Rapid distortion correction enables accurate real-time adaptive radiotherapy on an MRI-Linac
OC-0044
Abstract
Rapid distortion correction enables accurate real-time adaptive radiotherapy on an MRI-Linac
Authors: Paul Liu1,2, Shanshan Shan1,2, David Waddington1,2, Bin Dong2, Gary Liney3,4,5, Paul Keall1,2
1The University of Sydney, ACRF Image X Institute, Sydney, Australia; 2Ingham Institute For Applied Medical Research, Ingham Institute For Applied Medical Research, Liverpool, Australia; 3Liverpool and Macarthur Cancer Therapy Centre, Department of Medical Physics, Liverpool, Australia; 4University of Wollongong, Centre for Medical Radiation Physics, Wollongong, Australia; 5University of New South Wales, Faculty of Medicine, Sydney, Australia
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Purpose or Objective
Real-time tumour tracking during radiotherapy relies on precise target localization to maintain target alignment with the radiation beam. MRI-Linacs are well-suited for this purpose, but MRI is affected by image distortion caused by magnetic field gradient non-linearities.
In this work, a real-time distortion correction method was
developed and integrated with an MLC tracking system. This method streams 2D
cine-MRIs from the MRI-Linac during irradiation and mathematically corrects
for distortion. The corrected image is then used to modulate the MLC leaves to
compensate for target motion.
Material and Methods
The gradient field of the Australian MRI-Linac, a 1T open bore prototype
system, was characterized using spherical harmonics (SH) (Fig1A). A grid
phantom with 3718 markers was imaged and the distorted marker positions were
compared to known marker positions to calculate SH distortion correction coefficients.
The SH correction algorithm was then modified to enable real-time implementation.
Prior to tracking, a deformation vector field (DVF) was calculated from the SH coefficients
and the tracking slice location (Fig1B). Subsequent images during irradiation
have the same slice location and are corrected for distortion by applying this DVF (Fig1C).
To verify this method, a 1D motion phantom underwent sinusoidal motion with an amplitude of 20
mm and a period of 9 s. The phantom was tested at three locations 11, 15 and 17 cm from the isocentre, with the magnitude of distortion increasing with distance. Cine-MRIs were acquired at 128×128 resolution at 3 Hz
and corrected by applying the pre-calculated DVF. The target position was found
using template matching and sent to the MLC tracking algorithm to obtain new
leaf positions. The DVF method was compared to tracking using
uncorrected images and images corrected with SH.
Fig. 1 Real-time distortion correction and MLC tracking workflow.
Results
Fig 2A shows the target position
found in cine-MRIs that are uncorrected, corrected with SH and corrected with
DVF for the 15 cm location, compared to the ground truth from the motion
phantom. Fig 2B shows the RMSE of tracking with each method compared to the
ground truth. Both distortion correction methods had similar
accuracy (average RMSE of 1.4 mm for SH and 1.4 mm for DVF) and corrected for
geometric errors caused by distortion (average RMSE of 2.6 mm for uncorrected
images). Distortion correction of each
cine-MRI using the DVF method took 18 ms, compared to 6 s using the SH method.
Fig. 2A) Target position and B) RMSE of each method compared to the ground truth
Conclusion
We have demonstrated a distortion correction
method that can account for gradient field non-linearity and allow accurate target
localization during beam delivery. The DVF method had similar geometric accuracy to
the SH method, but the faster computation time allowed it be implemented in real-time with MLC tracking.