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Virtual Couch Shift (VCS) by Online Plan Re-Optimization for the MRI Linear Accelerator

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B Raaymakers

G Bol1, S Hissoiny2, J Lagendijk1, B Raaymakers1*, (1) University Medical Center Utrecht, Utrecht, The Netherlands (2) Ecole Polytechnique de Montreal, Montreal, Canada

WE-G-BRCD-8 Wednesday 4:30:00 PM - 6:00:00 PM Room: Ballroom CD

With the MRI accelerator it will be possible to get continuous patient anatomy updates, ranging from organ deformation to patient translation. To compensate for translations, one can re-optimize the treatment plan based on the online MRI images. Consequently, the IMRT optimization system should be fast and robust enough to generate daily a clinically acceptable plan to perform this 'virtual couch shift (VCS)'.

The system uses a GPU based Monte-Carlo dose engine (GPUMCD) for online beamlet generation in a 1.5 T magnetic field and a fast inverse dose optimization algorithm (FIDO). For four phantom and two clinical cases (cervix and kidney), we generated clinically acceptable plans. The given plans are regenerated after a series of x, y, and z translations (up to 34 mm) of the patient anatomy, without adapting the optimization constraints as used during the initial optimization. The differences between the original plan and the regenerated plans are evaluated by using the gamma criterion and the relative D99 target coverage.

The system accurately reproduced the initial dose distribution after translating the phantom and patient anatomies. The gamma criterion of 2%/2 mm is satisfied for 99.2% all target voxels and for 97.2% for all OAR voxels. The relative D99 differences are almost 0.0 with a small standard deviation. With current hardware, a 7 beam cervix beamlet generation and IMRT optimization takes 141 seconds, the kidney case takes only 14 seconds.

We developed a system which is fast and accurate enough to perform a VCS by online re-optimization for the MRI accelerator. Currently we are adding sequencing to the system. We expect that this method can also be used for compensating patient rotations and tissue deformation and with this go towards realtime adaptive treatment planning and delivery.

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