Accelerated imaging of metallic implants using model-based nonlinear reconstruction

To accelerate imaging near metallic implants with multi-spectral imaging (MSI) techniques by exploiting a signal model in the spectral dimension.

MSI techniques resolve metal-induced field perturbations by acquiring separate 3D spatial encodings at multiple excitation frequencies, which are referred to as spectral bins. The proposed model-based reconstruction exploits the correlation between spectral bins in image reconstruction by enforcing a signal model to describe the signal profile across bins. This work evaluates the accuracy of the MSI signal model in simulations and in vivo experiments. The proposed model-based reconstruction was evaluated in 6 subjects at an overall undersampling factor of 17.4 and compared with model-free parallel imaging and compressed sensing (PI & CS). The quality of reconstructed images was evaluated using normalized RMS error (nRMSE) and structural similarity index (SSIM) comparisons, with paired Wilcoxon tests in 6 subjects used to determine whether there was a significant difference in the metrics.

Both simulations and in vivo experiments show that the proposed signal model can represent the MSI signal profiles in the spectral dimension compactly and accurately. In the in vivo experiments, the model-based reconstruction significantly improved image quality over model-free PI & CS, with P < 0.05 for both nRMSE and SSIM at 17.4× acceleration.

This work presents the feasibility of using a model-based reconstruction to accelerate MSI techniques for faster MR imaging near metal.