MR-linac MLC positioning QA by digitally stitching dual double-exposed films.

PURPOSE: The picket fence (PF) test is highly recommended for multi-leaf collimator (MLC) quality assurance. However, since the electronic portal imaging device (EPID) on the Elekta Unity only covers a small area, it is not feasible to perform the PF test for the entire MLC. Here, we propose a technique for the PF test by stitching two double-exposed films. METHODS: Two EBT3 films were used to encompass the entire MLC, with each one covering one half of the area. Two fields were employed to apply double exposure: a PF pattern consisting of 11 2 mm wide pickets and a 2.84 cm x 22 cm open field. The edges of the open field defined by the diaphragms were used to correct film rotation as well as align them horizontally. The PF pattern was also measured with the EPID where the pickets were used to align the films vertically. Individual leaf positions were detected on the merged film for quantitative analysis. Various MLC positioning errors were introduced to evaluate the technique's sensitivity. RESULTS: The merged films covered 72 leaf pairs properly (four leaf pairs on both sides were outside the treatment couch). With the EPID, the leaf positioning accuracy was -0.02 ± 0.07 mm (maximum: 0.29 mm) and the picket width variation was 0.00 ± 0.03 mm (maximum: 0.11 mm); with the films, the position accuracy and width variation were -0.03 ± 0.13 mm (maximum: 0.80 mm) and 0.00 ± 0.13 mm (maximum: 0.74 mm), respectively. The EPID was able to detect errors of 0.5 mm or above with submillimeter accuracy; the films were only able to detect errors > 1.0 mm. CONCLUSION: We developed a quantitative technique for the PF test on the Elekta Unity. The merged films covered nearly the entire MLC leaf banks. The technique exhibited clinically acceptable accuracy and sensitivity to MLC positioning errors.

Reconstruction of volume averaging effect-free continuous photon beam profiles from discrete ionization chamber array measurements using a machine learning technique.

PURPOSE: The use of the ionization chamber array ICProfiler (ICP) is limited by its relatively poor detector spatial resolution and the inherent volume averaging effect (VAE). The purpose of this work is to study the feasibility of reconstructing VAE-free continuous photon beam profiles from ICP measurements with a machine learning technique. METHODS: In- and cross-plane photon beam profiles of a 6 MV beam from an Elekta linear accelerator, ranging from 2 × 2 to 10 × 10 cm2 at 1.5 cm, 5 cm, and 10 cm depth, were measured with an ICP. The discrete measurements were interpolated with a Makima method to obtain continuous beam profiles. Artificial neural networks (ANNs) were trained to restore the penumbra of the beam profiles. Plane-specific (in- and cr-plane) ANNs and a combined ANN were separately trained. The performance of the ANNs was evaluated using the penumbra width difference (PWD, the difference between the penumbra widths of the reconstructed and the reference profile). The plane-specific and the combined ANNs were compared to study the feasibility of using a single ANN for both in- and cross-plane. RESULTS: The profiles reconstructed with all the ANNs had excellent agreement with the reference. For in-plane, the ANNs reduced the PWD from 1.6 ± 0.7 mm at 1.5 cm depth to 0.1 ± 0.1 mm, from 1.8 ± 0.6 mm at 5.0 cm depth to 0.1 ± 0.1 mm, and from 2.4 ± 0.1 mm at 10.0 cm depth to 0.0 ± 0.0 mm; for cross-plane, the ANNs reduced the PWD from 1.2 ± 0.4 mm at 1.5 cm depth, 1.2 ± 0.3 mm at 5.0 cm depth, and 1.6 ± 0.1 mm at 10.0 cm depth, to 0.1 ± 0.1 mm. CONCLUSIONS: This study demonstrated the feasibility of using simple ANNs to reconstruct VAE-free continuous photon beam profiles from discrete ICP measurements. A combined ANN can restore the penumbra of in- and cross-plane beam profiles of various fields at different depths.

Corrections of photon beam profiles of small fields measured with ionization chambers using a three-layer neural network.

The purpose of this work is to study the feasibility of photon beam profile deconvolution using a feedforward neural network (NN) in very small fields (down to 0.56 × 0.56 cm2 ). The method's independence of the delivery and scanning system is also investigated. Lateral beam profiles of photon fields between 0.56 × 0.56 cm2 and 4.03 × 4.03 cm2 were collected on a Siemens Artiste linear accelerator. Three scanning ionization chambers (SNC 125c, PTW 31021, and PTW 31022) of sensitive volumes ranging from 0.016 cm3 to 0.108 cm3 were used with a PTW MP3 water phantom. A reference dataset was also collected with a PTW 60019 microDiamond detector to train and test individual NNs for each ionization chamber. Further testing of the trained NNs was performed with additional test data collected on an Elekta Synergy linear accelerator using a Sun Nuclear 3D Scanner. The results were evaluated with a 1D gamma analysis (0.5 mm/0.5%). After the deconvolution, the gamma passing rates increased from 54.79% to 99.58% for the SNC 125c, from 57.09% to 99.83% for the PTW 31021, and from 91.03% to 96.36% for the PTW 31022. The delivery system, the scanning system, the scanning mode (continuous vs. step-by-step), and the electrometer had no significant influence on the results. This study successfully demonstrated the feasibility of using NN to correct the beam profiles of very small photon fields collected with ionization chambers of various sizes. Its independence of the delivery and scanning system was also shown.

Evaluation of a neural network-based photon beam profile deconvolution method.

PURPOSE: The authors have previously shown the feasibility of using an artificial neural network (ANN) to eliminate the volume average effect (VAE) of scanning ionization chambers (ICs). The purpose of this work was to evaluate the method when applied to beams of different energies (6 and 10 MV) and modalities [flattened (FF) vs unflattened (FFF)], measured with ICs of various sizes. METHODS: The three-layer ANN extracted data from transverse photon beam profiles using a sliding window, and output deconvolved value corresponding to the location at the center of the window. Beam profiles of seven fields ranging from 2 × 2 to 10 × 10 cm2 at four depths (1.5, 5, 10 and 20 cm) were measured with three ICs (CC04, CC13, and FC65-P) and an EDGE diode detector for 6 MV FF and FFF. Similar data for the 10 MV FF beam was also collected with CC13 and EDGE. The EDGE-measured profiles were used as reference data to train and test the ANNs. Separate ANNs were trained by using the data of each beam energy and modality. Combined ANNs were also trained by combining data of different beam energies and/or modalities. The ANN's performance was quantified and compared by evaluating the penumbra width difference (PWD) between the deconvolved and reference profiles. RESULTS: Excellent agreement between the deconvolved and reference profiles was achieved with both separate and combined ANNs for all studied ICs, beam energies, beam modalities, and geometries. After deconvolution, the average PWD decreased from 1-3 mm to under 0.15 mm with separate ANNs and to under 0.20 mm with combined ANN. CONCLUSIONS: The ANN-based deconvolution method can be effectively applied to beams of different energies and modalities measured with ICs of various sizes. Separate ANNs yielded marginally better results than combined ANNs. An IC-specific, combined ANN can provide clinically acceptable results as long as the training data includes data of each beam energy and modality.

A self-checking treatment couch coordinate calculation system in radiotherapy.

PURPOSE: Traditionally, the treatment couch coordinates (TCCs) for patients undergoing radiotherapy can only be determined at the time of treatment, placing pressure on the treating therapists and leaving several pathways for errors such as wrong-site treatment or wrong treatment table shift from a reference point. The purpose of this work is to propose an accurate, robust, and streamlined system that calculates TCC in advance. METHODS: The proposed system combines the advantages of two different calculation methods that use an indexed immobilization device. The first method uses an array of reference ball bearings (BBs) embedded in the CT scanner's couch-top. To obtain the patient-specific TCC, the spatial offset of the treatment planning isocenter from the reference BB is used. The second method performs a calculation using the one-to-one mapping relationship between the CT scanner's DICOM (Digital Imaging and Communications in Medicine) coordinate system and the TCC system. Both methods use a reference point in the CT coordinate system to correlate a point in the TCC system to perform the coordinate transfer between the two systems. Both methods were used to calculate the TCC and the results were checked against each other, creating an integrated workflow via automated self-checking. The accuracy of the calculation system was retrospectively evaluated with 275 patients, where the actual treatment position determined with cone-beam CT was used as a reference. RESULTS: An efficient workflow transparent to the therapists at both CT simulation and treatment was created. It works with any indexed immobilization device and can be universally applied to all treatment sites. The two methods had comparable accuracy, with 95% of the calculations within 3 mm. The inter-fraction variation was within ± 1.0 cm for 95% of the coordinates across all the treatment sites. CONCLUSIONS: A robust, accurate, and streamlined system was implemented to calculate TCCs in advance. It eases the pressure on the treating therapists, reduces patient setup time, and enhances the patient safety by preventing setup errors.

Beam flatness modulation for a flattening filter free photon beam utilizing a novel direct leaf trajectory optimization model.

Flattening filter free (FFF) linear accelerators produce a fluence distribution that is forward peaked. Various dosimetric benefits, such as increased dose rate, reduced leakage and out of field dose has led to the growth of FFF technology in the clinic. The literature has suggested the idea of vendors offering dedicated FFF units where the flattening filter (FF) is removed completely and manipulating the beam to deliver conventional flat radiotherapy treatments. This work aims to develop an effective way to deliver modulated flat beam treatments, rather than utilizing a physical FF. This novel optimization model is an extension of the direct leaf trajectory optimization (DLTO) previously developed for volumetric modulated radiation therapy (VMAT) and is capable of accounting for all machine and multileaf collimator (MLC) dynamic delivery constraints, using a combination of linear constraints and a convex objective function. Furthermore, the tongue and groove (T&G) effect was also incorporated directly into our model without introducing nonlinearity to the constraints, nor nonconvexity to the objective function. The overall beam flatness, machine deliverability, and treatment time efficiency were assessed. Regular square fields, including field sizes of 10 × 10 cm2 to 40 × 40 cm2 were analyzed, as well as three clinical fields, and three arbitrary contours with "concave" features. Quantitative flatness was measured for all modulated FFF fields, and the results were comparable or better than their open FF counterparts, with the majority having a quantitative flatness of less than 3.0%. The modulated FFF beams, due to the included efficiency constraint, were able to achieve acceptable delivery time compared to their open FF counterpart. The results indicated that the dose uniformity and flatness for the modulated FFF beams optimized with the DLTO model can successfully match the uniformity and flatness of their conventional FF counterparts, and may even provide further benefit by taking advantage of the unique FFF beam characteristics.

Discovering Suspicious APT Behaviors by Analyzing DNS Activities.

As sensors become more prevalent in our lives, security issues have become a major concern. In the Advanced Persistent Threat (APT) attack, the sensor has also become an important role as a transmission medium. As a relatively weak link in the network transmission process, sensor networks often become the target of attackers. Due to the characteristics of low traffic, long attack time, diverse attack methods, and real-time evolution, existing detection methods have not been able to detect them comprehensively. Current research suggests that a suspicious domain name can be obtained by analyzing the domain name resolution (DNS) request to the target network in an APT attack. In past work based on DNS log analyses, most of the work would simply calculate the characteristics of the request message or the characteristics of the response message or the feature set of the request message plus the response message, and the relationship between the response message and the request message was not considered. This may leave out the detection of some APT attacks in which the DNS resolution process is incomplete. This paper proposes a new feature that represents the relationship between a DNS request and the response message, based on a deep learning method used to analyze the DNS request records. The algorithm performs threat assessment on the DNS behavior to be detected based on the calculated suspicious value. This paper uses the data of 4, 907, 147, 146 DNS request records (376, 605, 606 records after DNS Data Pre-processing) collected in a large campus network and uses simulation attack data to verify the validity and correctness of the system. The results of the experiments show that our method achieves an average accuracy of 97.6% in detecting suspicious DNS behavior, with the orange false positive (FP) at 2.3% and the recall at 96.8%. The proposed system can effectively detect the hidden and suspicious DNS behavior in APT.

AULD: Large Scale Suspicious DNS Activities Detection via Unsupervised Learning in Advanced Persistent Threats.

In recent years, sensors in the Internet of things have been commonly used in Human's life. APT (Advanced Persistent Threats) has caused serious damage to network security and the sensors play an important role in the attack process. For a long time, attackers infiltrate, attack, conceal, spread, and steal information of target groups through the compound use of various attacking means, while existing security measures based on single-time nodes cannot defend against such attacks. Attackers often exploit the sensors' vulnerabilities to attack targets because the security level of the sensors is relatively low when compared with that of the host. We can find APT attacks by checking the suspicious domains generated at different APT attack stages, since every APT attack has to use DNS to communicate. Although this method works, two challenges still exist: (1) the detection method needs to check a large scale of log data; (2) the small number of attacking samples limits conventional supervised learning. This paper proposes an APT detection framework AULD (Advanced Persistent Threats Unsupervised Learning Detection) to detect suspicious domains in APT attacks by using unsupervised learning. We extract ten important features from the host, domain name, and time from a large number of DNS log data. Later, we get the suspicious cluster by performing unsupervised learning. We put all of the domains in the cluster into the list of malicious domains. We collected 1,584,225,274 DNS records from our university network. The experiments show that AULD detected all of the attacking samples and that AULD can effectively detect the suspicious domain names in APT attacks.

Efficient independent planar dose calculation for FFF IMRT QA with a bivariate Gaussian source model.

The aim of this study is to perform a direct comparison of the source model for photon beams with and without flattening filter (FF) and to develop an efficient independent algorithm for planar dose calculation for FF-free (FFF) intensity-modulated radiotherapy (IMRT) quality assurance (QA). The source model consisted of a point source modeling the primary photons and extrafocal bivariate Gaussian functions modeling the head scatter, monitor chamber backscatter, and collimator exchange effect. The model parameters were obtained by minimizing the difference between the calculated and measured in-air output factors (Sc ). The fluence of IMRT beams was calculated from the source model using a backprojection and integration method. The off-axis ratio in FFF beams were modeled with a fourth degree polynomial. An analytical kernel consisting of the sum of three Gaussian functions was used to describe the dose deposition process. A convolution-based method was used to account for the ionization chamber volume averaging effect when commissioning the algorithm. The algorithm was validated by comparing the calculated planar dose distributions of FFF head-and-neck IMRT plans with measurements performed with a 2D diode array. Good agreement between the measured and calculated Sc was achieved for both FF beams (<0.25%) and FFF beams (<0.10%). The relative contribution of the head-scattered photons reduced by 34.7% for 6 MV and 49.3% for 10 MV due to the removal of the FF. Superior agreement between the calculated and measured dose distribution was also achieved for FFF IMRT. In the gamma comparison with a 2%/2 mm criterion, the average passing rate was 96.2 ± 1.9% for 6 MV FFF and 95.5 ± 2.6% for 10 MV FFF. The efficient independent planar dose calculation algorithm is easy to implement and can be valuable in FFF IMRT QA.

Impact of scanning parameters and breathing patterns on image quality and accuracy of tumor motion reconstruction in 4D CBCT: a phantom study.

Four-dimensional, cone-beam CT (4D CBCT) substantially reduces respiration-induced motion blurring artifacts in three-dimension (3D) CBCT. However, the image quality of 4D CBCT is significantly degraded which may affect its accuracy in localizing a mobile tumor for high-precision, image-guided radiation therapy (IGRT). The purpose of this study was to investigate the impact of scanning parameters hereinafter collectively referred to as scanning sequence) and breathing patterns on the image quality and the accuracy of computed tumor trajectory for a commercial 4D CBCT system, in preparation for its clinical implementation. We simulated a series of periodic and aperiodic sinusoidal breathing patterns with a respiratory motion phantom. The aperiodic pattern was created by varying the period or amplitude of individual sinusoidal breathing cycles. 4D CBCT scans of the phantom were acquired with a manufacturer-supplied scanning sequence (4D-S-slow) and two in-house modified scanning sequences (4D-M-slow and 4D-M-fast). While 4D-S-slow used small field of view (FOV), partial rotation (200°), and no imaging filter, 4D-M-slow and 4D-M-fast used medium FOV, full rotation, and the F1 filter. The scanning speed was doubled in 4D-M-fast (100°/min gantry rotation). The image quality of the 4D CBCT scans was evaluated using contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and motion blurring ratio (MBR). The trajectory of the moving target was reconstructed by registering each phase of the 4D CBCT with a reference CT. The root-mean-squared-error (RMSE) analysis was used to quantify its accuracy. Significant decrease in CNR and SNR from 3D CBCT to 4D CBCT was observed. The 4D-S-slow and 4D-M-fast scans had comparable image quality, while the 4D-M-slow scans had better performance due to doubled projections. Both CNR and SNR decreased slightly as the breathing period increased, while no dependence on the amplitude was observed. The difference of both CNR and SNR between periodic and aperiodic breathing patterns was insignificant (p > 0.48). At end-exhale phases, the motion blurring was negligible for both periodic and aperiodic breathing patterns; at mid-inhale phase, the motion blurring increased as the period, the amplitude or the amount of cycle-to-cycle variation on amplitude increased. Overall, the accuracy of localizing the moving target in 4D CBCT was within 2 mm under all studied cases. No difference in the RMSEs was noticed among the three scanning sequences. The 4D-M-fast scans, free of volume truncation artifacts, exhibited comparable image quality and accuracy in tumor motion reconstruction as the 4D-S-slow scans with reduced imaging dose (0.60 cGy vs. 0.99 cGy) due to the use of faster gantry rotation and the F1 filter, suggesting its suitability for clinical use.

Synthesis, selective cytotoxicities and probable mechanism of action of 7-methoxy-3-arylflavone-8-acetic acids.

Thirteen new analogues of flavone-8-acetic acid, that is, compounds 10a-m bearing a methoxy group at the 7-position and diverse subsitiuents on the benzene ring at the 2- and 3-positions of flavone nucleus, were synthesized and evaluated for their direct antiproliferative effects on two human tumor cell lines and for their indirect antiproliferative activities in the transwell co-culture system. The results indicated that most of compounds 10a-m showed moderate direct cytotoxicities. Among them, compound 10i exhibited higher direct cytotoxicity and selectivity for both cell lines over BJ human foreskin fibroblast cells than 5,6-dimethylxanthenone-4-acetic acid (DMXAA). Interestingly, compared with DMXAA, compound 10e showed comparable indirect cytotoxicity and higher selectivity. In addition, compound 10e was found to be able to induce tumor necrosis factor α (TNF-α) production in human peripheral blood mononuclear cells.

Microwave-assisted efficient synthesis of 2-hydroxydeoxybenzoins from the alkali degradation of readily prepared 3-aryl-4-hydroxycoumarins in water.

This paper describes an operationally simple, green and efficient approach for the synthesis of 2-hydroxydeoxybenzoins bearing diverse substituents from the microwave-assisted alkali degradation of 3-aryl-4-hydroxycoumarins in water. The latter compounds were readily prepared from the intramolecular Claisen condensation reaction of methyl 2-(2-arylacetoxy)benzoates in the presence of Cs2CO3-acetone, in excellent yields and without laborious workup procedures. This method is highly atom-economic and thus applicable for the large-scale synthesis of 2-hydroxydeoxybenzoins.

Prevention of gross setup errors in radiotherapy with an efficient automatic patient safety system.

Treatment of the wrong body part due to incorrect setup is among the leading types of errors in radiotherapy. The purpose of this paper is to report an efficient automatic patient safety system (PSS) to prevent gross setup errors. The system consists of a pair of charge-coupled device (CCD) cameras mounted in treatment room, a single infrared reflective marker (IRRM) affixed on patient or immobilization device, and a set of in-house developed software. Patients are CT scanned with a CT BB placed over their surface close to intended treatment site. Coordinates of the CT BB relative to treatment isocenter are used as reference for tracking. The CT BB is replaced with an IRRM before treatment starts. PSS evaluates setup accuracy by comparing real-time IRRM position with reference position. To automate system workflow, PSS synchronizes with the record-and-verify (R&V) system in real time and automatically loads in reference data for patient under treatment. Special IRRMs, which can permanently stick to patient face mask or body mold throughout the course of treatment, were designed to minimize therapist's workload. Accuracy of the system was examined on an anthropomorphic phantom with a designed end-to-end test. Its performance was also evaluated on head and neck as well as abdominalpelvic patients using cone-beam CT (CBCT) as standard. The PSS system achieved a seamless clinic workflow by synchronizing with the R&V system. By permanently mounting specially designed IRRMs on patient immobilization devices, therapist intervention is eliminated or minimized. Overall results showed that the PSS system has sufficient accuracy to catch gross setup errors greater than 1 cm in real time. An efficient automatic PSS with sufficient accuracy has been developed to prevent gross setup errors in radiotherapy. The system can be applied to all treatment sites for independent positioning verification. It can be an ideal complement to complex image-guidance systems due to its advantages of continuous tracking ability, no radiation dose, and fully automated clinic workflow.

A polar-coordinate-based pencil beam algorithm for VMAT dose computation with high-resolution gantry angle sampling.

PURPOSE: Most commercially available treatment planning systems (TPSs) approximate the continuous delivery of volumetric modulated arc therapy (VMAT) plans with a series of discretized static beams for treatment planning, which can make VMAT dose computation extremely inefficient. In this study, we developed a polar-coordinate-based pencil beam (PB) algorithm for efficient VMAT dose computation with high-resolution gantry angle sampling that can improve the computational efficiency and reduce the dose discrepancy due to the angular under-sampling effect. METHODS AND MATERIALS: 6 MV 1 × 1 m m 2 $1 \times 1{\rm{\;m}}{{\rm{m}}^2}$ pencil beams were simulated on a uniform cylindrical phantom under an EGSnrc Monte Carlo (MC) environment. The MC-generated PB kernels were collected in the polar coordinate system for each bixel on a 40 × 40 c m 2 $40 \times 40{\rm{\;c}}{{\rm{m}}^2}$ fluence map and subsequently fitted via a series of Gaussians. The fluence was calculated using a detectors' eye view with off-axis and MLC transmission factors corrected. Doses of VMAT arc on the phantom were computed by summing the convolution results between the corresponding PB kernels and fluence for each bixel in the polar coordinate system. The convolution was performed using fast Fourier transform to expedite the computing speed. The calculated doses were converted to the Cartesian coordinate system and compared with the reference dose computed by a collapsed cone convolution (CCC) algorithm of the TPS. A heterogeneous phantom was created to study the heterogeneity corrections using the proposed algorithm. Ten VMAT arcs were included to evaluate the algorithm performance. Gamma analysis and computation complexity theory were used to measure the dosimetric accuracy and computational efficiency, respectively. RESULTS: The dosimetric comparisons on the homogeneous phantom between the proposed PB algorithm and the CCC algorithm for 10 VMAT arcs demonstrate that the proposed algorithm can achieve a dosimetric accuracy comparable to that of the CCC algorithm with average gamma passing rates of 96% (2%/2mm) and 98% (3%/3mm). In addition, the proposed algorithm can provide better computational efficiency for VMAT dose computation using a PC equipped with a 4-core processor, compared to the CCC algorithm utilizing a dual 10-core server. Moreover, the computation complexity theory reveals that the proposed algorithm has a great advantage with regard to computational efficiency for VMAT dose computation on homogeneous medium, especially when a fine angular sampling rate is applied. This can support a reduction in dose errors from the angular under-sampling effect by using a finer angular sampling rate, while still preserving a practical computing speed. For dose calculation on the heterogeneous phantom, the proposed algorithm with heterogeneity corrections can still offer a reasonable dosimetric accuracy with comparable computational efficiency to that of the CCC algorithm. CONCLUSIONS: We proposed a novel polar-coordinate-based pencil beam algorithm for VMAT dose computation that enables a better computational efficiency while maintaining clinically acceptable dosimetric accuracy and reducing dose error caused by the angular under-sampling effect. It also provides a flexible VMAT dose computation structure that allows adjustable sampling rates and direct dose computation in regions of interest, which makes the algorithm potentially useful for clinical applications such as independent dose verification for VMAT patient-specific QA.

An approach for online evaluations of dose consequences caused by small rotational setup errors in intracranial stereotactic radiation therapy.

PURPOSE: The purpose of this work is to investigate the impact of small rotational errors on the magnitudes and distributions of spatial dose variations for intracranial stereotactic radiotherapy (SRT) treatment setups, and to assess the feasibility of using the original dose map overlaid with rotated contours (ODMORC) method as a fast, online evaluation tool to estimate dose changes (using DVHs) to clinical target volumes (CTVs) and organs-at-risks (OARs) caused by small rotational setup errors. METHODS: Fifteen intracranial SRT cases treated with either three-dimensional conformal radiation therapy (3DCRT) or intensity-modulated radiation therapy (IMRT) techniques were chosen for the study. Selected cases have a variety of anatomical dimensions and pathologies. Angles of ±3° and ±5° in all directions were selected to simulate the rotational errors. Dose variations in different regions of the brain, CTVs, and OARs were evaluated to illustrate the various spatial effects of dose differences before and after rotations. DVHs accounting for rotations that were recomputed by the treatment planning system (TPS) and those generated by the ODMORC method were compared. A framework of a fast algorithm for multicontour rotation implemented by ODMORC is introduced as well. RESULTS: The average values of relative dose variations between original dose and recomputed dose accounting for rotations were greater than 4.0% and 10.0% in absolute mean and in standard deviation, respectively, at the skull and adjacent regions for all cases. They were less than 1.0% and 2.5% in absolute mean and in standard deviation, respectively, for dose points 3 mm away from the skull. The results indicated that spatial dose to any part of the brain organs or tumors separated from the skull or head surface would be relatively stable before and after rotations. Statistical data of CTVs and OARs indicate the lens and cochleas have the large dose variations before and after rotations, whereas the remaining ROIs have insignificant dose differences. DVH comparisons suggest that the ODMORC method is able to estimate the DVH of CTVs fairly accurately (within 1.5% of relative dose differences for evaluation volumes). The results also show that most of the OARs including the brain stem, spinal cord, chiasm, hippocampuses, optic nerves, and retinas, which were relatively distal from the skull and surface, had good agreement (within 2.0% of relative dose differences for 0.1 cc of the volumes ) between the ODMORC method and the recomputation, whereas OARs more proximate to the bone-tissue interface or surface, such as the lenses and cochlea, had larger dose variations (greater than 5.0%) for some cases due to the incapability of the ODMORC to account for scatter contribution variations proximate to interfaces and intrinsic dose calculation uncertainties for ROIs with small volumes. CONCLUSIONS: The ODMORC method can be implemented as an online evaluation system for rotation-induced dose changes of CTVs and most OARs and for other related dose consequence analyses.

Calibration of a novel four-dimensional diode array.

PURPOSE: The aim of this work is to develop effective calibration methods for a novel fourdimensional (4D) diode array for pretreatment verification of intensity-modulated radiation therapy (IMRT) and rotational therapy. METHODS: A novel 4D diode array (ArcCHECK, Sun Nuclear, Melbourne, FL) was developed to meet the needs of appropriate and efficient quality assurance for IMRT and especially rotational radiotherapy. The diode array presents a consistent detector image in beam's eye view at arbitrary gantry angles due to isotropic arrangement of diodes in a three-dimensional (3D) cylindrical phantom. The 50 ms simultaneous update of all diodes on the detector array (fourth dimension) makes it capable of time-resolved beam delivery analysis with any rotational delivery techniques. The calibration procedure consisted of delivering and measuring a series of calibration beams with 5.8 degrees angular spacing surrounding the cylindrical diode array. Correction factors for diode intrinsic sensitivity and directional response dependence were derived from these measurements. A real-time algorithm to derive gantry angles based on the detector signal was developed to interpolate and apply the corresponding angular correction factors. RESULTS: The calibration was validated with ion chamber scanned beam profiles in a 3D water tank. Excellent agreement was observed between diode array measurement and treatment planning system calculation. The accuracy of the gantry angle derivation algorithm was within 1 degree which caused a less than 0.2% dosimetric uncertainty. CONCLUSIONS: With the proposed calibration method and the automatic gantry angle derivation algorithm, the 4D diode array achieved isotropic detector response and is suitable for both IMRT and rotational therapy pretreatment verification.

Assessment of the setup dependence of detector response functions for mega-voltage linear accelerators.

PURPOSE: Accurate modeling of beam profiles is important for precise treatment planning dosimetry. Calculated beam profiles need to precisely replicate profiles measured during machine commissioning. Finite detector size introduces perturbations into the measured profiles, which, in turn, impact the resulting modeled profiles. The authors investigate a method for extracting the unperturbed beam profiles from those measured during linear accelerator commissioning. METHODS: In-plane and cross-plane data were collected for an Elekta Synergy linac at 6 MV using ionization chambers of volume 0.01, 0.04, 0.13, and 0.65 cm3 and a diode of surface area 0.64 mm2. The detectors were orientated with the stem perpendicular to the beam and pointing away from the gantry. Profiles were measured for a 10 x 10 cm2 field at depths ranging from 0.8 to 25.0 cm and SSDs from 90 to 110 cm. Shaping parameters of a Gaussian response function were obtained relative to the Edge detector. The Gaussian function was deconvolved from the measured ionization chamber data. The Edge detector profile was taken as an approximation to the true profile, to which deconvolved data were compared. Data were also collected with CC13 and Edge detectors for additional fields and energies on an Elekta Synergy, Varian Trilogy, and Siemens Oncor linear accelerator and response functions obtained. Response functions were compared as a function of depth, SSD, and detector scan direction. Variations in the shaping parameter were introduced and the effect on the resulting deconvolution profiles assessed. RESULTS: Up to 10% setup dependence in the Gaussian shaping parameter occurred, for each detector for a particular plane. This translated to less than a +/- 0.7 mm variation in the 80%-20% penumbral width. For large volume ionization chambers such as the FC65 Farmer type, where the cavity length to diameter ratio is far from 1, the scan direction produced up to a 40% difference in the shaping parameter between in-plane and cross-plane measurements. This is primarily due to the directional difference in penumbral width measured by the FC65 chamber, which can more than double in profiles obtained with the detector stem parallel compared to perpendicular to the scan direction. For the more symmetric CC13 chamber the variation was only 3% between in-plane and cross-plane measurements. CONCLUSIONS: The authors have shown that the detector response varies with detector type, depth, SSD, and detector scan direction. In-plane vs. cross-plane scanning can require calculation of a direction dependent response function. The effect of a 10% overall variation in the response function, for an ionization chamber, translates to a small deviation in the penumbra from that of the Edge detector measured profile when deconvolved. Due to the uncertainties introduced by deconvolution the Edge detector would be preferable in obtaining an approximation of the true profile, particularly for field sizes where the energy dependence of the diode can be neglected. However, an averaged response function could be utilized to provide a good approximation of the true profile for large ionization chambers and for larger fields for which diode detectors are not recommended.

Characterization of a real-time surface image-guided stereotactic positioning system.

PURPOSE: The AlignRT3C system is an image-guided stereotactic positioning system (IGSPS) that provides real-time target localization. This study involves the first use of this system with three camera pods. The authors have evaluated its localization accuracy and tracking ability using a cone-beam computed tomography (CBCT) system and an optical tracking system in a clinical setting. METHODS: A modified Rando head-and-neck phantom and five patients receiving intracranial stereotactic radiotherapy (SRT) were used to evaluate the calibration, registration, and position-tracking accuracies of the AlignRT3C system and to study surface reconstruction uncertainties, including the effects due to interfractional and intrafractional motion, skin tone, room light level, camera temperature, and image registration region of interest selection. System accuracy was validated through comparison with the Elekta kV CBCT system (XVI) and the Varian frameless SonArray (FSA) optical tracking system. Surface-image data sets were acquired with the AlignRT3C daily for the evaluation of pretreatment and interfractional and intrafractional motion for each patient. Results for two different reference image sets, planning CT surface contours (CTS) and previously recorded AlignRT3C optical surface images (ARTS), are reported. RESULTS: The system origin displacements for the AlignRT3C and XVI systems agreed to within 1.3 mm and 0.7 degrees. Similar results were seen for AlignRT3C vs FSA. For the phantom displacements having couch angles of 0 degrees, those that utilized ART_S references resulted in a mean difference of 0.9 mm/0.4 degrees with respect to XVI and 0.3 mm/0.2 degrees with respect to FSA. For phantom displacements of more than +/- 10 mm and +/- 3 degrees, the maximum discrepancies between AlignRT and the XVI and FSA systems were 3.0 and 0.4 mm, respectively. For couch angles up to +/- 90 degrees, the mean (max.) difference between the AlignRT3C and FSA was 1.2 (2.3) mm/0.7 degrees (1.2 degrees). For all tests, the mean registration errors obtained using the CT_S references were approximately 1.3 mm/1.0 degrees larger than those obtained using the ART_S references. For the patient study, the mean differences in the pretreatment displacements were 0.3 mm/0.2 degrees between the AlignRT3C and XVI systems and 1.3 mm/1 degrees between the FSA and XVI systems. For noncoplanar treatments, interfractional motion displacements obtained using the ART_S and CT_S references resulted in 90th percentile differences within 2.1 mm/0.8 degrees and 3.3 mm/0.3 degrees, respectively, compared to the FSA system. Intrafractional displacements that were tracked for a maximum of 14 min were within 1 mm/1 degrees of those obtained with the FSA system. Uncertainties introduced by the bite-tray were as high as 3 mm/2 degrees for one patient. The combination of gantry, aSi detector panel, and x-ray tube blockage effects during the CBCT acquisition resulted in a registration error of approximately 3 mm. No skin-tone or surface deformation effects were seen with the limited patient sample. CONCLUSIONS: AlignRT3C can be used as a nonionizing IGSPS with accuracy comparable to current image/marker-based systems. IGSPS and CBCT can be combined for high-precision positioning without the need for patient-attached localization devices.

A sliding-window approach for improved VMAT dose calculation accuracy.

The continuous delivery of volumetric modulated arc therapy (VMAT) plans is usually approximated by discrete apertures at evenly-spaced gantry angles for dose calculation purposes. This approximation can potentially lead to large dose calculation errors if the gantry angle spacings are large and/or there are large changes in the MLC apertures from one control point (CP) to the next. In this work, we developed a sliding-window (SW) method to improve VMAT dose calculation accuracy. For any 2 adjacent VMAT CPs ni and ni + 1, the dose distribution was approximated by a 2-CP SW IMRT beam with the starting MLC positions at CP ni and ending MLC positions at CP ni + 1, with the gantry angle fixed in the middle of the 2 VMAT CPs. Therefore, a VMAT beam with N CPs was approximated by a SW plan with N-1 SW beams. To validate the method, VMAT plans were generated for 10 patients in Pinnacle using 4° gantry spacing. Each plan was converted to a SW plan and dose was recalculated. Another VMAT plan, with 1° gantry spacing, was created by interpolating the original VMAT beam. The original plans were delivered on an Elekta Versa HD and measured with ArcCHECK. For both the isodose distribution and DVH, there were significant differences between the original VMAT plan and either the SW or the interpolated plan. However, they were indistinguishable between the SW and the interpolated plans. When compared with measurement, the average passing rates of the original VMAT plans were 87.3 ± 2.8% and 93.1 ± 1.0% for the 5 HN and 5 spine SBRT cases, respectively. On the other hand, the passing rates for both the VMAT1 and SW plans were above 95% for all the 10 cases studied. The dose calculation times of the original VMAT plans and the SW plans were very similar. We conclude that the proposed SW approach improves VMAT dose calculation accuracy without increase in dose calculation time.

Error detection and classification in patient-specific IMRT QA with dual neural networks.

PURPOSE: Despite being the standard metric in patient-specific quality assurance (QA) for intensity-modulated radiotherapy (IMRT), gamma analysis has two shortcomings: (a) it lacks sensitivity to small but clinically relevant errors (b) it does not provide efficient means to classify the error sources. The purpose of this work is to propose a dual neural network method to achieve simultaneous error detection and classification in patient-specific IMRT QA. METHODS: For a pair of dose distributions, we extracted the dose difference histogram (DDH) for the low dose gradient region and two signed distance-to-agreement (sDTA) maps (one in x direction and one in y direction) for the high dose gradient region. An artificial neural network (ANN) and a convolutional neural network (CNN) were designed to analyze the DDH and the two sDTA maps, respectively. The ANN was trained to detect and classify six classes of dosimetric errors: incorrect multileaf collimator (MLC) transmission (±1%) and four types of monitor unit (MU) scaling errors (±1% and ±2%). The CNN was trained to detect and classify seven classes of spatial errors: incorrect effective source size, 1 mm MLC leaf bank overtravel or undertravel, 2 mm single MLC leaf overtravel or undertravel, and device misalignment errors (1 mm in x- or y direction). An in-house planar dose calculation software was used to simulate measurements with errors and noise introduced. Both networks were trained and validated with 13 IMRT plans (totaling 88 fields). A fivefold cross-validation technique was used to evaluate their accuracy. RESULTS: Distinct features were found in the DDH and the sDTA maps. The ANN perfectly identified all four types of MU scaling errors and the specific accuracies for the classes of no error, MLC transmission increase, MLC transmission decrease were 98.9%, 96.6%, and 94.3%, respectively. For the CNN, the largest confusion occurred between the 1-mm-MLC bank overtravel class and the 1-mm-device alignment error in x-direction class, which brought the specific accuracies down to 90.9% and 92.0%, respectively. The specific accuracy for the 2-mm-single MLC leaf undertravel class was 93.2% as it misclassified 5.7% of the class as being error free (false negative). Otherwise, the specific accuracy was above 95%. The overall accuracies across the fivefold were 98.3 ± 0.7% and 95.6% ± 1.5% for the ANN and the CNN, respectively. CONCLUSIONS: Both the DDH and the sDTA maps are suitable features for error classification in IMRT QA. The proposed dual neural network method achieved simultaneous error detection and classification with excellent accuracy. It could be used in complement with the gamma analysis to potentially shift the IMRT QA paradigm from passive pass/fail analysis to active error detection and root cause identification.