• Progress in Medical Physics
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• v.29 no.4
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• pp.106-114
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• 2018

This study aimed to compare the performance of previous optimization algorithms against new a photon optimizer (PO) algorithm for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) plans for prostate cancer. Eighteen patients with prostate cancer were retrospectively selected and planned to receive 78 Gy in 39 fractions of the planning target volume (PTV). All plans for each patient optimized with the dose volume optimizer (DVO) and progressive resolution optimizer (PRO) algorithms for IMRT and VMAT were compared against plans optimized with the PO within Eclipse version 13.7. No interactive action was performed during optimization. Dosimetric and radiobiological indices for the PTV and organs at risk were analyzed. The monitor units (MU) per plan were recorded. Based on the plan quality for the target coverage, prostate IMRT and VMAT plans using the PO showed an improvement over DVO and PRO. In addition, the PO generally showed improvement in the tumor control probability for the PTV and normal tissue control probability for the rectum. From a technical perspective, the PO generated IMRT treatment plans with fewer MUs than DVO, whereas it produced slightly more MUs in the VMAT plan, compared with PRO. The PO showed over potentiality of DVO and PRO whenever available, although it led to more MUs in VMAT than PRO. Therefore, the PO has become the preferred choice for planning prostate IMRT and VMAT at our institution.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3772-3783
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• 2021

This work proposes a new efficient meta-heuristic optimization algorithm called Gamma Ray Interactions Based Optimization (GRIBO). The algorithm mimics different energy loss processes of a gamma-ray photon during its passage through a matter. The proposed novel algorithm has been applied to search for the global minima of 30 standard benchmark functions. The paper also considers solving real optimization problem in the field of nuclear engineering, radioisotope identification. The results are compared with those obtained by the Particle Swarm Optimization, Genetic Algorithm, Gravitational Search Algorithm and Grey Wolf Optimizer algorithms. The comparisons indicate that the GRIBO algorithm is able to provide very competitive results compared to other well-known meta-heuristics.

• Progress in Medical Physics
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• v.30 no.4
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• pp.150-154
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• 2019

The objective of this study is to monitor the radiation doses delivered to a cardiac implantable electronic device (CIED) by comparing the absorbed doses calculated by a commercial treatment planning system (TPS) to those measured by an in vivo dosimeter. Accurate monitoring of the radiation absorbed by a CIED during radiotherapy is necessary to prevent damage to the device. We conducted this study on three patients, who had the CIED inserted and were to be treated with radiotherapy. Treatment plans were generated using the Eclipse system, with a progressive resolution photon optimizer algorithm and the Acuros XB dose calculation algorithm. Measurements were performed on the patients using optically stimulated luminescence detectors placed on the skin, near the CIED. The results showed that the calculated doses from the TPS were up to 5 times lower than the measured doses. Therefore, it is recommended that in vivo dosimetry be conducted during radiotherapy for CIED patients to prevent damage to the CIED.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.5019-5024
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• 2015

Background: The purpose of this study was to assess the dosimetric and clinical feasibility of volumetric modulated arc based hypofractionated stereotactic radiotherapy (RapidArc) treatment for large acoustic schwannoma (AS >10cc). Materials and Methods: Ten AS patients were immobilized using BrainLab mask. They were subject to multimodality imaging (magnetic resonance and computed tomography) to contour target and organs at risk (brainstem and cochlea). Volumetric modulated arc therapy (VMAT) based stereotactic plans were optimized in Eclipse (V11) treatment planning system (TPS) using progressive resolution optimizer-III and final dose calculations were performed using analytical anisotropic algorithm with 1.5 mm grid resolution. All AS presented in this study were treated with VMAT based HSRT to a total dose of 25Gy in 5 fractions (5fractions/week). VMAT plan contains 2-4 non-coplanar arcs. Treatment planning was performed to achieve at least 99% of PTV volume (D99) receives 100% of prescription dose (25Gy), while dose to OAR's were kept below the tolerance limits. Dose-volume histograms (DVH) were analyzed to assess plan quality. Treatments were delivered using upgraded 6 MV un-flattened photon beam (FFF) from Clinac-iX machine. Extensive pretreatment quality assurance measurements were carried out to report on quality of delivery. Point dosimetry was performed using three different detectors, which includes CC13 ion-chamber, Exradin A14 ion-chamber and Exradin W1 plastic scintillator detector (PSD) which have measuring volume of $0.13cm^3$, $0.009cm^3$ and $0.002cm^3$ respectively. Results: Average PTV volume of AS was 11.3cc (${\pm}4.8$), and located in eloquent areas. VMAT plans provided complete PTV coverage with average conformity index of 1.06 (${\pm}0.05$). OAR's dose were kept below tolerance limit recommend by American Association of Physicist in Medicine task group-101(brainstem $V_{0.5cc}$ < 23Gy, cochlea maximum < 25Gy and Optic pathway <25Gy). PSD resulted in superior dosimetric accuracy compared with other two detectors (p=0.021 for PSD.

• Progress in Medical Physics
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• v.30 no.1
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• pp.7-13
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• 2019

Purpose: To compare the dosimetrical and radiobiological parameters among various volumetric modulated arc therapy (VMAT) techniques using restricted and continuous arc beams for left-sided breast cancer. Materials and Methods: Ten patients with left-sided breast cancer without regional nodes were retrospectively selected and prescribed the dose of 42.6 Gy in 16 fractions on the planning target volume (PTV). For each patient, three plans were generated using the $Eclipse^{TM}$ system (Varian Medical System, Palo Alto, CA) with one partial arc 1pVMAT, two partial arcs 2pVMAT, and two tangential arcs 2tVMAT. All plans were calculated through anisotropic analytic algorithm and photon optimizer with 6 MV photon beam of $VitalBEAM^{TM}$. The same dose objectives for each plan were used to achieve a fair comparison during optimization. Results: For PTV, dosimetrical parameters such as Homogeneity index, conformity index, and conformal number were superior in 2pVMAT than those in both techniques. $V_{95%}$, which indicates PTV coverage, was 91.86%, 96.60%, and 96.65% for 1pVMAT, 2pVMAT, and 2tVMAT, respectively. In most organs at risk (OARs), 2pVMAT significantly reduced the delivered doses compared with the other techniques, excluding the doses to contralateral lung. For the analysis of radiobiological parameters, a significant difference in normal tissue complication probability was observed in ipsilateral lung while no difference was observed in the other OARs. Conclusions: Our study showed that 2pVMAT had better plan quality and normal tissue sparing than 1pVMAT and 2tVMAT but not for all parameters. Therefore, 2pVMAT could be considered the priority choice for the treatment planning for left breast cancer.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.93-99
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• 2024

The digital medical imaging, especially, computed tomography (CT), should necessarily be considered in terms of noise distribution caused by converting to X-ray photon to digital imaging signal. Recently, the denoising technique based on deep learning architecture is increasingly used in the medical imaging field. Here, we evaluated noise reduction effect according to various noise types based on the U-net deep learning model in the lung CT images. The input data for deep learning was generated by applying Gaussian noise, Poisson noise, salt and pepper noise and speckle noise from the ground truth (GT) image. In particular, two types of Gaussian noise input data were applied with standard deviation values of 30 and 50. There are applied hyper-parameters, which were Adam as optimizer function, 100 as epochs, and 0.0001 as learning rate, respectively. To analyze the quantitative values, the mean square error (MSE), the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. According to the results, it was confirmed that the U-net model was effective for noise reduction all of the set conditions in this study. Especially, it showed the best performance in Gaussian noise.