Dynalog Files Research Articles

MO‐D‐224A‐09: Validation of Synchronized Dynamic Dose Reconstruction Using Film Dosimetry

Purpose: To validate a method of retrospective dose reconstruction that uses real‐time intra‐treatment patient motion data that is synchronized with MLC leaf positions during IMRT treatments. Method and Materials: IMRT fields from an IRB‐approved prostate protocol were delivered to a water‐equivalent phantom on a programmable translation stage. Kodak XV film was placed at 5 cm depth with an SSD of 95cm and marked to register it with the Monte Carlo (MC) calculation grid. Motion was synchronized with beam delivery by using the target signal to trigger motion. Film measurements were repeated for each beam while the phantom was stationary, and while moving with both idealized and clinically measured motion profiles. MLC leaf positions and fluence state for each beam were obtained from the Varian DynaLog files. MC dose accumulation was performed which incorporated the real‐time phantom motion, DynaLog files and beam state. Films were digitized and compared to the results of the MC calculations. Results: Film measurements in stationary phantoms were measured three times on two machines. The measured dose distributions were compared and showed an average difference of 0.38 +/− 1.53 cGy. The average difference between MC and film measurements of the moving phantom was 0.44 +/− 3.4 cGy and was independent of the motion profile. Measured dose patterns, for both stationary and moving phantoms, were generally well reproduced by MC dose accumulation, including tongue‐and‐groove and motion related features. Doses in moving and static phantoms were compared, for both films and simulations. The measured dose deviations due to motion were well‐characterized by the MC dose accumulation method and not significantly different when a static phantom was compared to MC calculation. Conclusion: Real‐time motion and machine data may be used to reconstruct the dose delivered to the target volume, and may serve as a basis for dynamic refinement of treatments.

SU‐FF‐T‐402: Synchronized Dynamic Dose Reconstruction

Purpose: To present a dose accumulation method that explicitly incorporates real‐time target volume motion, and real‐time machine configuration, MLC motion and fluence state. Method and Materials: Effects of inter‐ and intra‐fraction motion on dose distributions delivered to a target volume are typically estimated by statistical methods that rely on idealized assumptions such as constant random and systematic errors, and constant offsets, periods and amplitudes of motion over the entire course of therapy. In addition, implementation and delivery related issues such as leaf sequencing limitations, spatial interplay between MLC leaf and target volume motion, and temporal interplay of the fluence state in IMRT, are assumed to average out during treatment. To include these effects, a dose accumulation technique is proposed which explicitly incorporates real‐time target volume motion data, and real‐time treatment machine data. Several technologies are becoming available to continuously monitor (10–30 Hz) the patient position, organs‐at‐risk, and the target volume during therapy. Likewise, real‐time (20 Hz) machine configuration, leaf position and fluence state data, is currently available in the Varian DynaLog files. These datasets are synchronized at the beginning of each beam; and the Monte Carlo method, which inherently accounts for time dependence, was used for dose calculations. Results: By synchronizing target volume position, machine configuration, leaf positions and fluence state, with sub‐second resolution, the dose delivered by each beam, may be accumulated. Such accumulated dose distributions reflect the interplay between target volume motion, MLC leaf motion and other machine‐related delivery effects based on real‐time, patient specific, measured data. The delivered dose distribution to date may then be compared to the planned dose distribution to provide input for dynamic refinement of treatment planning and delivery. Conclusion: Delivered target volume dose may be reconstructed using real‐time motion and machine data, and serve as a basis for dynamic refinement of treatments.

QA of intensity-modulated beams using dynamic MLC log files

To evaluate the utility of Dynalog file information for planar dose verification in IMRT QA, a program is developed to convert Dynalog file data to DMLC field files. For this study, five predefined fluencies are planned and delivered using Varian, Eclipse 3D planning system and 6MV photon beam of Varian, Clinac DMX linear accelerator. To measure planar dose distribution, Kodak, EDR2 films are exposed in similar setup as planning setup. Dynalog files are recorded for each delivery and converted into DMLC field files using in-house program. Delivered dose distributions are calculated using DMLC field files from Dynalog files. Planned, Measured and Delivered dose distributions are compared using gamma evaluation in Scanditronix, Omni Pro IMRT software. The Planned and Delivered planar dose distributions agree within 2% dose difference and 2 mm DTA. Measured dose distributions agree within 4% dose difference and 4 mm DTA with Planned dose distribution. Our results show Dynalog file as a promising tool for dynamic IMRT QA.

SU‐FF‐T‐155: Monte Carlo Dosimetric Verification for IMRT QA Using MLC Log Files and EPID

Purpose: EPID is a useful tool for pre‐treatment patient setup and target localization and post‐treatment dosimetry verification. However, EPID images are significantly affected by photon attenuation and scattering in the patient and the detector and therefore cannot be used directly for dose reconstruction. This work investigates a Monte Carlo dosimetric verification method based on MLC log files and the electronic portal imaging device (EPID) for IMRT quality assurance (QA). Method and Materials: We have developed Monte Carlo based software to derive accurate intensity‐modulated fluence maps behind the patient using MLC log files, which take into account the accelerator head geometry, the MLC leaf movement accuracy and the patient attenuation and scatter. Patient initial simulation and pre‐treatment CT data were used to simulate the patient anatomy for interfraction dose comparison. The phase space data behind the patient were used in the EPID response simulation and compared with measured EPID images to investigate patient setup accuracy and dose reconstruction uncertainty. Results: Ten previously treated prostate IMRT plans and patient CT data are included in this study. The MLC leaf position accuracy of the dynalog files from a Varian 21Ex accelerator is verified to within 1mm. The dose distributions based on the leaf sequences from the treatment planning system and the fluence maps rebuilt from the dynalog files are consistent to within 2%, validating our software implementation. The primary photons and scatters are recorded separately behind the patient for different applications as described above. Energy spectrum, fluence distribution and angular distribution are derived to facilitate dose calculation in the EPID. Conclusion: We have developed a log file based Monte Carlo method to generate phase space and fluence maps for pre‐treatment patient setup and post‐treatment dose verification with EPID. This work is implemented as part of our IMRT QA procedure.

SU‐FF‐T‐167: Systematic Approach for Predicting DMLC Motor Failure: A Prognostic Strategy

Purpose: Dynamic MLC and segmental MLC are the two competing delivery modes for IMRT plans. Each has its merits and limitations. Dynamic MLC provides a higher temporal resolution and more accurate dose delivery. However, it exerts extra stress on MLC driving motors, leading to a high motor failure rate. This stems from frequently alternated leaf acceleration and deceleration. The excessive heat generated by high instantaneous torque of the driving motors eventually leads to motor failure. This is particularly true for complicated head and neck cases, where many irregularly‐shaped critical structures are involved and steep dose gradients are required. Frequent machine down‐times create serious logistical problems and delay patient treatment. In this study, an attempt was made to decipher the DMLC motor failure pattern and design a practical prognostic strategy. Method and Materials: A new VARIAN CLINAC 21EX was installed in our center in June 2004. Each evening, a “stress‐test” DMLC file was run at gantry angles 0°, 90°, 180°, and 270°. This procedure was repeated after MLC reinitialization. All Dynalog files recording the leaf positions were saved for postprocessing. The “stress‐test” file was created to intentionally “exercise” the leave at high speed so that those “sick” leave could be identified. The Dynalog files were then analyzed using ARGUS IMRT software. Based on our past seven months' data, a prognostic most probable motor failure threshold was established. Results: The motor failure patterns in terms of leaf position RMS errors were diverse, complicated, and disease site specific. An RMS value of 0.15 cm could be a good prognostic index for potential motor failure. However, “false positive” cases could still occur. Conclusion: The prognostic strategy presented here is practical and easy to implement. However, a large sample of failed motors is needed to further validate the established threshold.

Commissioning and quality assurance for intensity‐modulated radiotherapy with dynamic multileaf collimator: Experience of the Pontificia Universidad Católica de Chile

The objective of this paper is to present our experience in the commissioning and quality assurance (QA) for intensity‐modulated radiotherapy (IMRT) using the dynamic multileaf collimator (dMLC), sliding window technique. The connectivity and operation between all IMRT chain components were checked on the Varian equipment. Then the following tests were performed: stability of leaf positioning and leaf speed, sensitivity to treatment interruptions (acceleration and deceleration), evaluation of standard field patterns, stability of dMLC output, segmental dose accuracy check, average leaf transmission, dosimetric leaf separation, effects of lateral disequilibrium between adjacent leaves in dose profiles, and multiple carriage field verification. Standard patterns were generated for verification: uniform field, pyramid, hole, wedge, peaks, and chair. Weekly QA protocol includes sweeping gap output, garden fence test (narrow bands, 2 mm wide, of exposure spaced at 2‐cm intervals), and segmental dose accuracy check. Monthly QA includes sweeping gap output at multiple gantry and collimator angles, sweeping gap off‐axis output, picket fence test (eight consecutive movements of a 5‐cm wide rectangular field spaced at 5‐cm intervals), stability of leaf speed and leaf motor current test (PWM test). Our patient QA procedure consists of an absolute dose measurement for all treatment fields in the treatment condition, analysis of actual leaf position versus planned leaf position (dynalog files) for each treatment field, film relative dose determination for each field, film relative dose determination for the plan (all treatment fields) in two axial planes, and patient positioning verification with orthogonal films. The tests performed showed acceptable results. After over one year of IMRT treatment, the routine QA machine checks confirmed the precision and stability of the IMRT system.PACS number: 87.53.Xd, 87.53.Dq, 87.53.Mr