Geometric distortion assessment in 3T MR images used for treatment planning in cranial Stereotactic Radiosurgery and Radiotherapy

Abstract

Magnetic Resonance images (MRIs) are employed in brain Stereotactic Radiosurgery and Radiotherapy (SRS/SRT) for target and/or critical organ localization and delineation. However, MRIs are inherently distorted, which also impacts the accuracy of the Magnetic Resonance Imaging/Computed Tomography (MRI/CT) co-registration process. In this phantom-based study, geometric distortion is assessed in 3T T2-weighted images (T2WIs), while the efficacy of an MRI distortion correction technique is also evaluated. A homogeneous polymer gel-filled phantom was CT-imaged before being irradiated with 26 4-mm Gamma Knife shots at predefined locations (reference control points). The irradiated phantom was MRI-scanned at 3T, implementing a T2-weighted protocol suitable for SRS/SRT treatment planning. The centers of mass of all shots were identified in the 3D image space by implementing an iterative localization algorithm and served as the evaluated control points for MRI distortion detection. MRIs and CT images were spatially co-registered using a mutual information algorithm. The inverse transformation matrix was applied to the reference control points and compared with the corresponding MRI-identified ones to evaluate the overall spatial accuracy of the MRI/CT dataset. The mean image distortion correction technique was implemented, and resulting MRI-corrected control points were compared against the corresponding reference ones. For the scanning parameters used, increased MRI distortion (>1mm) was detected at areas distant from the MRI isocenter (>5cm), while median radial distortion was 0.76mm. Detected offsets were slightly higher for the MRI/CT dataset (0.92mm median distortion). The mean image distortion correction improves geometric accuracy, but residual distortion cannot be considered negligible (0.51mm median distortion). For all three datasets studied, a statistically significant positive correlation between detected spatial offsets and their distance from the MRI isocenter was revealed. This work contributes towards the wider adoption of 3T imaging in SRS/SRT treatment planning. The presented methodology can be employed in commissioning and quality assurance programmes of corresponding treatment workflows.

Fig 1. MRIs of the irradiated phantom.

(a) An indicative axial image of the irradiated phantom, depicting the polymerized areas (lower T2-weighted signal) as a result of GK 4-mm shot delivery. (b) and (c) The same slice after applying 5% and 10% random noise, respectively, for estimating control point localization uncertainty.

Fig 2. Vectors representing total spatial offset, d_R.

(a) MRI forward images, (b) MRI/CT dataset and (c) MRI-corrected images. Vectors’ origins correspond to the positions of the reference control points. Vectors’ lengths have been magnified to increase visibility but are proportional to the detected offsets which are quantified by the colorbar in mm. The MRI DICOM coordinate system is adopted.

Fig 3. Magnitude of the radial spatial offset, |d_R|, for all 26 control points with respect to their distance from the MRI isocenter.

Using Spearman’s correlation coefficient, a statistically significant positive correlation between the distance to the MRI isocenter and detected radial offset was revealed for all three datasets.