Measurement the Hounsfield Unit of Alginate Impression Material Using CT-Scan Equipment to Find Equility with The Human Body Tissue

Guswantoro, Taat; Supratman, Astri Suppa; Asih, Imelda Sakti

doi:10.2991/assehr.k.210615.070

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The digital images were Region of Interest (ROI) processed using Radiant DICOM Viewers and Micro DICOM Viewers software to obtain CT number. The CT number value was then used in determining relative electron density using equation 1 and 2 [14].…”

Section: Experimental Methodsmentioning

confidence: 99%

Study of 3D printing materials as potential applications of phantom radiology

Yunianto,

Andika,

Suparmi

et al. 2023

J. Phys.: Conf. Ser.

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This paper reports the results of the study of 3D printing materials applied as phantom radiology, which is an imitation of human body tissue for medical radiology purposes. The types of 3D printing materials were ABS, PETG, flexible TPU, Wood and PLA resin. Each of them was printed using 3D printing into phantom sample of 30 mm x 30 mm x 9 mm. The phantom samples were then tested using Siemens CT-Scan machine. Radiant Dicom Viewers and Micro Dicom Viewers software were used to process the data and determine the amount of CT number. The CT number values of ABS, PETG, fleksible TPU, Wood and PLA resin filaments were -680,00 HU; -641,17 HU; -685,62 HU; -690,00 HU and 133,00 HU. The electron density values of 3D printing materials were 0,32; 0,36; 0,31; 0,31 and 1,12. These two values were confirmed using Gammex 467 Tissue Characterization Phantom. The results found that ABS, Wood, PETG and FLEX had potential for lung phantom radiology materials. However fleksibel TPU filament has the greatest potential because it has the closest CT-number and electron density value.

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Section: Experimental Methodsmentioning

confidence: 99%

Study of 3D printing materials as potential applications of phantom radiology

Yunianto,

Andika,

Suparmi

et al. 2023

J. Phys.: Conf. Ser.

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“…Making alginate boluses is quite easy, namely by mixing the impression material alginate with water to form a dough and then shaping it as needed. Tests that have been carried out on alginate boluses are the measurement of CT-Number, Electron Density and increased surface dose [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Determination of Effective Atomic Number and Radiation Mass Attenuation Coefficient of the Bolus from Alginate Impression Material

Guswantoro,

Hidayat,

Haryanto

et al. 2023

AMM

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Alginate impression material is a material that is commonly used to print dentures. This material is made from alginate. The properties of this material are rubbery, elastic, and not easily deformed so it can be applied as a bolus. This study aims to determine the tissue equivalence of this material by looking at aspects of the effective atomic number and radiation mass attenuation coefficient. To determine the effective atomic number, we used the EDX results from the SEM machine and input the concentration data of each element from alginate gel with 20% water content into the Autozeff software. The radiation mass attenuation coefficient is simulated by calculations involving each element’s concentration, then confirmed by CT-Number obtained from CT-Scan images. We obtained an effective atomic number and mass attenuation coefficient from the calculation results, which are described as a function of photon energy. Based on the results obtained, it can be concluded that the bolus made from alginate impression material is equivalent to soft tissue and breast tissue.

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Non-Ionic Deep Learning-Driven IR-UWB Multiantenna Scheme for Breast Tumor Localization

Phasukkit

2022

IEEE Access

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This research proposes a deep learning-driven impulse radio ultra-wideband (IR-UWB) multiantenna scheme for non-ionic breast tumor localization. The structure of the multiantenna scheme consists of one side slot Vivaldi transmitting (Tx) and nine side slot Vivaldi receiving (Rx 1 -Rx 9 ) antennas. To mitigate the attenuation and improve the diagnostic accuracy, the multiantenna scheme is rotated clockwise in 90 ° increments around the breast, with the angular position of the Tx antenna of 0 °, 90 °, 180 °, and 270 °. The deep learning algorithm is utilized to detect and localize the breast tumor, with 17 classification outputs, consisting of classifications 1 -16 which correspond to 16 vertically discretized segments of the breast and classification 17 for cancer-free. Experiments were carried out using heterogenous breast replicas with a tumor of 1 cm in diameter, and the breast replicas possess the dielectric property and Hounsfield units (HU) similar to those of human breasts. The experimental results were compared with the computed tomography (CT) scan images. The results reveal that the multiantenna scheme could efficiently detect and accurately localize the breast tumor for nearly all classifications, with the total accuracy (average of F1 scores) of 99.11 %. Specifically, the novelty of this research lies in the use of deep learning with the IR-UWB technology to effectively localize breast tumors.

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Measurement the Hounsfield Unit of Alginate Impression Material Using CT-Scan Equipment to Find Equility with The Human Body Tissue

Cited by 3 publications

References 17 publications

Study of 3D printing materials as potential applications of phantom radiology

Study of 3D printing materials as potential applications of phantom radiology

Determination of Effective Atomic Number and Radiation Mass Attenuation Coefficient of the Bolus from Alginate Impression Material

Non-Ionic Deep Learning-Driven IR-UWB Multiantenna Scheme for Breast Tumor Localization

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