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Session: CT and MR-Guided Adaptive RT [Return to Session]

Impact of Visual Biofeedback On Breathing Regularity During 4D-MRI Acquisition On the MR-Linac

K Keijnemans*, P Borman, B Raaymakers, M Fast, Department of Radiotherapy, University Medical Center Utrecht, NL


SU-K-BRC-7 (Sunday, 7/10/2022) 5:00 PM - 6:00 PM [Eastern Time (GMT-4)]

Ballroom C

Purpose: 4D-MRI is useful for guiding abdominothoracic radiotherapy on the MR-linac. Natural variations in breathing cycles affect the amount of missing data and 4D-MRI quality. Providing biofeedback on the respiratory signal can potentially increase motion regularity and therefore improve image quality and shorten acquisition times. This study investigates the effect of providing visual biofeedback on the respiratory signal while acquiring 4D-MRI data on a 1.5 T Unity MR-linac (Elekta AB, Stockholm, SE).

Methods: Data were acquired with a simultaneous multi-slice (SMS) accelerated 4D-MRI sequence in two healthy volunteers. The sequence repeatedly acquires a stack of 52 coronal slices taking 4:06 minutes. Using in-house software, we enabled real-time streaming of the SMS data to the scanner’s reconstructor and subsequently to our research software. First, a “pre-beam” 4D-MRI was acquired and streamed to Matlab, where initial 4D-MRI sorting started after 80% of scan completion. After scan completion, 4D-MRI sorting was finalized using craniocaudal motion of the liver-lung interface as a surrogate. Based on the “pre-beam” 4D-MRI, a 4D motion model and a corresponding 3D mid-position MRI were created, and then used during “beam-on” imaging to extract real-time motion. Visual guidance was provided by displaying a cosine or cosine⁴ trajectory (with the average amplitude and period extracted from the “pre-beam” data) on an in-room screen together with the real-time extracted position. Another unguided scan was acquired for reference.

Results: 4D-MRI sorting was completed within 5 seconds after scan completion, and mid-position calculation took 45 seconds. The visual biofeedback had a latency of 446 ms. Visual biofeedback decreased the normalized breathing period variability (SD/mean) by 15-71% (cosine) and 41-54% (cosine⁴) compared to no guidance. The normalized peak-to-peak motion variability (SD/mean) decreased by 33-62% (cosine) and 26-43% (cosine⁴) during visual biofeedback.

Conclusion: Visual biofeedback drastically improves breathing motion regularity during a 4D-MRI acquisition.

Funding Support, Disclosures, and Conflict of Interest: The authors acknowledge funding by the Dutch Research Council (NWO) through project no. 17515 (BREATHE EASY).


Lung, MRI



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