Nicolas Arango

Nicolas is a PhD student in electrical engineering and computer science whose research focuses on medical imaging technology. He is trying to solve challenging MRI problems by creating and distributing the hardware and computational tools needed for simultaneous optimization of coil geometries, encoding field patterns, and received-signal reconstruction algorithms. He has observed that the steps, or modules, that make up most MRI sequences often have conflicting patient-specific field pattern requirements. Nicolas developed a general MATLAB-based framework for use at scan time that optimizes the field patterns for each sequence module. The framework reads patient rapid-scan data from the imager, exploits the coil pre-characterization and convex formulations to efficiently compute sets of sequence-module optimized coil currents, and then transfers the coil currents sets, along with triggering conditions, to the coil driver array. Nicolas earned an SB in electrical engineering from MIT.



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