CC Algorithm Research Articles

Feasibility of EPID Based In Vivo Dosimetry for On-Couch Adaptive Radiotherapy

CBCT-based online adaptive radiotherapy allows treatment plans to be tailored to the anatomy of the day. For this purpose, dose optimization and computation are performed on a synthetic CT (sCT), i.e., a density map of the planning CT deformably registered onto the acquired CBCT. Plan-specific quality assurance of adaptive treatment sessions is currently limited to the vendors own secondary dose calculation on the sCT. The use of EPID recordings promises to detect deviations in beam delivery or patient position, incorrect sCT data and anatomical changes. The purpose of this research was to evaluate the feasibility of using a new commercial technology available for in-vivo dose reconstruction based on EPID exit beam measurements. EPID images were recorded from on-couch adapted, hypofractionated treatment plans created for a prostate patient. Secondary dose calculation of the adapted plan and 3D reconstructions from EPID images of the absolute dose delivered to the patient were performed with the in-vivo dosimetry system. Comparison of reconstructed and planned delivery was conducted by means of gamma analysis and DVH metrics. Comparisons of the intended plan to a second volumetric check (collapsed cone - CC algorithm), and to the EPID in-vivo calculation, produced gamma passing rates averaged over the 7 fractions of 98.3% and 88.8% at TG-218/219 recommended criteria (Table 1). DVH metrics showed average deviations of -2.9% / -3.3% for PTV D95% and -1.2% / -0.2% for PTV D50% between intended dose and secondary / EPID in-vivo dose. The pronounced discrepancies for fraction 3 in terms of both reduced gamma passing rates and increased deviations for the EPID reconstructed dose coincide with a large air cavity in the rectum showing up in the patient's CBCT prior to treatment. Secondary dose calculation based on the density information of the CBCT as an additional option for QA confirmed origin and magnitude of this effect. In-vivo EPID dosimetry of adaptive clinical plans is feasible. It was shown exemplary that it reveals fractions with noticeable anatomical changes between the sCT and the patient anatomy during treatment. As new recommendations were published on in-vivo dosimetry through TG 307, the development of new clinical tools and evaluation of existing commercial solutions is essential, also in the context of the growing use of on-couch adaptive technology.

Application of optical flow algorithm for drift correction in electron microscopy images.

Transmission electron microscopy (TEM) image drift correction has been effectively addressed using diverse approaches, including the cross correlation algorithm (CC) and other strategies. However, most of the strategies fall short of achieving sufficient accuracy or cannot strike a balance between time consumption and accuracy. The present study proposes a TEM image drift correction strategy that enhances accuracy without any additional time consumption. Unlike the CC algorithm that matches pixels one by one, our approach involves the extraction of multiple feature points from the first TEM image and then uses the Lucas-Kanade (LK) optical flow algorithm to calculate the optical field of these feature points in the subsequent TEM images. The LK algorithm is used to calculate the instantaneous velocity of these feature points, which can help track the movement of the TEM image series. In addition, a high-precision sub-pixel level correction strategy by the utilization of linear interpolation during the correction process is developed in this work. Experimental results confirm that this strategy offers superior accuracy in comparison with the CC algorithm and also is insensitive to the size of the image. Furthermore, we offer a semantic segmentation neural network for electron microscope image pre-processing, thereby expanding the applicability of our methodology.

Production-Inventory Model with Preservation Technology Investment with Carbon Emission and Partial Backlogging

This study examines how well the Twin concatenation strategy for error-correcting capabilities performs with the BCH code. Low complexity BCH codes are the main focus. The quantity of mistakes introduced into the transmission channel has a significant impact on the quality of digital transmission. The Bose-Chaudhuri-Hocquenghem (BCH) codes are frequently employed in storage and communication systems. In next-generation wireless networks, low-latency communication is one of the most crucial application situations. The time needed for a packet to be transmitted through a channel is a common definition of latency in communication-theoretic studies. However, due to the strict latency requirements and complexity-restricted receivers, the time needed for packet decoding must be taken into account in the overall delay analysis through precise modeling. The new Twin CC algorithm has been proved to perform better than the current approach.

Development of a natural language processing algorithm to detect chronic cough in electronic health records

BackgroundChronic cough (CC) is difficult to identify in electronic health records (EHRs) due to the lack of specific diagnostic codes. We developed a natural language processing (NLP) model to identify cough in free-text provider notes in EHRs from multiple health care providers with the objective of using the model in a rules-based CC algorithm to identify individuals with CC from EHRs and to describe the demographic and clinical characteristics of individuals with CC.MethodsThis was a retrospective observational study of enrollees in Optum’s Integrated Clinical + Claims Database. Participants were 18–85 years of age with medical and pharmacy health insurance coverage between January 2016 and March 2017. A labeled reference standard data set was constructed by manually annotating 1000 randomly selected provider notes from the EHRs of enrollees with ≥ 1 cough mention. An NLP model was developed to extract positive or negated cough contexts. NLP, cough diagnosis and medications identified cough encounters. Patients with ≥ 3 encounters spanning at least 56 days within 120 days were defined as having CC.ResultsThe positive predictive value and sensitivity of the NLP algorithm were 0.96 and 0.68, respectively, for positive cough contexts, and 0.96 and 0.84, respectively, for negated cough contexts. Among the 4818 individuals identified as having CC, 37% were identified using NLP-identified cough mentions in provider notes alone, 16% by diagnosis codes and/or written medication orders, and 47% through a combination of provider notes and diagnosis codes/medications. Chronic cough patients were, on average, 61.0 years and 67.0% were female. The most prevalent comorbidities were respiratory infections (75%) and other lower respiratory disease (82%).ConclusionsOur EHR-based algorithm integrating NLP methodology with structured fields was able to identify a CC population. Machine learning based approaches can therefore aid in patient selection for future CC research studies.

Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis.

In recent years, with the rapid development of the Internet, online social networks have been continuously integrated with traditional interpersonal networks and research on information dissemination in social networks has gradually increased. This article studies and analyzes the multidimensional information network big data mining algorithm based on the finite element analysis method. This paper firstly introduces the finite element analysis and calculation process, a finite element data mining simulation application software management system will integrate current data, calculation, and background data into one, then analyzes the data mining clustering algorithm, and conducts an experimental exploration of the influential node mining algorithm in complex networks. The experimental results show that the LIC algorithm is better than the CC algorithm, the DC algorithm, and the BC algorithm; its overall performance is improved by 30%, and the effect is better. The LIC algorithm can effectively and quickly determine the influential nodes, which is helpful for social network analysis.

Investigation of Buildup Region and Surface Dose: Comparison of Parallel Plane Ion Chamber, Treatment Planning System, and MC Simulation

In these days, Monte Carlo (MC) simulation is a method that can calculate the radiation dose that occurs in an environment in the most accurate way. The correct measurement of the dose occurring on the patient’s surface is of great importance to estimate the reactions that may occur on the patient’s skin. This importance encouraged us to do this study. The aim of this study is to determine buildup region and surface doses using MC simulation and to compare them with results of the parallel plane ion chamber and Treatment Planning System (TPS) measurements for 6-MV photon beams. Surface doses normalized to the maximum dose for the parallel plane ion chamber, MC simulation, fast photon (FP) algorithm, and collapsed cone convolution superposition (CC) algorithm are 13.6%, 30.28%, 0%, and 27.33%, respectively. The CC algorithm and parallel plane ion chamber measurements are compatible with MC simulation but the FP algorithm has calculated the dose less to a depth of 0.8 cm. Measuring the surface dose and the doses in the buildup region is of great importance in terms of accurately predicting the complications that may occur in the patient’s skin and taking precautions early. Using some methods and correction factors, the surface dose and the doses that may occur in the buildup region can be accurately calculated. It is recommended not to use the FP algorithm for stereotactic body radiation therapy and intensity-modulated radiation therapy treatments, as it cannot calculate doses correctly in the buildup region and surface.

Comparison of Monaco treatment planning system algorithms and Monte Carlo simulation for small fields in anthropomorphic RANDO phantom: The esophagus case.

In this study, the dose distributions obtained by the algorithms used in Monaco treatment planning system (TPS) and Monte Carlo (MC) simulation were compared for small fields in the anthropomorphic RANDO phantom, and then, the results were analyzed using the gamma analysis method. In the study, dose distributions obtained from the collapse cone algorithm, MC algorithm, and MC simulation were examined. The EGSnrc was utilized for MC simulation. In radiation fields smaller than 3 cm × 3 cm, the doses calculated by the CC algorithm are particularly high in the region of lung/soft-tissue interfaces. In the region of soft-tissue/vertebral interfaces, the doses calculated by the CC algorithm and the MC algorithm are compatible with the MC simulation. For each algorithm, the main reason for the non-overlapping dose curves in small fields compared to MC simulation is that the lateral electronic equilibrium loss is not taken into account by the algorithms. The doses calculated by the algorithms used in TPS may differ, especially in environments where density changes are sharp. Even if the radiation dose from different angles is calculated similarly in the target area by the algorithms, the calculated doses in the tissues in each radiation field path may be different. Therefore, to increase the quality of radiotherapy and to protect critical organs more accurately, the accuracy of the algorithms in TPS should be checked before treatment, especially in multi-field treatments such as stereotactic body radiation therapy and intensity-modulated radiotherapy for tumors in the abdominal region.

The Effect of Algorithms on Dose Distribution in Inhomogeneous Phantom: Monaco Treatment Planning System versus Monte Carlo Simulation.

Background:The aim of this study is to evaluate the dose calculation algorithms commonly used in TPS by using MC simulation in the highly different inhomogeneous region and in the small fields and to provide the following uniquely new information in the study of correction algorithm.Materials and Methods:We compared the dose distribution obtained by Monaco TPS for small fields.Results:When we examine lung medium, for four different fields, we can see that the algorithms begin to differ. In both the lung and bone environment, the percentage differences decrease as the field size increases. In areas less than or equal to 3x3 cm2, there are serious differences between the algorithms. The CC algorithm calculates a low dose value as the photon passes from the lung environment to water environment. We can also see that this algorithm measures a low dose value in voxel as the photon passes from the water medium to the bone medium. In the transition from the water environment to the bone environment or from the bone environment to the water environment, the results of the CC algorithm are not close to MC simulation.Conclusion:The effect of the algorithms used in TPS on dose distribution is very strong, especially in environment with high electron density variation and in applications such as Stereotactic Body Radiotherapy and Intensity Modulated Radiotherapy where small fields are used.

Bicluster CC Algoritm Analysis to Identify Patterns of Food Insecurity in Indonesia

Indonesia is known as an agricultural country. This means that most of the population work in the agricultural sector related to food. However, food insecurity still occurs in Indonesia. With the COVID-19 pandemic, the Food and Agriculture Organization (FAO) stated that there was a threat of food scarcity which had an impact on food insecurity conditions. This would undermine the second goal of the SDGs, which is to end hunger and create sustainable agriculture. The purpose of this study was to determine the spatial pattern of food insecurity in each province in Indonesia using the bicluster method. The data used are data from Susenas and Sakernas by BPS in 2019. Several studies show that the bicluster method with the CC algorithm shows that each province group has a different characteristic pattern. In the bicluster approach, the researcher runs parameter tuning to select the best parameter based on the Mean Square Residual in Volume (MSR / V). The CC algorithm tries to get a bicluster with a low MSR value, therefore the best parameter is the one that produces the smallest MSR / V value, in this study the smallest MSR / V is 0,01737 with δ = 0,01. The application of the CC biclustering algorithm to the food insecurity structure in Indonesia results in 5 bicluster. Bicluster 1 consists of 15 provinces with 8 variables, Bicluster 2 consists of 10 provinces with 5 variables, Bicluster 3 consists of 3 provinces with 7 variables, Bicluster 4 consists of 4 provinces with 4 variables and Bicluster 5 consists of 2 provinces with 5 variables. Biculster 4 represents a cluster of food insecurity areas with the characteristics of the bicluster P0, P1, P2 and calorie consumption of less than 1400 KKAL.

Recognizing Patterns of Food Insecurity in Indonesia New Recommendations for Hunger Prevention Policy

Geographically, Indonesia is a tropical area that has two seasons so it is very suitable for agriculture. Indonesia is also known as an agricultural country. It means that most of its population works in the agricultural sector which is related to food. However, food insecurity still occurs in Indonesia. This is supported by the COVID-19 pandemic in which the Food and Agriculture Organization (FAO) stated that there is a threat of food scarcity during the COVID19 pandemic. This will interfere with the SDGs second goal of ending hunger and sustainable agriculture. The purpose of this study captures the causes of food insecurity in each province in Indonesia spatially using the bicluster method. The data that used is data from Susenas and Sakernas by BPS in 2019. The results of three studies using the bicluster method show that each province has different aspects and letters. The results of this research can serve as a reference for policies in dealing with hunger in Indonesia. This study uses the CC Algorithm method by Cheng and Church. In the bicluster approach, we run some parameters and do manual tunning to choose the best parameter based on the average of residue over volume (MSR/V). CC algorithm tries to get bicluster as large as possible with low MSR Value, therefore the best parameter should be the one that generates the smallest MSR/V and the smallest MSR/V is produced when sigma = 0.3. The application of the CC biclustering algorithm to the poverty structure in Indonesia produces 3 biclusters. Bicluster 1 Consist of 28 Province with 11 Variables, Bicluster 2 consists of 4 Province with 7 Variables, and Bicluster 3 consist of 2 Province with 12 variables. Variable X5 and X6, as absolute food insecurity measurement is included in all bicluster.

A novel framework based on biclustering for automatic epileptic seizure detection

Automatic epileptic seizure detection based on electroencephalogram is crucial to epilepsy diagnosis and treatment. However, the large numbers of time series make it quite challenging to establish a high performance automatic detection method. Considering different physiological states of the brain could be characterized by distinct combinations or interactions of similar discontinuous local temporal patterns, a novel framework based on biclustering for automatic epileptic seizure detection is proposed in this paper. First, the CC algorithm is used to identify similar discontinuous local temporal patterns. Then, the bicluster membership matrix using a new similarity measurement is constructed to reduce the dimensionality. At last, the ELM classifier is adopted to discriminate between epileptic seizure and seizure-free EEG signals. With extensive comparative studies and evaluations on the publicly available Bonn epileptic EEG dataset, it indicates that the proposed framework could not only automatically detect or predict an epilepsy seizure with high performances with respect to accuracy, robustness and efficiency, but also implicitly provide valuable knowledge for studying the mechanisms of epilepsy.

Scheduling parallel and distributed processing for automotive data stream management system

In this paper, to analyze end-to-end timing behavior in heterogeneous processor and network environments accurately, we adopt and modify a heterogeneous selection value on communication contention (HSV_CC) algorithm, which can synchronize tasks and messages simultaneously, for stream processing distribution. In order to adapt the concepts of a static algorithm like HSV_CC to automotive data stream management system (DSMSs), one must first address three issues: (i) previous task and message schedules might lead to less efficient resource usages in this scenario; (ii) the conventional method to determine the task scheduling order may not be best suited to deal with stream processing graphs, and; (iii) there is a need to be able to schedule tasks with time-varying computational requirements efficiently. To address (i), we propose the heterogeneous value with load balancing and communication contention (HVLB_CC) (A) algorithm, which considers load balancing in addition to the parameters considered by the HSV_CC algorithm. We propose HVLB_CC (B) to address issue (ii). HVLB_CC (B) can deal with stream processing task graphs and more various directed acyclic graphs to prevent assigning a higher priority to successor tasks. In addition, to address issue (iii), we propose HVLB_CC_IC. To schedule tasks more efficiently with various computation times, HVLB_CC_IC utilizes schedule holes left in processors. These idle time slots can be used for the execution of an optional part to generate more precise data results by applying imprecise computation models. Experimental results demonstrate that the proposed algorithms improve minimum schedule length, accuracy, and load balancing significantly compared to the HSV_CC algorithm. In addition, the proposed HVLB_CC (B) algorithm can schedule more varied task graphs without reducing performance, and, using imprecise computation models, HVLB_CC_IC yields higher precision data than HVLB_CC without imprecise computation models.

Using clinical algorithms to identify psychosocial distress and other risk factors in cancer survivors.

242 Background: Late effects, such as pain and fatigue, increase a cancer survivors' risk to psychosocial distress. Assessment and identification of distress and other risk factors can be integrated into survivorship care. Yet, clinical tools and processes needed to coordinate these efforts are severely limited. Here we describe the processes and metrics used to assess and document distress in the survivorship care plans (SCPs) of survivors diagnosed with colon and rectal cancers (CC and RC). Methods: Eligibility criteria for this longitudinal analysis included being: ≥ 18 years, diagnosed with a primary colon/rectal cancer, no evidence of disease ≥ 5 years, seen between 09/01/2011 - 8/31/2014, and alive at time of data abstraction. 117 survivors met these criteria and the first 50 sequential cases were included in this sub-analysis. Data were collected at Visits 1, 2, and 3 within specific time parameters. Data sources included the SCP, electronic medical records (EMRs), and CC/RC algorithms. Variables included demographic/clinical characteristics and standards of care (SOC) elements embedded in the algorithm related to distress, fatigue, pain, and social support. Compliance with SOC were measured using a “yes/no” response format. Results: Of 50 survivors, 58% were diagnosed with CC and 42% with RC. 52% were male, 64% Caucasian, 54% age 65-89 years, and 68% were 5-9 years from diagnosis. 52% of CC survivors were Stage IIIA-C or IV and 57% of RC were Stage 1-IIB. Documentation and metrics for each element indicated a total of 111 visits over the course of the analysis, 59% were CC and 42% RC. Provider's compliance rates (PCRs) were: distress (65%), fatigue (32%), pain (68%), and social support (46%). The pain element was not included in the CC algorithm. PCRs for each element varied by site. Evidence that providers followed algorithms' recommdendations to assess survivors current risk of distress was found in the EMRs, SCPs. Conclusions: We found cancer survivorship clinical algorithms and care plans can guide assessment, documentation, and monitoring of psychosocial assessment. Screening for risk factors to distress in cancer survivors can be done in real time and help identify at-risk survivors during their visit.

Estimation of Canopy and Woody Components Clumping Indices at Three Mature Picea crassifolia Forest Stands

Many previous studies have shown that the underestimation of the plant area index (PAI) by traditional optical instruments is mainly due to the clumping effects of forest canopies. However, the evaluation of the clumping effects of canopy (leaves, stems, branches, fruits, and flowers) and woody components (stems and branches) within forest canopies is still a challenging task for current indirect instruments and algorithms. In this study, two key issues of the gap size distribution algorithm (CC) were discussed first, such as the stop conditions of the gap-removal procedure and gap-removing methods. Four types of gap-removing schemes were compared and their impacts to the estimation of canopy and woody components clumping indices (Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> and Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> ) were analyzed. Obvious discrepancies were found between the estimates of Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> and Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> from the four gap-removing schemes at each zenith angle. A new gap-removal scheme was proposed subsequently. After that, a solution was presented to estimate Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> and Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> at multiple zenithal and azimuthal directions in three mature Picea crassifolia forest stands. These estimates were retrieved based on multispectral canopy imager (MCI) equipment and CC algorithm. The estimates from the MCI equipment were later compared with those from the TRAC instrument. Small differences were found between the Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> estimates from the MCI and TRAC instruments, indicating that the MCI instrument was effective for evaluating the clumping effects of forest canopies at multiple zenithal and azimuthal directions. Results showed that both of Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> and Ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> change with the zenithal and azimuthal directions.

Application of Cascade-Correlation Algorithm in Vibration Characteristics of Hydro Turbine

The cascade correlation algorithm that is CC algorithms, CC network structure and CC network weights learning algorithm are introduced, based on the operation data of Wanjiazhai hydropower station, considering the pressure fluctuation, the network model of vibration characteristics is established based on CC algorithm, and the applications of CC and BP algorithm in vibration characteristics of turbine are compared. The results show that the CC algorithm is better than BP neural network, the results can be used in the optimal operation of hydropower, and it has a practical significance.

Multi-Objective Biclustering: When Non-dominated Solutions are not Enough

The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features, and, consequently, to allow the extraction of more accurate information from datasets. Given that biclustering requires the optimization of at least two conflicting objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a few multi-objective evolutionary algorithms for biclustering were proposed in the literature. However, these algorithms only focus their search in the generation of a global set of non-dominated biclusters, which may be insufficient for most of the problems as the coverage of the dataset can be compromised. In order to overcome such problem, a multi-objective artificial immune system capable of performing a multipopulation search, named MOM-aiNet, was proposed. In this work, the MOM-aiNet algorithm will be described in detail, and an extensive set of experimental comparisons will be performed, with the obtained results of MOM-aiNet being confronted with those produced by the popular CC algorithm, by another immune-inspired approach for biclustering (BIC-aiNet), and by the multi-objective approach for biclustering proposed by Mitra & Banka.

A planning comparison of dose patterns in organs at risk and predicted risk for radiation induced malignancy in the contralateral breast following radiation therapy of primary breast using conventional, IMRT and Volumetric modulated arc treatment techniques

Purpose. To investigate the impact of using different radiation therapy techniques on contra-lateral breast (CB) dose, and also dose to other involved organs at risk such as heart and lungs following radiation therapy of breast and regional lymph nodes. Furthermore, to predict the risk for induced malignancies in CB using linear and non linear models. Material and methods. Eight patients with stage II-III breast cancer were included in this analysis. It was focused on three treatment techniques; conventional radiotherapy technique forwardly planed, IMRT and volumetric modulated arc (RapidArc) techniques, inversely planed. The CC algorithm was employed to calculate the standard treatment plans whereas for the IMRT and RapidArc treatment plans AAA algorithm was adopted. The dose results based on mostly DVH analysis were compared. The excess relative risk (ERR) for cancer induction in CB, employed both linear and non-linear models, was estimated. Results. A better homogeneity and conformation in PTV was observed in the RapidArc plans. The highest minimum dose to PTV was observed in the conventional plans while no difference was observed for minimum significant doses D98% and D99% where DX% is the dose received by X% of the PTV volume. In terms of organ sparing, the IMRT and RapidArc plans spare ipsilateral-lung better, but a 40% lower mean dose in the contra-lateral lung in the conventional plans is observed. The mean dose to the contra-lateral breast was lowest for the RapidArc plans as well as the V10Gy and the maximum dose. The mean predicted ERR for the eight patients were lower for the conventional and RA plans than for the IMRT plans assuming a linear dose-risk relationship. The mean predicted ERR when using a non linear model was lower for all the three techniques (with lowest ERR for RapidArc plans). Conclusions. From a clinical perspective, it should be concluded that all three solutions investigated in the study can offer high quality treatment of patients. Further comparative analysis of the two algorithms used in the present study, however, should be performed especially on the peripheral organ dose. The impact of CB exposure to a low-dose radiation on minimizing the risk of radiation induced malignancy in CB can be interpreted differently when using linear or non linear models to predict ERR. In general, no detriment was observed when using RapidArc compared to conventional treatments while a potentially higher risk could be associated to IMRT treatments with fixed gantry.

Analysis of the dose calculation accuracy for IMRT in lung: A 2D approach

The purpose of this study was to compare the dosimetric accuracy of IMRT plans for targets in lung with the accuracy of standard uniform-intensity conformal radiotherapy for different dose calculation algorithms. Tests were performed utilizing a special phantom manufactured from cork and polystyrene in order to quantify the uncertainty of two commercial TPS for IMRT in the lung. Ionization and film measurements were performed at various measuring points/planes. Additionally, single-beam and uniform-intensity multiple-beam tests were performed, in order to investigate deviations due to other characteristics of IMRT. Helax-TMS V6.1(A) was tested for 6, 10 and 25 MV and BrainSCAN 5.2 for 6 MV photon beams, respectively. Pencil beam (PB) with simple inhomogeneity correction and ‘collapsed cone’ (CC) algorithms were applied for dose calculations. However, the latter was not incorporated during optimization hence only post-optimization recalculation was tested. Two-dimensional dose distributions were evaluated applying the γ index concept. Conformal plans showed the same accuracy as IMRT plans. Ionization chamber measurements detected deviations of up to 5% when a PB algorithm was used for IMRT dose calculations. Significant improvement (deviations ∼2%) was observed when IMRT plans were recalculated with the CC algorithm, especially for the highest nominal energy. All γ evaluations confirmed substantial improvement with the CC algorithm in 2D. While PB dose distributions showed most discrepancies in lower (<50%) and high (>90%) dose regions, the CC dose distributions deviated mainly in the high dose gradient (20–80%) region. The advantages of IMRT (conformity, intra-target dose control) should be counterbalanced with possible calculation inaccuracies for targets in the lung. Until no superior dose calculation algorithms are involved in the iterative optimization process it should be used with great care. When only PB algorithm with simple inhomogeneity correction is used, lower energy photon beams should be utilized.

Influence of Calculation Algorithm on Dose Distribution in Irradiation of Non-Small Cell Lung Cancer (NSCLC)

The influence of two different calculation algorithms (“pencil beam” [PB] versus “collapsed cone” [CC]) on dose distribution, as well as the dose-volume histograms (DVHs) of the planning target volume (PTV) and the organs at risk was analyzed for irradiation of lung cancer. Between 10/2001 and 02/2002 three-dimensional treatment planning was done in ten patients with lung cancer (Helax, TMS®, V.6.01). The PTV, the ipsilateral lung (IL) and the contralateral lung (CL) were defined in each axial CT slice (slice thickness 1 cm). Dose distributions for three-dimensional multiple-field technique were calculated using a PB and a CC algorithm, respectively. Normalization was in accordance with ICRU 50. The DVHs were analyzed relating the minimum, maximum, median and mean dose to the volumes of interest (VOI). Median PTV amounted to 774 cm3. Minimum dose within the PTV was 67.4% for CC and 75.6% for PB algorithm (p = 0.04). Using the CC algorithm, only 76.5% of the PTV was included by the 95% isodose, whereas 90.1% was included when the PB algorithm (p = 0.01) was used. Median volume of IL amounted to 1 953 cm3. Mean dose to IL was 43.0% for CC and 44.0% for PB algorithm (p = 0.02). Median volume of IL within the 80% isodose was 19.6% for CC and 24.1% for PB algorithm (p < 0.01). Median volume of CL amounted to 1 847 cm3. Mean dose to CL was 17.4% for CC and 18.1% for PB algorithm (p < 0.01). Volume of CL within the 80% isodose was 3.3% for CC and 4.1% for PB algorithm (p = 0.03). The CC and PB calculation algorithms result in different dose distributions in case of lung tumors. Particularly the minimum dose to the PTV, which may be relevant for tumor control, is significantly lower for CC. Since it is generally accepted that the CC algorithm describes secondary particle transport more exactly than PB models, the use of the latter should be critically evaluated in the treatment planning of lung cancer.

Evaluation of dose calculation algorithms for dynamic arc treatments of head and neck tumors

Background and purpose: To investigate if the Pencil Beam (PB) algorithm takes the disturbance of the dose distribution due to tissue inhomogeneities sufficiently into account in dynamic field shaping rotation therapy (called the dynamic arc treatment modality) for fractionated stereotactic radiation therapy of head and neck tumors. Material and methods: A treatment plan using the dynamic arc treatment modality of an oropharynx lesion on a humanoid phantom was evaluated. The same plan was calculated with three different calculation algorithms: the Clarkson and the PB algorithm (both available on the planning system of the Novalis ® system used for dynamic arc treatments), and the Collapsed Cone Convolution Superposition (CC) algorithm (used by the Pinnacle ® planning system). The three resulting plans are compared using isodose distributions and cumulative dose volume histograms (CDVHs). An intercomparison of the results of the three algorithms was performed to investigate how accurately each of them takes the influence of tissue inhomogeneities into account such as bony structures and air cavities often appearing in the head and neck region. Additionally, the resulting plans were compared with absolute and relative dosimetric measurements of the treatment plan on the humanoid phantom with thermoluminescent detectors and radiographic film, respectively. Results: All calculated dose distributions show a good agreement with the measured distribution except in the planning target volume (PTV) in and at the border of the air cavity. All three algorithms overestimate the dose in the PTV at the boundary with the low-density tissue, with 12, 10 and 7% for the Clarkson, the PB and the CC algorithm, respectively. The correspondence between the calculated dose distributions is reflected in the graphs of the CDVHs. They show similar curves for the PTV and the structures except for the left parotic gland and the myelum. Conclusions: The PB algorithm of the Novalis ® system calculates a treatment plan for the dynamic arc treatment modality adequately for fractionated stereotactic radiation therapy of head and neck tumors, except in the PTV in and at the border of the air cavity where the actual dose is overestimated. Care needs to be taken in clinical cases where it is critical to irradiate the air–tissue boundary to a sufficient dose.