Integration of spatial distortion effects in a 4D computational phantom for simulation studies in extra-cranial MRI-guided radiation therapy: Initial results.

PURPOSE: Spatial distortions in magnetic resonance imaging (MRI) are mainly caused by inhomogeneities of the static magnetic field, nonlinearities in the applied gradients, and tissue-specific magnetic susceptibility variations. These factors may significantly alter the geometrical accuracy of the reconstructed MR image, thus questioning the reliability of MRI for guidance in image-guided radiation therapy. In this work, we quantified MRI spatial distortions and created a quantitative model where different sources of distortions can be separated. The generated model was then integrated into a four-dimensional (4D) computational phantom for simulation studies in MRI-guided radiation therapy at extra-cranial sites. METHODS: A geometrical spatial distortion phantom was designed in four modules embedding laser-cut PMMA grids, providing 3520 landmarks in a field of view of (345 × 260 × 480) mm3 . The construction accuracy of the phantom was verified experimentally. Two fast MRI sequences for extra-cranial imaging at 1.5 T were investigated, considering axial slices acquired with online distortion correction, in order to mimic practical use in MRI-guided radiotherapy. Distortions were separated into their sources by acquisition of images with gradient polarity reversal and dedicated susceptibility calculations. Such a separation yielded a quantitative spatial distortion model to be used for MR imaging simulations. Finally, the obtained spatial distortion model was embedded into an anthropomorphic 4D computational phantom, providing registered virtual CT/MR images where spatial distortions in MRI acquisition can be simulated. RESULTS: The manufacturing accuracy of the geometrical distortion phantom was quantified to be within 0.2 mm in the grid planes and 0.5 mm in depth, including thickness variations and bending effects of individual grids. Residual spatial distortions after MRI distortion correction were strongly influenced by the applied correction mode, with larger effects in the trans-axial direction. In the axial plane, gradient nonlinearities caused the main distortions, with values up to 3 mm in a 1.5 T magnet, whereas static field and susceptibility effects were below 1 mm. The integration in the 4D anthropomorphic computational phantom highlighted that deformations can be severe in the region of the thoracic diaphragm, especially when using axial imaging with 2D distortion correction. Adaptation of the phantom based on patient-specific measurements was also verified, aiming at increased realism in the simulation. CONCLUSIONS: The implemented framework provides an integrated approach for MRI spatial distortion modeling, where different sources of distortion can be quantified in time-dependent geometries. The computational phantom represents a valuable platform to study motion management strategies in extra-cranial MRI-guided radiotherapy, where the effects of spatial distortions can be modeled on synthetic images in a virtual environment.

Improving the modelling of susceptibility-induced spatial distortions in MRI-guided extra-cranial radiotherapy.

Magnetic-resonance linear-accelerator (MR-LINAC) systems integrating in-room magnetic-resonance-imaging (MRI) guidance are a currently emerging technology. Such systems address the need to provide frequent imaging at optimal soft-tissue contrast for treatment guidance. However, the use of MRI-guidance in radiotherapy should address imaging-related spatial distortions, which may hinder accurate geometrical characterization of the treatment site. Since spatial encoding relies on well-defined magnetic fields, accurate modeling of the magnetic field alterations due to [Formula: see text]-inhomogeneities, gradient nonlinearities, and susceptibilities is needed. In this work, the modeling of susceptibility induced distortions is considered. Dedicated susceptibility measurements are reported, aiming at extending the characterization of different tissues for MRI-guided extra-cranial radiotherapy applications. A digital 4D anthropomorphic phantom, providing time-resolved anatomical changes due to breathing, is exploited as reference anatomy to quantify spatial distortions due to variations in tissue susceptibility. Sub-millimeter values can be attributed to susceptibility-induced distortions, with maximum values up to 2.3 mm at a gradient strength of 5 mT m-1. Improvements in susceptibility simulation for extra-cranial sites are shown when including specifically the contributions from lung, liver and muscular tissues.