Volume 34, Issue 6, June 2007
Index of content:
- Therapy Continuing Education Course: BallroomB
CE‐Therapy: Monte Carlo I: Review of the AAPM TG‐105
34(2007); http://dx.doi.org/10.1118/1.2761193View Description Hide Description
Despite its proven accuracy and the potential for improved dose distributions to influence treatment outcomes, the long calculation times previously associated with MC simulation rendered this method impractical for routine clinical treatment planning. However, the development of faster codes optimized for radiotherapy calculations and improvements in computer processor technology have substantially reduced calculation times to, in some instances, within minutes on a single processor. Several commercial vendors have released or are currently in the process of releasing MC algorithms for photon and/or electron beamtreatment planning. Consequently, the accessibility and use of MCtreatment planning algorithms may well become widespread in the radiotherapy community. With MC simulation,dose is computed stochastically using first principles; this method is therefore quite different from conventional dose algorithms. Issues such as statistical uncertainties, the use of variance reduction techniques, the ability to account for geometric details in the accelerator treatment head simulation, and other features, are all unique components of a MCtreatment planning algorithm. Successful implementation by the clinical physicist of such a system will require an understanding of the basic principles of MC techniques.
1. To provide an educational review of the physics of the MC including discussion of methods used for coupled photon and electron transport.
2. To review the methods used to improve the simulation efficiency.
3. To briefly review the vendor transport codes currently used for clinical treatment planning.
4. To describe the development of treatment head models for clinical treatment planning.
5. To discuss the factors associated with MCdose calculation within the patient‐specific geometry, such as statistical uncertainties, CT‐to‐material, and reporting of dose‐to‐medium versus dose‐to‐water.
6. To discuss the issues associated with experimental verification of MC algorithms.
7. To briefly review the potential clinical implications of MC calculated dose distributions.
8. To provide example timing comparisons of the major vendor MC codes in the clinical setting.
CE‐Therapy: Stereotactic Cranial RS/RT
MO‐B‐BRB‐01: Quality Assurance in Stereotactic Radiosurgery and Fractionated Stereotactic Radiotherapy34(2007); http://dx.doi.org/10.1118/1.2761203View Description Hide Description
Stereotactic radiosurgery involves the high‐dose irradiation of cranial neoplasms delivered in a single fraction. In the 50‐plus years since it was first introduced, stereotactic radiosurgery has become a standard of care in the treatment of braintumors, vascular malformations, functional disorders, and pain. Modern radiosurgery can be performed non‐invasively and on an outpatient basis while maintaining an extremely high degree of accuracy. Within the past ten years, the field of radiosurgery has seen numerous technological enhancements including: (1) the development of dedicated devices for stereotactic delivery; (2) the use of relocatable frames to facilitate fractionated delivery; (3) the development of “frameless” approaches; and (4) the application to extracranial tumor sites. Each of these developments has been accompanied by its own challenges in assuring targeting and dosimetric accuracy. In this presentation we review the technologies for stereotactic localization and treatment of cranial targets with particular emphasis on the quality assurance aspects associated with establishing and maintaining a clinical radiosurgery program. Specifically, the presentation will:
1. Differentiate the technologies used in the delivery of stereotactic radiosurgery including linac‐based techniques (both conventional and robotic) and the Gamma Knife.
2. Define the treatment planning parameters for linac‐based and Gamma Knife stereotactic radiosurgery.
3. Discuss measures for assuring accuracy in stereotactic localization and dose.
4. delivery for linac‐based and Gamma Knife stereotactic radiosurgery.
CE‐Therapy: Imaging for Planning/Verification: In‐Room Imaging
34(2007); http://dx.doi.org/10.1118/1.2761304View Description Hide Description
Multi‐modality imaging is improving the accuracy and precision in treatment planning by including more information in the initial model of the patient. The goals of imaging for treatment planning are to determine the boundaries and functional information of the tumor and critical normal structures. Quantification of physiological motion has also become increasingly important in highly conformal treatment planning. Obstacles for imaging include improving contrast, limiting artifacts, improving temporal and spatial resolution, and reducing or eliminating the interference of motion.
Combining the soft tissue imaging of MR, functional imaging of MR, CT, PET and SPECT with geometrically robust CTimaging improves the definition of the tumor and critical normal structures. In addition, dynamic information can be accurately quantified and incorporated into the treatment planning process through 4D imaging capabilities in CT and repeat MR imaging. Reducing motion artifacts allows improvement in tumor definition. Methods of reducing the interference of motion on image acquisition include suspending the motion, through voluntary or assisted methods, and reducing the imaging session length, through multi‐slice acquisition and parallel imaging. Optimizing imaging sequences and contrast enhancement and timing improves the ability to define the tumor. The integration of these multi‐modality images into one more complete model of the patient is evolving through the use of automatic registration methods.
The presentation will highlight the benefits of multi‐modality imaging in the treatment planning of tumors in the thorax, abdomen, and pelvis. Image optimization strategies will be discussed for each modality and developments to improve image acquisition and integration into treatment planning will be described.
1. Appreciate the benefits of including multi‐modality imaging in treatment planning.
2. Understand methods to optimize the acquisition of multi‐modality images for accurate treatment planning, including sequences, postprocessing, and timing.
3. Identify technical developments to improve image acquisition and integration into treatment planning.
34(2007); http://dx.doi.org/10.1118/1.2761305View Description Hide Description
It was not too long ago when CT based treatment planning was reserved for the few patients that was critical to have volumetric imaging, and treatment verification was limited to planar films of mediocre quality from a megavoltage imaging source. Today, we use multi‐modality based planning (CT,MRI,PET etc) and we employ sophisticated imaging tools and techniques to verify the correct delivery of the treatment. Such imaging capabilities include electronic portal imaging devices coupled with megavoltage (MV) or kilovoltage (kV) x‐ray sources, MV and kV cone beam CT,ultrasound guidance, optical systems, MV computed tomography,CT on rails and others. The goal of any in‐room imaging device is to verify that the patient setup is accurate and in accordance to the treatment plan. While some modalities allow for pre‐treatment verification, others can also provide on‐treatment verification feedback. The choice of the modality to use depends largely on user preference but also on the investment the clinic is willing to make to purchase the necessary equipment. Nonetheless, image based treatment verification has become a necessary and valuable aspect of our clinical practice and is enabling us to become a lot more aggressive with the planning and delivery of radiation treatments.Image Guided Radiation Therapy(IGRT) is undoubtedly the future of radiation therapy.
In this presentation we will discuss some of the most popular methods of imaging for treatment verification techniques. Clinical examples will also be presented to demonstrate the implementation, use, and clinical experience with these modalities.
1. Review of existing image based treatment verification modalities.
2. Discuss pros and cons of each modality and appropriateness of use.
CE‐Therapy: Monte Carlo — II: Clinical Impact
34(2007); http://dx.doi.org/10.1118/1.2761317View Description Hide Description
This presentation is aimed at facilitating participants with the clinical application of Monte Carlodose calculation algorithms in radiotherapytreatment planning and dosimetry verification. Following a brief introduction to the clinical implementation and commissioning of the Monte Carlodose calculation software detailed discussions will be given on the actual and potential clinical impact of Monte Carlodose calculation algorithms on conventional electron beam therapy and advanced mixed beam treatments.
Patient treatment plans generated using conventional dose calculation algorithms and Monte Carlo methods will be compared with the causes of the dose discrepancies discussed. Further discussions will be conducted on the use of Monte Carlodose calculation as a radiotherapy treatment QA tool to validate individual patient plans and as an investigation tool to improve target dose conformity and normal tissue sparing using novel treatment techniques.
1. To introduce important aspects of clinical implementation and commissioning of Monte Carlodose calculation algorithms for radiotherapytreatment planning.
2. To describe the role of Monte Carlodose calculation in electron therapy and advanced mixed beam treatment planning.
3. To describe the applications of Monte Carlodose calculations in treatment planning and beam delivery QA for advanced radiotherapy treatments.
34(2007); http://dx.doi.org/10.1118/1.2761318View Description Hide Description
The goal of this course is to familiarize participants with the clinical use of Monte Carlo(MC) dose calculation algorithms in IMRT photon‐beam treatment planning. Particular focus will be applied to the actual and potential clinical impact of MC dose calculation algorithms on patient treatments. Following a brief description of the integration of MC algorithms with commercial planning systems, MC dose re‐calculation results will be compared with plans computed using the conventional commercial treatment planning system algorithms. Specific patient examples where MC computed doses differ significantly from those computed with standard treatment planning system algorithms will be presented, with the causes of the dose discrepancies identified. With this background, the roles that MC has played in actual clinical practice will be described: as a QA tool to validate treatment planning system algorithms, as a QA tool to validate individual IMRT plans, and as a treatment planning tool to improve conformity between optimized and delivered IMRT plans. Strategies to minimize MC dose calculation time during IMRT optimization will be described.
1. To instruct clinical physicists regarding roles of Monte Carlo dose calculations for clinical IMRT patient treatment planning.
2. To describe the role of Monte Carlo in IMRTtreatment planning system and patient specific quality assurance.
3. To describe clinical cases where Monte Carlo dose calculation either resulted —or would have resulted— in a change in the clinical treatment.
4. To describe the applications of and impact of Monte Carlo dose calculations for IMRT optimization.
5. This work was sponsored in part by Philips Medical Systems and Varian Medical Systems.