Vol 24, No 6 (2019)
Original research articles
Published online: 2019-11-01

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Evaluating the accuracy of geometrical distortion correction of magnetic resonance images for use in intracranial brain tumor radiotherapy

Seyed Mehdi Bagherimofidi1, Claus Chunli Yang2, Roberto Rey-Dios3, Madhava R. Kanakamedala2, Ali Fatemi24
DOI: 10.1016/j.rpor.2019.09.011
Rep Pract Oncol Radiother 2019;24(6):606-613.

Abstract

Aim

Determine the 1) effectiveness of correction for gradient-non-linearity and susceptibility effects on both QUASAR GRID3D and CIRS phantoms; and 2) the magnitude and location of regions of residual distortion before and after correction.

Background

Using magnetic resonance imaging (MRI) as a primary dataset for radiotherapy planning requires correction for geometrical distortion and non-uniform intensity.

Materials and Methods

Phantom Study: MRI, computed tomography (CT) and cone beam CT images of QUASAR GRID3D and CIRS head phantoms were acquired. Patient Study: Ten patients were MRI-scanned for stereotactic radiosurgery treatment. Correction algorithm: Two magnitude and one phase difference image were acquired to create a field map. A MATLAB program was used to calculate geometrical distortion in the frequency encoding direction, and 3D interpolation was applied to resize it to match 3D T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) images. MPRAGE images were warped according to the interpolated field map in the frequency encoding direction. The corrected and uncorrected MRI images were fused, deformable registered, and a difference distortion map generated.

Results

Maximum deviation improvements: GRID3D, 0.27mm y-direction, 0.07mm z-direction, 0.23mm x-direction. CIRS, 0.34mm, 0.1mm and 0.09mm at 20-, 40- and 60-mm diameters from the isocenter. Patient data show corrections from 0.2 to 1.2mm, based on location. The most-distorted areas are around air cavities, e.g. sinuses.

Conclusions

The phantom data show the validity of our fast distortion correction algorithm. Patient-specific data are acquired in <2min and analyzed and available for planning in less than a minute.

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