Purpose: To describe an algorithm for determining appropriate physics staffing for radiation treatment. Motivation for this work came from the age of current guidelines which predate the recent evolution in techniques and technology, and also several significant adverse incidents where a lack of physics staffing was identified as a contributing factor to excessive radiation exposure of patients. Methods: Guided by published times required per procedure, we developed an algorithm adaptable to local practice which estimates staffing requirements for medical physics with parameters derived from clinical procedures and service workload, equipment inventory, training, clinical development and administration. The predictive power was evaluated using data from 32 Canadian centres. This algorithm was used to model staffing requirements for the next 10 years to aid regional, institutional and educational program planning with consideration given to the “4Rs” of human resources planning: Requirements, Recruitment, Retention and Residency. Results: For centre‐specific human resource planning, we propose a grid of coefficients addressing specific workload factors for each group. For larger scale planning, case‐based ratios were determined at 260, 300 and 600 annual radiotherapy cases for medical physicists, dosimetrists and electronics technologists respectively. Assuming a 2.5% growth in incidence of cancer and stable utilisation, our supply and demand model predicts a requirement for an additional 39 medical physicists for Ontario by the year 2020. If an additional 3% annual growth in radiation therapy utilisation is included, the number rises to 87. Conclusions: We describe a robust algorithm to determine medical physics staffing levels adaptable to centre‐specific workload and evolving local radiation treatment practice. Although annual caseload has been used in the past as a major parameter for global physics staffing determination, our results indicate that local clinical services and equipment as well as academic activity cause significant deviations from predictions based solely on caseload.
Purpose: To quantify organ‐specific objective function weights in a multi‐objective optimization formulation of prostate IMRT treatments; to demonstrate that optimized weights can be used to generate clinical plans with fewer objective functions more efficiently than current practice. Methods: Starting with a simple prostate IMRT inverse planning formulation involving four weighted OAR objectives, we developed an inverse optimization model (IOM) to determine optimal weights for each objective. The objectives were: penalize bladder and rectum voxels with dose above 50 Gy, and minimize maximum dose to the left and right femoral heads. Tumor objectives were modeled as hard constraints. A historical, clinical prostate treatment plan was input to the IOM, and optimized objective function weights were output. The clinical plan was generated in Pinnacle using 15 objective functions. The IOM was formulated as a linear optimization problem and solved using the CPLEX optimization solver. Using the optimized weights and the original inverse planning formulation, a new treatment plan was generated and then compared to the clinical plan. Results: The optimized weight on the rectum objective was almost exactly three times the bladder objective weight. The weights for the femoral heads were negligible. A new treatment plan generated using the optimized weights and four objectives was nearly identical to the clinical plan. All required clinical dosimetric criteria were satisfied by the new plan. Conclusions: A treatment planning formulation with optimized objective function weights can generate plans that are nearly identical to clinical plans that were generated using many more objective functions. With optimized weights and fewer objectives, it may be possible to significantly reduce the need for parameter tuning and trial‐and‐error approaches in treatment planning. While parameter tuning may be unavoidable, inverse optimization can be used to significant reduce the size of the parameter space that needs to be searched.
Purpose: Linac‐based kilo‐voltage x‐ray systems for image‐guidance have been widely adopted in radiation therapy. Over recent months, six cone‐beam CT enabled linacs have been installed and commissioned in our clinic. In the era of image‐guidance, a reference image of the patient anatomy and the treatment plan must be delivered to the unit for matching. This requires coordination of the simulation, planning and delivery systems. This work describes the commissioning and integration process for kilo‐voltage x‐ray image‐guidance systems in the modern radiotherapy clinic. Method and Materials: Accuracy and reproducibility of the kV source was measured using the RTI Barracuda diagnostic x‐ray meter. Image quality of the cone‐beam CT acquisition system was assessed using the CatPhan 500 multi‐slice CT image quality phantom. Metrics here include spatial resolution, uniformity, CT# accuracy and linearity as well as scale, orientation and slice thickness. Geometric coincidence of the imaging and treatment iso‐centers was verified using a steel BB localized to the MV radiation iso‐center using a Winston‐Lutz technique. A custom‐made anthropomorphic torso phantom was CT simulated and planned in four orientations (head first supine, head first prone, feet first supine and feet first prone) and exported to each linac for image‐guided setup correction and “treatment”. This tested the DICOM connectivity, orientation, scale, structure sets and isocenter of the imported reference images as well as magnitude and direction of couch corrections. Results: The average absolute error after correction across all orientations and all platforms was (0.8±0.7, 0.6±0.7, 1.0±0.7) [mm±1S.D.] (L/R, S/I, A/P). Conclusion: A commissioning process for linacs with kilo‐voltage imaging was described. Image‐guided radiotherapy increases precision and accuracy of the delivered treatment but it also increases the demand for integration and coordination of other systems in the modern clinic. Conflict of Interest: This work is sponsored by the Elekta Synergy Research Consortium.
As high‐precision 3‐D conformal radiation therapy and intensity‐modulated radiation therapy have become standard practice, radiographic imaging using kilovoltage (kV) x‐ray sources has been rapidly implemented for in‐room target localization and patient positioning to ensure conformal dose delivery. Various types of imaging devices are commercially available for clinical applications and their typical imaging functionalities include 2‐D radiographic and fluoroscopic imaging as well as 3‐D cone beam CT. There are substantial demands for fundamental understanding of what kind of systems can be used for clinical practice, when and what imaging systems should be used, how they can be properly used for daily target localization for different anatomical sites, and what kind of quality assurance programs are needed. In this session, we will briefly introduce the latest commercially available imaging systems using kV imaging for in‐room target localization and their imaging principles. Clinical applications and imaging protocols using these systems for accurate target localization and patient positioning will then be discussed. Finally, systematic quality assurance procedures will be presented. Objectives: 1. Understand the latest commercially available technologies for in‐room kV radiography, fluoroscopy, and cone‐beam CT and their basic imaging principles. 2. Understand the basic clinical imaging applications for daily localization. 3. Understand the basic system limitations and QA components of a comprehensive QA program.
Purpose: To develop a combined imaging and dosimetric phantom for the quality assurance (QA) of linear accelerators capable of cone‐beam CT image‐guided and intensity modulated radiotherapy (IG‐IMRT). This integrated approach verifies image quality, registration, and delivery performance. Method and Materials: The prototype consisted of a cylindrical imaging phantom (CatPhan) combined with an array of 11 radiation diodes arranged in a plane, oriented perpendicular to the phantom axis. Single diode performance was assessed at 6 and 18 MV (profiles, depth‐dose curves and angular dependence) with comparison to ion chamber. The detection of geometric and dosimetric errors in delivery was assessed using an IG‐IMRT treatment (6 MV, 7 beams, 180 cGy, CBCT‐guided) in which known displacements relative to isocenter were applied. The minimum detectable shift was determined by comparing the discrepancy between planned and measured doses to the dose measurement uncertainty under non‐shifted conditions. Results: Diode profiles and depth dose curves agreed generally within ±1% with the chamber results. Angular dependence for the diode was low for axial beams (±1%) but increased to a maximum of 11% for out‐of‐plane irradiation. The normalized dose measurements obtained with the multi‐diode phantom agreed well with the planning results. Displacements as small as 1 mm resulted in detectable deviation dose (8.2 cGy SD, n=11) relative to the uncertainty in dose measurements for non‐shifted conditions (1.6 cGy SD, n=11). Conclusion: A phantom prototype was designed and constructed for comprehensive QA of image‐guided radiotherapy in terms of image quality and dose delivery. The results allow us to set specifications for further development. We anticipate the system will permit the localization/detection of sub‐millimeters errors in dose gradient placement. Future phantom designs will facilitate absolute dosimetry and investigate the use of additional diodes in different patterns. Conflict of Interest: Supported in part by Sun Nuclear Corporation and Elekta Inc.
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