MAGNETIC RESONANCE 2D RELAXOMETRY RECONSTRUCTION USING PARTIAL DATA

摘要

An approach is presented to reconstruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.