardiyanti, Yati; Haryanto, Freddy
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol 9, No 2 (2019)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v9n2.p132-139

Automatitation method in defining the quality of CT image is needed to optimize CT Scan treatment planning. So, the optimization of treatment planning can also be done automatically. There are various methods proposed to define the quality of an image. The purpose of this study was to find the simple and precision method to define CT image. We compared the performance of Automated Noise Measurement (ANM) and Automated Universal Image Quality Index (UIQI). We also compared them with the Manual noise measurement method based on the level of convergence in homogeneous images. The first step of Automated Noise Measurement was to create binary density slice using threshold values. Then, a masked image was performed by masking the original image and binary image. The standard deviation of every pixel for a certain kernel size was calculated by using a sliding window operation. The fourth step was to make a noise histogram from the noise map and determine the final noise in the image as the histogram peak. Then this calculation was normalized by the peak of the Hounsfield Unit (HU) histogram. All these steps were done with various kernel sizes for different slices in-homogenous phantom. In the Automatic UIQI method, the steps in the ANM method are carried out until the masked image stage, then UIQI is calculated for the masked image. The results show that automatic UIQI was more convergence in defining image quality than manual noise measurement and automated noise measurement by the lowest standard deviation which was only 0.00032867.

Automated Universal Image Quality Index Measurement vs. Automated Noise Measurement: Which Method is Better to Define CT Image Quality? Lestari, Fauzia Puspa; Anam, Choirul; Hardiyanti, Yati; Haryanto, Freddy
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol 9, No 2 (2019)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v9n2.p132-139

Automatitation method in defining the quality of CT image is needed to optimize CT Scan treatment planning. So, the optimization of treatment planning can also be done automatically. There are various methods proposed to define the quality of an image. The purpose of this study was to find the simple and precision method to define CT image. We compared the performance of Automated Noise Measurement (ANM) and Automated Universal Image Quality Index (UIQI). We also compared them with the Manual noise measurement method based on the level of convergence in homogeneous images. The first step of Automated Noise Measurement was to create binary density slice using threshold values. Then, a masked image was performed by masking the original image and binary image. The standard deviation of every pixel for a certain kernel size was calculated by using a sliding window operation. The fourth step was to make a noise histogram from the noise map and determine the final noise in the image as the histogram peak. Then this calculation was normalized by the peak of the Hounsfield Unit (HU) histogram. All these steps were done with various kernel sizes for different slices in-homogenous phantom. In the Automatic UIQI method, the steps in the ANM method are carried out until the masked image stage, then UIQI is calculated for the masked image. The results show that automatic UIQI was more convergence in defining image quality than manual noise measurement and automated noise measurement by the lowest standard deviation which was only 0.00032867.

Dose Volume Product (DVP) As Descriptor for Estimating Total Energy Imparted to Patient Undergoing CT Examination Choirul Anam; Freddy Haryanto; Rena Widita; Idam Arif; Geoff Dougherty
Journal of Medical Physics and Biophysics Vol 3, No 1 (2016)
Publisher : Indonesian Association of Physicists in Medicine (AIPM/AFISMI)

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The purpose of this study is to expand a descriptor for estimating the total energy imparted to a patient undergoing a CT examination and to investigate its relationship to the currently used descriptor. Estimating the total energy imparted to a patient has previously been characterized by dose length product (DLP). We propose a descriptor which we call the dose volume product (DVP), defined as the product of the size specific-dose estimate (SSDE) and the volume irradiated in the patient (V). We also present algorithm to automate the calculation of DVP. There are several steps in calculating the DVP: the first is to contour the patient automatically, the second is to calculate the area of patient in every single slice, the third is to calculate the volume of the radiated part of the patient, the fourth is to calculate the water equivalent diameter (DW) automatically, the fifth is to calculate the SSDE, and the last is to calculate the DVP. To investigate the effectiveness of the algorithm, we used it on images of phantoms and patients. The results of this study show that the automated calculations of DVP for both body and head phantoms were in good agreement with theoretical calculations. The differences between them were within 2%. DVP and DLP had a linear relationship with R2 = 0.971 (slope 1099 cm2, 95% confidence interval (CI), 1047 to 1157 cm2) and R2 = 0.831 (slope 248.6 cm2: CI, 237.6 to 259.7 cm2), for thorax and head patients respectively.

Kajian dari Proses Studi Kontrol Kualitas Gambar Magnetic Resonance Imaging (MRI) pada Simulasi Treatment Planning Gamma Knife Irhas Irhas; Freddy Haryanto; Elia Soediatmoko
Journal of Medical Physics and Biophysics Vol 5, No 2 (2018)
Publisher : Indonesian Association of Physicists in Medicine (AIPM/AFISMI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (668.352 KB)

Kajian dari Proses Studi Kontrol Kualitas Gambar Magnetic Resonance Imaging (MRI) pada Simulasi Treatment Planning Gamma Knife

Automated Universal Image Quality Index Measurement vs. Automated Noise Measurement: Which Method is Better to Define CT Image Quality? Fauzia Puspa Lestari; Choirul Anam; Yati Hardiyanti; Freddy Haryanto
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol. 9 No. 2 (2019)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v9n2.p132-139

Automatitation method in defining the quality of CT image is needed to optimize CT Scan treatment planning. So, the optimization of treatment planning can also be done automatically. There are various methods proposed to define the quality of an image. The purpose of this study was to find the simple and precision method to define CT image. We compared the performance of Automated Noise Measurement (ANM) and Automated Universal Image Quality Index (UIQI). We also compared them with the Manual noise measurement method based on the level of convergence in homogeneous images. The first step of Automated Noise Measurement was to create binary density slice using threshold values. Then, a masked image was performed by masking the original image and binary image. The standard deviation of every pixel for a certain kernel size was calculated by using a sliding window operation. The fourth step was to make a noise histogram from the noise map and determine the final noise in the image as the histogram peak. Then this calculation was normalized by the peak of the Hounsfield Unit (HU) histogram. All these steps were done with various kernel sizes for different slices in-homogenous phantom. In the Automatic UIQI method, the steps in the ANM method are carried out until the masked image stage, then UIQI is calculated for the masked image. The results show that automatic UIQI was more convergence in defining image quality than manual noise measurement and automated noise measurement by the lowest standard deviation which was only 0.00032867.

Simulasi Rekonstruksi Citra Pada Sensor Brain ECVT (Electrical Capacitance Volume Tomography) dengan Metode ILBP (Iterative Linear Back Projection) Nita Handayani; Kharisma Fajar H; Freddy Haryanto; Siti Nurul K; Marlin R Baidillah; Warsito P Taruno
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 6, No 02 (2016): IJAP Volume 06 Issue 02 Year 2016
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v6i02.1480

The purpose of this study is to simulate the sensor 32-channel Brain ECVT image reconstruction using ILBP (Iterative Linear Back Projection) methods. ECVT is a dynamic volume imaging technique that utilizes non-linear difference of electric field distribution to determine the distribution of permittivity in the sensing area. ECVT has measured the capacitance of data as a result of changes in the permittivity distribution between the electrode pairs. ECVT device consists of three main parts: helmet-shaped sensors, DAS (Data Acquisition System), PC for display and image reconstruction process. Simulation of sensor design using COMSOL Multiphysics 3.5 software, while the process of image reconstruction and analysis of the results using Matlab software 2009a. The principle of ECVT includes two stages of data collection capacitance of electrodes (forward problem) and image reconstruction from the measured capacitance (inverse problem). In the study, the simulation of image reconstruction was done by varying the object position, the number of objects and charge density of the object. From the simulation results showed that the reconstructed image with ILBP method is influenced by several parameters: the object's position in the sensor,charge density value of the object, an alpha value and the number of iterations was selected.

Analisis Perbedaan Pembacaan Nilai Uji Kesesuaian Pesawat Sinar-X Radiografi Umum menggunakan Multimeter X-Ray Raysafe dan Radcal R. Silvia Putri Raharja Effendi; Wuwus Ardiatna; Freddy Haryanto
Journal of Medical Physics and Biophysics Vol 10, No 1 (2023)
Publisher : Indonesian Association of Physicists in Medicine (AIPM/AFISMI)

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Penggunaan pesawat sinar-X secara terus-menerus berakibat pada penurunan efisiensi kinerja, maka perlu dilakukan Uji Kesesuaian untuk menjamin mutu fungsi kerja alat. Multimeter X-ray dengan jenis berbeda menunjukkan hasil pembacaan pengukuran berbeda terhadap pesawat sinar-X yang sama dalam satu rentang waktu. Penelitian ini dilakukan dengan tujuan menentukan besar perbedaan pembacaan nilai yang diperoleh dari dua multimeter X-ray berbeda. Metode penelitian mengacu pada Pedoman Teknis Uji Kesesuaian Pesawat Sinar-X Radiografi Umum Nomor KU/PD/DKKN/04/1. Parameter yang diuji yaitu akurasi tegangan pada 40, 50, 60, 70 dan 80 kVp dengan pengaturan konstan 0,100 s dan 80 mA, akurasi waktu pada 0,025; 0,050; 0,080; 0,100 dan 0,125 s dengan pengaturan konstan 50 kVp dan 200 mA, linearitas keluaran pada 20, 25, 40, 50 dan 80 mA dengan pengaturan konstan 70 kVp dan 0,100 s, reproduksibilitas pada pengaturan 70 kVp, 200 mA dan 0,100 s. Pembacaan nilai uji dilakukan secara bergantian. Diperoleh nilai error terbesar akurasi tegangan pada merk Raysafe sebesar 2,60% dan Radcal 1,60%, error terbesar akurasi waktu pada Raysafe 1,20% dan Radcal 3,64%, nilai CL merk Raysafe dan Radcal sebesar 0,04. Nilai CV untuk tegangan, waktu dan dosis pada Raysafe dan Radcal menunjukkan nilai yang sama yaitu sebesar 0,00; 0,00 dan 0,01. Kemudian uji-t pada hasil pembacaan uji reproduksibilitas dengan confidence level 95% menunjukkan tidak adanya perbedaan pembacaan nilai yang signifikan dari multimeter X-ray Raysafe dan Radcal.

Algoritma Convolutional Neural Network sebagai Alat Bantu Analisa Tingkat Keparahan Tumor Otak IRMANIAR, IRMANIAR; MANIK, JOSUA TIMOTIUS; HARYANTO, FREDDY
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 1 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i1.1-12

AbstrakKecerdasan buatan telah menjadi dasar dalam pengembangan computer-aided-diagnosed (CAD), yaitu alat tambahan yang digunakan untuk melakukan diagnosa penyakit, misalnya tumor otak. Pada penelitian ini dilakukan klasifikasi otomatis citra MRI otak ke dalam 4 kategori, yaitu tumor otak grade II, III, IV dan non-tumor menggunakan Convolutional Neural Network (CNN). Tiga jenis arsitektur yang digunakan, yaitu arsitektur 12 lapisan, Resnet-152 dan VGG-16. Peningkatan jumlah gambar dilakukan dengan melakukan 6 jenis teknik augmentasi. Hasilnya menunjukkan bahwa ketiga model dapat melakukan klasifikasi tumor dengan akurasi masing-masing sebesar 84%, 95% dan 84% pada data tanpa augmentasi dan 49%, 81% dan 72% untuk data yang mengalami augmentasi. Hasil tersebut menunjukkan bahwa arsitektur Resnet-152 memberikan performa terbaik dibandingkan dengan arsitektur lainnya.Kata kunci: Tumor otak, Convolutional Neural Network (CNN), Resnet-152, VGG-16AbstractArtificial intelligence has become the basis for the development of computer-aided-diagnosed (CAD), an additional tool used to diagnose diseases, such as brain tumors. In this study, automatic classification of brain tumor was carried out into 4 categories, namely grade II, III, IV and non-tumor using the Convolutional Neural Network (CNN) algorithm. Three types of architecture are used, namely 12 layer architecture, Resnet-152 and VGG-16. The dataset comes from the REMBRANDT and IXI dataset. Increasing the number of images using 6 types of augmentation techniques is also done. The results show that the three models can classify tumors with an accuracy of 84%, 95% and 84% respectively for data without augmentation and 49%, 81% and 72% for data with augmentation. It can be concluded that the Resnet-152 architecture provides the best performance than the other architectures.Keywords: Brain tumor, Convolutional Neural Network (CNN), Resnet-152, VGG-16

Comparison of CTDIw and Homogeneity Index on CTDI Phantoms Utami, Moh. Shofi Nur; Asni, Nur; Haryanto, Freddy; Laksono, Muharam Budi; Yusifa, Anggun; Nermina, Nermina
Jurnal Fisika Vol 13, No 2 (2023)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jf.v13i2.48397

The study was conducted to compare the Computed Tomography Dose Index Weighted (CTDIw) value values and homogeneity index on head and body phantoms with tube voltage variations. Two CTDI phantoms are Gammex (Sun Nuclear, Florida, United States) and IBA (IBA Dosimetry, Schwarzenbruck, Germany). The pencil ionization chamber was used for the measurement of CTDI. The measurements were carried out with a Toshiba Alexion 16 MSCT in a single rotation of axial mode with detector position in the phantom’s center, top, bottom, right, and left. Tube voltage values are 80 kVp, 100 kVp, and 120 kVp. Then, the homogeneity test of the phantom was carried out. The homogeneity value was obtained by measuring the average CT number in the image by determining the region of interest (ROI) at positions namely a, b, c, d, and e, In addition the ratio of the two phantoms was also carried out. The ratio was obtained from the difference of the CTDI100 value at the edge to the CTDI100 value at the center of the head and body phantom from Gammex and IBA. The results showed that the CTDIwof the Gammex head phantom are 26.83 mGy (80 kV), 53.32 mGy (100 kV) and 83.32 mGy (120 kV). While the CTDIw of the Gammex body phantom are 11.73 mGy (80 kV), 21.58 mGy (100 kV) and 36.45 mGy (120 kV). In comparison, CTDIw of the IBA head phantom are 27.01 mGy (80 kV), 55.33 mGy (100 kV) and 81.69 mGy (120 kV). While the CTDIw of the IBA body phantom are 11.85 mGy (80 kV), 23.32 mGy (100 kV) and 35.00 mGy (120 kV). The differences in CTDIw of the two phantoms were within (head phantom is 0.18 % – 2.01 %) and (body phantom is 0.13 % – 1.75 %). The difference below 5% with the p-value of the head phantom is 0.87 and body phantom is 0.89 (more than 0.05) indicates that the two phantoms are not significantly different because the two phantoms are made of the same material. The average ratio for the Gammex head phantom is 1.12 – 1.28, while the IBA head phantom is 1.07 – 1.28. Then the average ratio for the Gammex body phantom is 2.03 – 2.56, while for the IBA body phantom is 1.91 – 2.59 which indicates that the head phantom produces a more uniform dose distribution compared to a body phantom. The average homogeneity value of the IBA phantom is 90.52 % and the average homogeneity value of the Gammex phantom is 87.15 % (a difference of around 3.37%). This value shows that Gammex and IBA phantom have fairly good homogeneity

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