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

Abstract

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 -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.

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

“…The susceptibility effects were attributable almost exclusively to the diamagnetic tissues, leading to a decrease of the overall magnetic field and a negative bias. 32 Overall, the implementation of the deformations into the CoMBAT phantom showed that deformations levels at the thoracic diaphragm can reach values up to 5 mm in fast MR sequences lacking 3D distortion correction. These effects can be effectively minimized by changing the MRI acquisition plane from axial to either sagittal or coronal, where the main motion direction (superior-inferior) can be sampled at better resolution and reduced distortion levels.…”

“…The magnitude of spatial distortions due to the use of the geometrical phantom for measurements was specifically evaluated, relying on the same susceptibility simulation algorithm used for the CoMBAT phantom. 32 A simulation was run at 1.5 T considering the presence of the PMMA phantom on the table support, including a thin layer of silica glue that was used to close the upper side of each phantom unit. Results show that average susceptibility-induced variations in the static magnetic field are in the micro-Tesla range, with corresponding deformations of −0.1 AE 0.3 mm in the z-direction at the minimum gradient strength of 9 mT/m, corresponding to the sGRE sequence.…”

“…These measurements are processed to derive a spatial distortion model, as complemented by numerical simulation of susceptibility effects. 32 The developed methodology is tested on two specific 2D pulse sequences: the spoiled gradient-echo (sGRE) and the balanced steady-state free precession (bSSFP). We show that the derived model can provide a realistic simulation that includes spatial distortion effects, and that those effects can be separated into individual contributions.…”

“…Experimental measurements in a custom‐made distortion phantom are reported, providing the quantification of system‐related distortions, namely inhomogeneities and gradient nonlinearities. These measurements are processed to derive a spatial distortion model, as complemented by numerical simulation of susceptibility effects 32 . The developed methodology is tested on two specific 2D pulse sequences: the spoiled gradient‐echo (sGRE) and the balanced steady‐state free precession (bSSFP).…”

“…On the left side, specific contributions from the current work are highlighted: parallelograms represent data, rectangles with thick lines are implemented procedures, and ovals represent output of such procedures. The susceptibility simulation algorithm reported in Ref [32]. provided the susceptibility distortion simulation in the CoMBAT phantom (thick arrow, right side) and the quantification of susceptibility effects in the physical distortion phantom (dashed arrows, right side) aiming at the separation of B 0 inhomogeneities distortion effects.…”