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Program Information

MRI for Radiation Oncology

J Wang

K Sheng

R Layman

J Wang1*, K Sheng2*, R Layman3*, (1) MD Anderson Cancer Center, Houston, TX, (2) Deparment of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, (3) Ohio State Univ, Columbus, OH


WE-B-BRD-0 (Wednesday, July 15, 2015) 8:30 AM - 9:30 AM Room: Ballroom D

The use of MRI in radiation therapy is rapidly increasing. Applications vary from the MRI simulator, to the MRI fused with CT, and to the integrated MRI+RT system.

Compared with the standard MRI QA, a broader scope of QA features has to be defined in order to maximize the benefits of using MRI in radiation therapy. These QA features include geometric fidelity, image registration, motion management, cross-system alignment, and hardware interference. Advanced MRI techniques require a specific type of QA, as they are being widely used in radiation therapy planning, dose calculations, post-implant dosimetry, and prognoses. A vigorous and adaptive QA program is crucial to defining the responsibility of the entire radiation therapy group and detecting deviations from the performance of high-quality treatment.

As a drastic departure from CT simulation, MRI simulation requires changes in the work flow of treatment planning and image guidance. MRI guided radiotherapy platforms are being developed and commercialized to take the advantage of the advance in knowledge, technology and clinical experience. This symposium will from an educational perspective discuss the scope and specific issues related to MRI guided radiotherapy.

Learning Objectives:
1. Understand the difference between a standard and a radiotherapy-specific MRI QA program.
2. Understand the effects of MRI artifacts (geometric distortion and motion) on radiotherapy.
3. Understand advanced MRI techniques (ultrashort echo, fast MRI including dynamic MRI and 4DMRI, diffusion, perfusion, and MRS) and related QA.
4. Understand the methods to prepare MRI for treatment planning (electron density assignment, multimodality image registration, segmentation and motion management).
5. Current status of MRI guided treatment platforms.

Funding Support, Disclosures, and Conflict of Interest: Dr. Jihong Wang has a research grant with Elekta-MRL project. Dr. Ke Sheng receives research grants from Varian Medical systems.

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