Bolus is widely used to protect and reduce irradiation in organ at risk in the process of radiotherapy. Generally, the bolus was made from polymer material due to had property is similar or equivalent to tissue. This study aimed to determine the density, transmission factor, effective mass attenuation coefficient on bolus with radiation of photons and electrons. The bolus material (B) was used Propylene Glycol (PG), Silicon Rubber (SR) and Aluminium Powder (Al), and had four types of bolus namely B - PG 24%, B - PG 24%; SR 8%, B - PG 24%; SR 8%; Al 0,5%, and B - PG 24%; SR 8%; Al 1,5% with dimensions of 11 × 11 cm and thickness of 1 and 1.5 cm. The bolus density is evaluated through the mass of each volume. The measured data revealed that all of the boluses have density values which are similar to tissue or water and air in the range from 0,864 - 1,202 g/cm3. For dosimetry testing, the bolus is irradiated using Linear Accelerator with 6 and 10 MV for photon source and 6 and 12 MeV for an electron source. The results showed that B - PG 24%; SR 8%; Al 1.5% for dosimetry testing both 6 and 10 MV photons obtained properties that resemble soft tissue. Meanwhile, for both dosimetry testing of 12 MeV electrons, the B - PG 24%; SR 8% with addition silicone rubber and aluminum have nature closest to soft tissue. All of the boluses that have been fabricated have properties similar to soft tissue for photon therapy whereas the addition of more aluminum making a bolus has features as a shield on the process of radiotherapy.

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The quality of the contrast enhancement, which is deemed to be vague in contrast between one region and another, is a problem that many doctors face once identifying their patients with CT scan images. Image correction was used in this study to help doctors gain good CT scan images. In addition to reducing errors in the administering of radiation doses during treatment, accurate images are used to locate and assess the extent of cancer in patients. In this study, a computer application program to improve image contrast was created using the linear regression equation method. In this investigation, the cancer area is still being manually marked by doctors. Additionally, the proportion of the cancer area in the image that the doctor marked from the corrected image is calculated by comparing the ratio of cancer pixels to body pixels. The severity of the cancer is estimated using the proportion of the affected area. The error percentage for the average improvement in application performance is 0.15689%.

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