A collaborative national survey initiated by the University of Malaya and the Ministry of Health was conducted from 1993 to 1995 to establish baseline patient dose data for seven common types (12 projections) of X-ray examinations in Malaysia. A total of 12 randomly selected public hospitals and 867 patients were included in this survey. The entrance surface doses (ESD) received by the patients were measured using thermoluminescent dosemeters (TLDs) attached to the patient's skin. Histograms are presented showing wide, positively skewed distributions of measured entrance surface doses for each examination. Mean, median, first and third quartile values of ESD and median effective dose are reported. Survey results are generally comparable with those reported in the UK, USA and by the International Atomic Energy Agency (IAEA). The results also provide information on dose level for a lower weight population (mean weight 60 kg) compared with the international reference dose values based on a 70 kg standard. The findings support the importance of the on-going national quality assurance programme to ensure doses are kept to a level consistent with optimum image quality. The data will also be useful for the formulation of national guidance levels as recommended by the IAEA. Furthermore, this study provides patient dosimetry information on healthcare level II countries.

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To apply failure mode and effect analysis (FMEA) to generate an effective and efficient initial physics plan checklist. Methods: A team of physicists, dosimetrists, and therapists was setup to reconstruct the workflow processes involved in the generation of a treatment plan beginning from simulation. The team then identified possible failure modes in each of the processes. For each failure mode, the severity (S), frequency of occurrence (O), and the probability of detection (D) was assigned a value and the risk priority number (RPN) was calculated. The values assigned were based on TG 100. Prior to assigning a value, the team discussed the values in the scoring system to minimize randomness in scoring. A local database of errors was used to help guide the scoring of frequency. Results: Twenty-seven process steps and 50 possible failure modes were identified starting from simulation to the final approved plan ready for treatment at the machine. Any failure mode that scored an average RPN value of 20 or greater was deemed "eligible" to be placed on the second checklist. In addition, any failure mode with a severity score value of 4 or greater was also considered for inclusion in the checklist. As a by-product of this procedure, safety improvement methods such as automation and standardization of certain processes (e.g., dose constraint checking, check tools), removal of manual transcription of treatment-related information as well as staff education were implemented, although this was not the team's original objective. Prior to the implementation of the new FMEA-based checklist, an in-service for all the second checkers was organized to ensure further standardization of the process. Conclusion: The FMEA proved to be a valuable tool for identifying vulnerabilities in our workflow and processes in generating a treatment plan and subsequently a new, more effective initial plan checklist was created.

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Purpose: In this study, patient setup accuracy was compared between surface guidance and tattoo markers for radiation therapy treatment sites of the thorax, abdomen and pelvis.Methods and materials: A total of 608 setups performed on 59 patients using both surface-guided and tattoo-based patient setups were analyzed. During treatment setup, patients were aligned to room lasers using their tattoos, and then the six-degree-of-freedom (6DOF) surface-guided offsets were calculated and recorded using AlignRT system. While the patient remained in the same post-tattoo setup position, target localization imaging (radiographic or ultrasound) was performed and these image-guided shifts were recorded. Finally, surface-guided vs tattoo-based offsets were compared to the final treatment position (based on radiographic or ultrasound imaging) to evaluate the accuracy of the two setup methods.Results: The overall average offsets of tattoo-based and surface-guidance-based patient setups were comparable within 3.2 mm in three principal directions, with offsets from tattoo-based setups being slightly less. The maximum offset for tattoo setups was 2.2 cm vs. 4.3 cm for surface-guidance setups. Larger offsets (ranging from 2.0 to 4.3 cm) were observed for surface-guided setups in 14/608 setups (2.3%). For these same cases, the maximum observed tattoo-based offset was 0.7 cm. Of the cases with larger surface-guided offsets, 13/14 were for abdominal/pelvic treatment sites. Additionally, larger rotations (>3°) were recorded in 18.6% of surface-guided setups. The majority of these larger rotations were observed for abdominal and pelvic sites (~84%).Conclusions: The small average differences observed between tattoo-based and surface-guidance-based patient setups confirm the general equivalence of the two potential methods, and the feasibility of tattooless patient setup. However, a significant number of larger surface-guided offsets (translational and rotational) were observed, especially in the abdominal and pelvic regions. These cases should be anticipated and contingency setup methods planned for.

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Calculation of four‐dimensional (4D) dose distributions requires the remapping of dose calculated on each available binned phase of the 4D CT onto a reference phase for summation. Deformable image registration (DIR) is usually used for this task, but unfortunately almost always considers only endpoints rather than the whole motion path. A new algorithm, 4D tissue deformation reconstruction (4D TDR), that uses either CT projection data or all available 4D CT images to reconstruct 4D motion data, was developed. The purpose of this work is to verify the accuracy of the fit of this new algorithm using a realistic tissue phantom. A previously described fresh tissue phantom with implanted electromagnetic tracking (EMT) fiducials was used for this experiment. The phantom was animated using a sinusoidal and a real patient‐breathing signal. Four‐dimensional computer tomography (4D CT) and EMT tracking were performed. Deformation reconstruction was conducted using the 4D TDR and a modified 4D TDR which takes real tissue hysteresis (4D TDRHysteresis) into account. Deformation estimation results were compared to the EMT and 4D CT coordinate measurements. To eliminate the possibility of the high contrast markers driving the 4D TDR, a comparison was made using the original 4D CT data and data in which the fiducials were electronically masked. For the sinusoidal animation, the average deviation of the 4D TDR compared to the manually determined coordinates from 4D CT data was 1.9 mm, albeit with as large as 4.5 mm deviation. The 4D TDR calculation traces matched 95% of the EMT trace within 2.8 mm. The motion hysteresis generated by real tissue is not properly projected other than at endpoints of motion. Sinusoidal animation resulted in 95% of EMT measured locations to be within less than 1.2 mm of the measured 4D CT motion path, enabling accurate motion characterization of the tissue hysteresis. The 4D TDRHysteresis calculation traces accounted well for the hysteresis and matched 95% of the EMT trace within 1.6 mm. An irregular (in amplitude and frequency) recorded patient trace applied to the same tissue resulted in 95% of the EMT trace points within less than 4.5 mm when compared to both the 4D CT and 4D TDRHysteresis motion paths. The average deviation of 4D TDRHysteresis compared to 4D CT datasets was 0.9 mm under regular sinusoidal and 1.0 mm under irregular patient trace animation. The EMT trace data fit to the 4D TDRHysteresis was within 1.6 mm for sinusoidal and 4.5 mm for patient trace animation. While various algorithms have been validated for end‐to‐end accuracy, one can only be fully confident in the performance of a predictive algorithm if one looks at data along the full motion path. The 4D TDR, calculating the whole motion path rather than only phase‐ or endpoints, allows us to fully characterize the accuracy of a predictive algorithm, minimizing assumptions. This algorithm went one step further by allowing for the inclusion of tissue hysteresis effects, a real‐world effect that is neglected when endpoint‐only val...

This work investigated effects of implementing the Delta 4 Discover diode transmission detector into the clinical workflow. Methods: PDD and profile scans were completed with and without the Discover for a number of photon beam energies. Transmission factors were determined for all beam energies and included in Eclipse TPS to account for the attenuation of the Discover. A variety of IMRT plans were delivered to a Delta 4 Phantom+ with and without the Discover to evaluate the Discover's effects on IMRT QA. An imaging QA phantom was used to assess the detector's effects on MV image quality. OSLDs placed on the Phantom+ were used to determine the detector's effects on superficial dose. Results: The largest effect on PDDs after d max was 0.5%. The largest change in beam profile symmetry and flatness was 0.2% and 0.1%, respectively. An average difference in gamma passing rates (2%/2 mm) of 0.2% was observed between plans that did not include the Discover in the measurement and calculation to plans that did include the Discover in the measurement and calculation. The Discover did not significantly change the MV image quality, and the largest observed increase in the relative superficial dose when the Discover was present was 1%. Conclusions:The effects the Discover has on the linac beam were found to be minimal. The device can be implemented into the clinic without the need to alter the TPS beam modeling, other than accounting for the device's attenuation. However, a careful workflow review to implement the Discover should be completed.