GRAPPA reconstruction using quantum computing
Abstract
The prolonged time required for Magnetic Resonance Imaging (MRI) procedures often causes stress and anxiety in patients. Patients' elongated exposure to a narrow bore of a scanner increases healthcare costs. Additionally, long scan times increase patient discomfort and cause motion artifacts in images. As a clinical MRI method, Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) is a parallel imaging technique that shortens data acquisition time and increases spatial resolution by employing multiple phased-array coils. Missing data are reconstructed by solving a linear time-invariant (LTI) system. When a large size of an interpolation kernel is used, the number of equations of the LTI system is significantly increased and the reconstruction time is delayed. Both software-based and hardware-based approaches have been proposed to accelerate the GRAPPA reconstruction. Dimension reduction is often used as the software-based approach which generates virtual coils and reduces the actual number of physical coils, so the total number of LTI equations is decreased. Field-programmable gate array (FPGA) was employed as a hardware-based approach to solve the LTI system. In this research, a quantum computing method was proposed to accelerate GRAPPA reconstruction. Different from the dimension reduction and FPGA techniques, quantum computing is an emerging computing paradigm that can accelerate computing speed with quantum bits (qubits) rather than classical bits. We…
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