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The use of Magnetic Resonance Imaging (MRI) in the radiotherapy (RT) treatment planning workflow is increasing. MRI offers superior soft-tissue contrast compared to Computed Tomography (CT) and therefore improves the accuracy in target volume definitions. There are, however concerns with inherent geometric distortions from system- (gradient nonlinearities and main magnetic field inhomogeneities) and patient-related sources (magnetic susceptibility effect and chemical shift). The lack of clearly defined quality assurance (QA) procedures has also raised questions on the ability of current QA protocols to detect common image quality degradations under radiotherapy settings. To fully implement and take advantage of the benefits of MRI in radiotherapy, these concerns need to be addressed.

In Papers I and II, the dosimetric impact of MR distortions was investigated. Patient CTs (CT) were deformed with MR distortion vector fields (from the residual system distortions after correcting for gradient nonlinearities and patient-induced susceptibility distortions) to create distorted CT (dCT) images. Field parameters from volumetric modulated arc therapy (VMAT) treatment plans initially optimized on dCT data sets were transferred to CT data to compute new treatment plans. Data from 19 prostate and 21 head and neck patients were used for the treatment planning. The dCT and CT treatment plans were compared to determine the impact of distortions on dose distributions. No clinically relevant dose differences between distorted CT and original CT treatment plans were found. Mean dose differences were < 1.0% and < 0.5% at the planning target volume (PTV) for the head and neck, and prostate treatment plans, respectively. 

Strategies to reduce geometric distortions were also evaluated in Papers I and II. Using the vendor-supplied gradient non-linearity correction algorithm reduced overall distortions to less than half of the original value. A high acquisition bandwidth of 488 Hz/pixel (Paper I) and 488 Hz/mm (Paper II) kept the mean geometric distortions at the delineated structures below 1 mm. Furthermore, a patient-specific active shimming method implemented in Paper II significantly reduced the number of voxels with distortion shifts > 2 mm from 15.4% to 2.0%.

B₀ maps from patient-induced magnetic field inhomogeneities obtained through direct measurements and by simulations that used MR-generated synthetic CT (sCT) data were compared in Paper III. The validation showed excellent agreement between the simulated and measured B₀ maps.

In Paper IV, the ability of current QA methods to detect common MR image quality degradations under radiotherapy settings were investigated. By evaluating key image quality parameters, the QA protocols were found to be sensitive to some of the introduced degradations. However, image quality issues such as those caused by RF coil failures could not be adequately detected.

In conclusion, this work has shown the feasibility of using MRI data for radiotherapy treatment planning as distortions resulted in a dose difference of less than 1% between distorted and undistorted images. The simulation software can be used to produce accurate B₀ maps, which could then be used as the basis for the effective correction of patient-induced field inhomogeneity distortions and for the QA verification of sCT data. Furthermore, the analysis of the strengths and weaknesses in current QA tools for MRI in RT contribute to finding better methods to efficiently identify image quality errors.