Fiducial markers are used in image-guided surgery to register images to physical space. Submillimetric accuracy is achievable with CT, but with MR geometrical distortions may cause substantial error. Some anatomical regions may suffer minimal distortion, and the markers can be placed in areas of low distortion, but the marker's own magnetic susceptibility causes distortions of its shape and centroid, compromising the accuracy of its localization. General methods for correcting MR distortion require a second image acquisition. We show that it is possible to provide an accurate localization of marker position without a second image. Our method is to perform a simulation of MR imaging based on the known shape and contents of the marker and the known parameters of the imaging protocol. We compare simulated images at multiple candidate angles and positions to the acquired image. The centroid associated with the most similar image is the improved localization. We use a second simulator to provide ground truth, a binary marker model, and a 1-mm resolution for the candidate positions. For three orientations, the method recovered the correct centroid for signal-to-noise ratios as low as 10. For ratios of 5 and 7, we found an improvement in localization accuracy of 1.0±0.4 mm.
This paper describes a new MRI simulator that provides realistic images for arbitrary pulse sequences executed in the presence of static field inhomogeneities including those due to magnetic susceptibility, errors in the applied field, and chemical shift. In contrast to previous simulators, this system generates object-specific inhomogeneity patterns from first principles and propagates the consequent frequency offsets and intravoxel dephasing through the acquisition protocols to produce images with realistic artifacts. The simulator consists of two parts. Part 1 calculates a frequency offset for each voxel. It calculates the size of the static field offset at each voxel in the image based on the known magnetic susceptibility of each of the components at all voxels. It uses a novel implementation of the ?Boundary Element Method? and takes advantage of the superposition principle of magnetism to include voxels with mixtures of substances of differing susceptibilities. Part 2 produces both a signal and a reconstructed image. Its inputs include the 3D digital brain phantom introduced by the McConnell Brain Imaging Centre, frequency offsets computed by part 1, applied static field errors, chemical shift values, and a description of the acquisition protocol.