Accuracy Of MLC Research Articles

From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery. A total of 744 "dynalog" files for 23 VMAT plans were extracted randomly from treatment database. Multi-correlation was used to analyzed the potential influences on MLC positional accuracy, including the spatial characteristics and temporal variability of VMAT fluence, and the mechanical wear parameters of MLC. We developed a model to forecast the accuracy of MLC moving position utilizing the random forest (RF) ensemble learning method. Spearman correlation was used to further investigate the associations between MLC positional deviation and dosage deviations as well as gamma passing rates. The MLC positional deviation and effective impact factors show a strong multi-correlation (R=0.701, p-value<0.05). This leads to the development of a highly accurate prediction model with average variables explained of 95.03% and average MSE of 0.059 in the 5-fold cross-validation, and MSE of 0.074 for the test data was obtained. The absolute dose deviations caused by MLC positional deviation ranging from 12.948 to 210.235cGy, while the relative volume deviation remained small at 0.470%-5.161%. The average MLC positional deviation correlated substantially with gamma passing rates (with correlation coefficient of -0.506 to -0.720 and p-value<0.05) but marginally with dosage deviations (with correlation coefficient<0.498 and p-value>0.05). The RF predictive model provides a prior tool for VMAT quality assurance.

Application of the Ion Chamber Array in Magnetic Resonance Accelerator QA

The magnetic resonance accelerator (MR-Linac) is gradually widely used due to high-quality soft tissue contrast and real-time tracking. However, the special dosimetry characteristics and wide field sizes of MR-Linac increase the QA difficulty with conventional measurement method. The purpose of this study was to confirm an ion chamber array could be used for measuring the beam quality, the profiles, as well as the positioning accuracy of all MLC leaves efficiently, by comparing results with the conventional method. To propose a new QA approach for solving the common problem in data acquisition caused by the wide fields of MR-Linac. The research was based on a MR-Linac fixed with 1.5T MR and 7MeV energy photon beam. The conventional QA method adopted the MR water tank with a gantry angle of 0°and an SSD of 133.5 cm, both microdiamond and ionization chamber detector were used to acquire the dose profiles (PDD, inline, crossline and diagonal). Field sizes 1 × 1 cm2, 2 × 2 cm2, 3 × 3 cm2, 5 × 5 cm2, 10 × 10 cm2, 15 × 15 cm2, 22 × 22 cm2, 40 × 22 cm2,57 × 22 cm2 were measured with depth 13mm, 50mm, 100mm for vertical beam. As for the wide fields (larger than 15 × 15 cm2), two profiles of x axis (one from left to right, the other from right to left) needed to be gathered and then stitched into one final profile. A boot phantom with an ionization chamber detector was used for measuring beam quality. We defined the profiles measured by conventional method as the baseline. An ion chamber array was adopted to acquire TPR, PDD, profiles and MLC positioning, comparing to the conventional method. The center of ion chamber array was placed to the isocenter of MR-Linac, the array could move to the right and left offset positions through engaging the pin into correct hole of QA platform, such 'once positioning and twice movements' operation could finish within 3 minutes. The central detector of the ion chamber array was used for measuring beam quality. TPRs for different depths were acquired by stacking solid water on the ion chamber array. As for the profiles, we could get the final profile by 'once positioning and twice movements' efficiently. As for the positioning accuracy of MLC leaves, firstly the central leaf pair was put on y = 0 to measure 'open profile' under the open field. Then we moved the MLC leaves to different positions to get the n profile (n for different leaf positions). The ratio of n profile to open profile could show the positioning accuracy of MLC. We adopted 2D gamma (1mm / 2%) to compare the profiles between the ion chamber array and the conventional method, the results were within 98%. The beam quality consistency of ion chamber array comparing to the wedge tank was within 1% according to daily measurement. The ion chamber array could reflect the MLC positioning differences, the sensitivity was 0.5 mm. The ion chamber array showed a convenient QA method both for the dosimetry and for the MLC positioning accuracy which did reduce the overall measurement time, it was recommended for daily and monthly QA for MR-Linac.

Machine learning-based diagnostic method of pre-therapeutic 18F-FDG PET/CT for evaluating mediastinal lymph nodes in non-small cell lung cancer.

We aimed to find the best machine learning (ML) model using 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for evaluating metastatic mediastinal lymph nodes (MedLNs) in non-small cell lung cancer, and compare the diagnostic results with those of nuclear medicine physicians. A total of 1329 MedLNs were reviewed. Boosted decision tree, logistic regression, support vector machine, neural network, and decision forest models were compared. The diagnostic performance of the best ML model was compared with that of physicians. The ML method was divided into ML with quantitative variables only (MLq) and adding clinical information (MLc). We performed an analysis based on the 18F-FDG-avidity of the MedLNs. The boosted decision tree model obtained higher sensitivity and negative predictive values but lower specificity and positive predictive values than the physicians. There was no significant difference between the accuracy of the physicians and MLq (79.8% vs. 76.8%, p = 0.067). The accuracy of MLc was significantly higher than that of the physicians (81.0% vs. 76.8%, p = 0.009). In MedLNs with low 18F-FDG-avidity, ML had significantly higher accuracy than the physicians (70.0% vs. 63.3%, p = 0.018). Although there was no significant difference in accuracy between the MLq and physicians, the diagnostic performance of MLc was better than that of MLq or of the physicians. The ML method appeared to be useful for evaluating low metabolic MedLNs. Therefore, adding clinical information to the quantitative variables from 18F-FDG PET/CT can improve the diagnostic results of ML. • Machine learning using two-class boosted decision tree model revealed the highest value of area under curve, and it showed higher sensitivity and negative predictive values but lower specificity and positive predictive values than nuclear medicine physicians. • The diagnostic results from machine learning method after adding clinical information to the quantitative variables improved accuracy significantly than nuclear medicine physicians. • Machine learning could improve the diagnostic significance of metastatic mediastinal lymph nodes, especially in mediastinal lymph nodes with low 18F-FDG-avidity.

Study on Urban Remote Sensing Classification Based on Improved RBF Network and Normalized Difference Indexes

Aiming at the complexity of ground objects in urban area, and the difficulty in distinguishing ground objects using spectral characteristics, we extracted normalized different indexes, namely Modified Normalized Difference Water Index (MNDWI), Soil Adjusted Vegetation Index (SAVI ) and Normalized Difference Building Index (NDBI) , as the key auxiliary information for land use classification of urban area. To solve problems of RBF neural network, such as local minimum values and discrete output value in output layer, we used max-min distance means to initialize RBF center, and introduced equilibrium factor into Gauss function to improve RBF neural network learning algorithm. On this basis, a new urban area classification model was proposed based on improved RBF network and normalized difference indexes. At last, NanChong city in SiChuan province of China was taken as the study area, and TM images was used as experiment data to test the model proposed in this paper. The results showed that, based on the improved RBF network, with the help of spectral band information, the classification overall accuracy was 89.97%, Kappa coefficient was 0.88; using both spectral band information and normalized difference indexes, the classification overall accuracy was 95.02%, Kappa coefficient is 0.94, the classification overall accuracy was improved by 5.05%. Also, the experiment results showed that, with the help of spectral band information and normalized difference indexes, the classification overall accuracy of MLC, BP and improved RBF network was 90.12%, 93.63%, 95.02%, respectively, which means RBF has an advantage of fusing geological parameters in classification.

SU‐E‐P‐36: Evaluation of MLC Positioning Errors in Dynamic IMRT Treatments by Analyzing Dynalog Files

Purpose:To assess the accuracy of MLC positioning in Varian linear accelerator, in dynamic IMRT technique, from the analysis of dynalog files generated by the MLC controller.Methods:In Clinac accelerators (pre‐TrueBeam technology), control system has an approximately 50ms delay (one control cycle time). Then, the system compares the measured position to the planned position corresponding to the next control cycle. As it has been confirmed by Varian technical support, this effect causes that measured positions appear in dynalogs one cycle out of phase with respect to the planned positions. Around 9000 dynalogs have been analyzed, coming from the three linear accelerators of our center (one Trilogy and two Clinac 21EX) equipped with a Millennium 120 MLC. In order to compare our results to recent publications, leaf positioning errors (RMS and 95th percentile) are calculated with and without delay effect. Dynalogs have been analyzed using a in‐house Matlab software.Results:The RMS errors were 0.341, 0.339 and 0.348mm for each Linac; being the average error 0.343 mm. The 95th percentiles of the error were 0.617, 0.607 and 0.625; with an average of 0.617mm. A recent multi‐institution study carried out by Kerns et al. found a mean leaf RMS error of 0.32mm and a 95th percentile error value of 0.64mm.Without delay effect, mean leaf RMS errors obtained were 0.040, 0.042 and 0.038mm for each treatment machine; being the average 0.040mm. The 95th percentile error values obtained were 0.057, 0.058 and 0.054 mm, with an average of 0.056mm.Conclusion:Results obtained for the mean leaf RMS error and the mean 95th percentile were consistent with the multi‐institution study. Calculated error statistics with delay effect are significantly larger due to the speed proportional and systematic leaf offset. Consequently it is proposed to correct this effect in dynalogs analysis to determine the MLC performance.

Correlation of phantom-based and log file patient-specific QA with complexity scores for VMAT.

The motivation for this study was to reduce physics workload relating to patient‐specific quality assurance (QA). VMAT plan delivery accuracy was determined from analysis of pre‐ and on‐treatment trajectory log files and phantom‐based ionization chamber array measurements. The correlation in this combination of measurements for patient‐specific QA was investigated. The relationship between delivery errors and plan complexity was investigated as a potential method to further reduce patient‐specific QA workload. Thirty VMAT plans from three treatment sites — prostate only, prostate and pelvic node (PPN), and head and neck (H&N) — were retrospectively analyzed in this work. The 2D fluence delivery reconstructed from pretreatment and on‐treatment trajectory log files was compared with the planned fluence using gamma analysis. Pretreatment dose delivery verification was also carried out using gamma analysis of ionization chamber array measurements compared with calculated doses. Pearson correlations were used to explore any relationship between trajectory log file (pretreatment and on‐treatment) and ionization chamber array gamma results (pretreatment). Plan complexity was assessed using the MU/ arc and the modulation complexity score (MCS), with Pearson correlations used to examine any relationships between complexity metrics and plan delivery accuracy. Trajectory log files were also used to further explore the accuracy of MLC and gantry positions. Pretreatment 1%/1 mm gamma passing rates for trajectory log file analysis were 99.1% (98.7%–99.2%), 99.3% (99.1%–99.5%), and 98.4% (97.3%–98.8%) (median (IQR)) for prostate, PPN, and H&N, respectively, and were significantly correlated to on‐treatment trajectory log file gamma results (R=0.989,p<0.001). Pretreatment ionization chamber array (2%/2 mm) gamma results were also significantly correlated with on‐treatment trajectory log file gamma results (R=0.623,p<0.001). Furthermore, all gamma results displayed a significant correlation with MCS (R>0.57,p<0.001), but not with MU/arc. Average MLC position and gantry angle errors were 0.001±0.002mm and 0.025°±0.008° over all treatment sites and were not found to affect delivery accuracy. However, variability in MLC speed was found to be directly related to MLC position accuracy. The accuracy of VMAT plan delivery assessed using pretreatment trajectory log file fluence delivery and ionization chamber array measurements were strongly correlated with on‐treatment trajectory log file fluence delivery. The strong correlation between trajectory log file and phantom‐based gamma results demonstrates potential to reduce our current patient‐specific QA. Additionally, insight into MLC and gantry position accuracy through trajectory log file analysis and the strong correlation between gamma analysis results and the MCS could also provide further methodologies to both optimize the VMAT planning and QA process.PACS number: 87.53.Bn, 87.55.Kh, 87.55.Qr

SU-E-T-146: Evaluation of MLC QA Software to Determine MLC Accuracy From EPID Images

Purpose: To determine the clinical usefulness of commercially available software tools to evaluate MLC accuracy for setting appropriate benchmark levels for mean and maximum leaf deviation errors and FWHM values. Methods: The RIT Picket Fence and Varian Rapidarc Test 1.1 (from the Rapidarc Acceptance Test) analysis routines were used to analyze EPID images delivered on Trilogy and TrueBeam accelerators over a 2 month period. Profiles are automatically detected across the picket fence pattern at each leaf pair. The leaf pair position is the mid-point between the two half-max positions. The FWHM is the distance between the two half-max positions. Maximum, mean and standard deviation statistics are calculated for the 10 openings for each leaf pair in the picket fence pattern. Process control limits (UCL, LCL) were calculated for the mean leaf position deviation and FWHM for both linacs. Results: For the RIT Picket Fence test the mean leaf position deviation was 0.017 mm with a standard deviation of 0.005mm with UCL=0.030 mm and LCL=0.003 mm (UCL-LCL=0.027 mm) for the TrueBeam accelerator and 0.013 mm with a standard deviation of 0.021 mm for the Trilogy accelerator with UCL=0.0423 mm and LCL=−0.069 mm (UCL-LCL=0.122 mm). For the Rapidarc Test the mean leaf position deviation was −0.197 mm with a standard deviation of 0.020 mm for the TrueBeam and 0.102 mm with a standard deviation of 0.026 mm for the Trilogy. The mean of the measured maximum leaf position deviations was <0.2mm for both accelerators for the RIT Picket Fence test and <0.5 mm for the Rapidarc test. Conclusion: This work indicates the potential for quantifying MLC performance with simple test patterns delivered to an EPID and analyzed. Appropriate data analysis and assignment of control limits quantitatively highlights differences between linacs and allows a prospective intervention by physicists or service engineers.

SU-E-T-129: An EPID-Based Method for Testing Accuracy of MLC and Backup Jaws

Purpose: Current methods for QA of MLC leaf position are limited to measurements relative to an arbitrary reference position. There also are no reported methods for testing accuracy of backup jaw position as recommended by AAPM Task Group 142. The purpose of this project was to develop an efficient method for testing accuracy of MLC and backup jaws relative to the mechanical isocenter. Methods: A set of four EPID images is used to determine the position of backup jaws and MLC leaves relative to the mechanical isocenter. The first pair of images are of a rectangular field that is rotated 180 degrees between the two images to locate the isocenter. The third image, consisting of three field strips defined by backup jaws, allows for determination of the position of the backup jaws across its range of travel relative to a line passing through the mechanical isocenter and parallel to the jaws. The fourth image, a set of three strips defined by the MLC, allows for determination of the position of each leaf relative to the isocenter reference line determined from the backup jaw image. A software routine was developed to efficiently analyze the images. The accuracy of the test results was validated with water phantom scans. Results: The test can be completed in less than 10 minutes. The software tool reliably and automatically reports, over a 20‐cm range of travel, jaw setback for each leaf, leaf position and jaw position deviation from nominal, and provides visual displays to facilitate rapid qualitative interpretation. The test was used to guide adjustment of MLC and backup jaw calibration of two matched linacs. Conclusion: This work demonstrates a method to efficiently and accurately measure MLC leaf and backup jaw position relative to the mechanical isocenter of the collimator.

Initial experience with TrueBeam trajectory log files for radiation therapy delivery verification

PurposeTraditionally, initial and weekly chart checks involve checking various parameters in the treatment management system against the expected treatment parameters and machine settings. This process is time-consuming and labor intensive. We explore utilizing the Varian TrueBeam log files (Varian Medical System, Palo Alto, CA), which contain the complete delivery parameters for an end-to-end verification of daily patient treatments. Methods and MaterialsAn in-house software tool for 3-dimensional (3D) conformal therapy, enhanced dynamic wedge delivery, intensity modulated radiation therapy (IMRT), volumetric modulated radiation therapy, flattening filter-free mode, and electron therapy treatment verification was developed. The software reads the Varian TrueBeam log files, extracts the delivered parameters, and compares them against the original treatment planning data. In addition to providing an end-to-end data transfer integrity check, the tool also verifies the accuracy of treatment deliveries. This is performed as part of the initial chart check for IMRT plans and after first fraction for the 3D plans. The software was validated for consistency and accuracy for IMRT and 3D fields. ResultsBased on the validation results the accuracy of MLC, jaw and gantry positions were well within the expected values. The patient quality assurance results for 127 IMRT patients and 51 conventional fields were within 0.25 mm for multileaf collimator positions, 0.3 degree for gantry angles, 0.13 monitor units for monitor unit delivery accuracy, and 1 mm for jaw positions. The delivered dose rates for the flattening filter-free modes were within 1% of the planned dose rates. ConclusionsThe end-to-end data transfer check using TrueBeam log files and the treatment delivery parameter accuracy check provides an efficient, reliable beam parameter check process for various radiation delivery techniques.

SU‐E‐T‐352: Commissioning and Quality Assurance of the First Commercial Hybrid MRI‐IMRT System

Purpose: To describe the commissioning process for the first installed commercial MRI‐guided IMRT system (ViewRay, Village of Oakwood, OH) and outline quality assurance methods for this novel treatment modality. Methods: The ViewRay™ System (510(k) pending) consists of a 0.35‐T double‐doughnut MRI coupled with a gantry that houses three Co‐60 sources,each with an activity up to 15,000 Ci (120° apart). IMRT delivery is enabled by doubly‐focused MLCs that serve as the only beam‐shaping collimators for each head, allowing a maximum field size of 27.3 cm2 at the 105‐cm isocenter. MRI imaging is used prior to and during delivery for setup evaluation, adaptive radiotherapy, and gating. The challenges in commissioning as well as periodic and patient‐specific QA arise due to the presence of the magnetic field, unique geometry of this device which is not compatible with many of conventional RT QA devices and techniques, and the penumbra of a 2‐cm wide Co‐60 source. The following devices were used for commissioning and quality assurance tests: radiochromic and radiographic film, ionization chambers (Exradin A12 &amp; A16), Sun Nuclear's IC Profiler (Melbourne, FL), and a water tank. Tests were conducted to evaluate the beam profiles, penumbra, and PDDs. Also, tests to check MLC accuracy and reproducibility were evaluated. Results: Tests were developed to validate geometric performance of the device including a comprehensive set of MLC tests. Due to the low strength of the magnetic field, the mean free path of electrons in the ionization chamber volume is too long to have a noticeable curvature; therefore the magnetic field is not expected to have a noticeable effect on dose measurement. Conclusions: While the presence of the magnetic field limited the choice of QA devices, it was found that satisfactory methods for MRI‐IMRT machine QA exist and can be successfully employed. Drs. Green, Goddu, and Mutic served as scientific consultants for ViewRay, Inc. Dr. Mutic is on the clinical focus group for ViewRay, Inc., and his spouse holds shares in ViewRay, Inc.

SU‐E‐T‐267: A Comprehensive QA Method of MLC Accuracies for Dynamic IMRT

Purpose: The purpose of this study was to investigate the usefulness of our original quality assurance (QA) program and patientsˈ treatment of MLC data analysis for dynamic IMRT as routine practice. Method and Materials: As to the QA program, we have made three MLC sweep test patterns by changing MLC travel speed of 0.5, 1.0 and 2.0 cm/sec, respectively. All three fields were irradiated by three gantry angles of 0, 90 and 270 degree as daily QA. Each MLC logs of the QA program and patientsˈ treatment were recorded during Aug 2009 to Nov 2010. Actual MLC position errors (Bank A, Bank B) and gap errors compared with planned position were analyzed by a program (Matlab, Cybernet Inc.). Systematic and random errors of each MLC and gap were determined by everyday and every month. And then, the temporal data were also evaluated. Results: The accuracy of MLC position was depended upon its travel speed and gantry angle. Our data showed that the MLC errors in the QA program got worse as twice if the gantry angle was 270 deg compared to other angles. From patientsˈ treatment data analysis, we could monitor the accuracy of MLC during the whole treatment courses. Conclusion: In this study, we have found that our original QA program and patientsˈ treatment of MLC data analysis could be useful for clinical routine MLC QA for dynamic IMRT to identify MLC position accuracies by time‐line.

SU-E-T-484: Impact of Multileaf Collimator Leaf Positioning Accuracy on Intensity Modulated Radiation Therapy

Purpose: It is reported to have an impact on dose due to uncertainty of MLC drive control in IMRT using MLC. It is reported to have displacement of about 0.5mm in a month as changes over time of MLC drive control accuracy installed in LINAC made by SIEMENS. There is fear that these changes over time contribute to dose distribution when SMLC‐IMRT is practiced. Methods: For LINAC, We used PRIMUS High‐Energy KD2 7467 (Siemens Medical Systems) that generates 10MV‐X rays. MLC installed in this therapy machine is MLC‐20A (Toshiba Medical Systems) with lower collimator replaced by MLC of 29 pairs and adopts double focus that does focusing with two aspects in a structure of MLC's leaf tip and side contacting always parallel to dose angle. We used Kodak Extended Dose Range2 (Carestream health Inc.) for film, D.D.system (R‐TECH Inc.) for film analyzer, flat bed scanner ES‐10000G (EPSON Corp.) for film reader and Xio‐version4.50.00 (ELEKTA) for RTP. We studied the impact of MLC drive control accuracy on dose evaluation (gamma analysis) measuring IMRTdose distribution as well as evaluating MLC drive control accuracy (resting positional accuracy and position reproducibility) once a week for 60 days. Results: MLC positional accuracy tended to expand by 0.1–0.15mm in one week accompanied by changes over time and tended to expand by about 1mm in 60 days. The reproducibility was within 0.2mm for roughly over 95%. For prostate gland IMRT, I did not see a significant difference in pass rate of y analysis if the resting positional accuracy of MLC is about 1 mm. Conclusions: It was suggested that it would be an effective index to continue IMRT safely in the future by practicing regular management upon setting an acceptable value by MLC positional accuracy test. This study was supported, in part, by a grant of the Japanese Society of Radiologocal Technology(JSRT)

SU‐GG‐T‐305: Evaluation of Some Essential Dosimetric Parameters for Beam Matching of Similar Accelerators

Purpose: To dosimetrically evaluate beam matching procedure of two similar accelerators using gamma index. Method and Materials: The second accelerator was beam matched with first one using Vendor's acceptance test (ATP) protocol. Depth dose and inline‐crossline profiles were matched within ±1%. In order to quantify the level of agreement between the matched beams we performed a set of measurements including total scatter, collimator scatter, wedge transmission and absolute dose measurement. The ATP specifications are at particular points on the ionization curve for depth dose or profile. Gamma was used to compare planar dose distribution from EDR2 films exposed on both units. For 6MV photon we exposed a film for PDD (10×10cm), beam profile (5,10 and 20cm), a pyramid shape, to compare dosimetric and positional accuracy of MLC a pattern of strips with different MUs at different positions and a diamond shape was exposed. We also exposed a film at tray level to a segmented IMRT field to compare collimator scatter. To account for TPS calculations a film kept in axial plane was exposed to 3DCRT and IMRT plan with actual gantry angles. Absolute dose was measured simultaneously using point chamber. Results: Total scatter, collimator scatter and wedge transmission factors were within ±1%. The absolute dose for 3DCRT and IMRT plan was within ±1% of reference value and within ±3% of TPS value. EDR2 films exposed on both units were analyzed using OmniPro IMRT software. Gamma was calculated for different delta dose and delta distance. The analysis has shown that all these comparisons were in good agreement for delta dose of 2% and delta distance of 2mm. Conclusion: All these tests were done at the commissioning time. We are periodically checking few parameters like PDD, beam profile and MLC accuracy to assure the consistency in beam matching

SU‐GG‐T‐157: Commission of 2.5 Mm ModuLeaf MLC for Stereotactic Radiation Therapy

Purpose: Improvements of MLC technology in radiotherapy, including smaller leaf sizes, allow greater accuracy in delivering radiation doses to the target with better conformality. For example, the high ablative doses used in hypo‐fractionated stereotactic body radiotherapy (SBRT) require high precision in dose delivery, thus high precision in commissioning the MLC system to verifiy MLC accuracy. The purpose of this study was to evaluate the procedure and challenges in the commissioning and application of 2.5‐mm small‐leaf MLC. Method and Materials: Siemens' newly‐developed Moduleaf MLC (MMLC) with 2.5mm leaf‐size was commissioned for SBRT. Step‐by‐step QA and application procedures for SBRT commission guidelines were followed. Error tolerance and QA results were analyzed through the entire commission process from beam data preparation, to treatment planning system commission, to treatment plan verification. The commission was divided into 3 phases: mechanical commission, software commission, and comprehensive commission. Isocenter accuracy was assured for mechanical precision. For software commission, the effect of small field factor was analyzed. In comprehensive phase, a single field and a head‐and‐neck case were tested with the QA standard. Results: The commission and clinical implementation of 2.5mm MMLC were described in this study, with provided guidelines for QA procedures from beam data collection and modeling to treatment planning methodology. The accuracy of the MLC mechanic isocenter was within 0.2 mm. At the same time, the treatment modalities with or without the flatten filter were compared, and they were consistent to each other except for the dose‐rate effect. Different MMLC clinical protocols, from SBRT to head‐and‐neck and intracranial treatments, have been suggested. Conclusion: After strictly following the physical QA requirements, we successfully commissioned the new mounted‐on 2.5mm MMLC for SRS/SBRT. In the follow‐up studies, clinical applications with MMLC will be further compared to other on‐site SRS systems, including Gamma‐Knife, and Tomotherapy system.

Identification of internal defect of sugar maple logs from CT images using supervised classification methods

Sugar maple logs (Acer saccharum Marsh.) were scanned using X-ray medical scanner in order to develop a classification procedure for this type of imagery (CT images). The classification procedure was required in order to separate sapwood from colored heartwood, knots, rot and bark. Five logs coming from a freshly cut tree (group 1) and three logs sampled from a sawmill yard (group 2) were chosen for this purpose. Two parametric supervised classification algorithms, a minimum distance (MDC) and maximum likelihood (MLC) ones, were qualitatively and quantitatively tested and the resulting thematic maps were filtered by a 5×5 median filter. The classification accuracy was evaluated with confusion matrix and Kappa analyses. Sapwood is known to be the key factor determining sugar maple lumber value. The sapwood identification accuracy was found to be 98.6% (MDC) and 97.2% (MLC) for group 1 and 80.7% (MDC) and 81.8% (MLC) for group 2, respectively. Misclassification of defects occurred mainly between knots and colored heartwood. The overall accuracy of classification was about 83.1% (MDC) and 82.6% (MLC) for group 1 and 76.4 (MDC) and 78.0% (MLC) for group 2, respectively. The Kappa value from MDC and MLC was 0.622 and 0.624 for group 1 and 0.440 and 0.470 for group 2, respectively. These Kappa values indicate the existence of strong and moderate degree of conformity between the reference data and the classification procedure for groups 1 and 2 of logs, respectively. Both classifiers show no statistically significant differences in their capability of separation of sapwood from the other classes. Nevertheless, as MLC accuracy for colored heartwood is higher than MDC accuracy in logs without bark (normal situation in sawmills), MLC appears at this stage as the better alternative for analysing CT images of sugar maple logs.