Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge

PURPOSE: The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully. METHODS: Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions. RESULTS: Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance. CONCLUSION: Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria

Provable Deterministic Sampling Strategies for Fourier Encoding in Magnetic Resonance Imaging

University of Minnesota M.S. thesis. August 2019. Major: Electrical/Computer Engineering. Advisor: Jarvis Haupt. 1 computer file (PDF); vii, 53 pages.There is a constant demand for acceleration of magnetic resonance (MR) imaging to alleviate motion artifacts, and more generally, due to the time sensitive nature of certain imaging applications. One way to speed up MR imaging is to reduce the image acquisition time by subsampling the data domain (k-space). There are several methods available to reconstruct the MR image from undersampled k-space, e.g., those based on the theory of Compressive Sensing. Standard methods employ random undersampling of k-space; however, these methods provide only probabilistic guarantees on the quality of reconstruction. We present a method to reconstruct MR images from deterministically undersampled k-space, and provide analytical guarantees on the quality of MR image reconstruction. Our approach uses sampling constructions formed by deterministic selection of rows of Fourier matrices; coupled with sparsity assumptions on the finite differences of MR images, we formulate the reconstruction problem as a Total Variation (TV) minimization problem. We demonstrate the utility of our TV minimization based approach for MR image reconstruction by reconstructing MR brain scan data, and compare our reconstructions with those obtained via random sampling. Our results suggest that accurate MR reconstructions are possible by deterministic undersampling the k-space, and the quality of deterministic reconstructions are on par with those of reconstructions from randomly acquired data