Quantum Computing for Data Calibration in Parallel Magnetic Resonance Imaging Reconstruction

Parallel imaging techniques, such as GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) play an important role in Magnetic Resonance Imaging (MRI) by significantly reducing scan times and enhancing patient comfort without compromising image quality. GRAPPA's algorithmic framework involving calibration and synthesis stages is critical in reconstructing high-quality images. However, the computational load of the calibration stage, especially with large convolutional kernel sizes or an increased number of receiver coils, poses a significant bottleneck and limits its efficiency and applicability in clinical settings. In this paper, we introduce an approach by proposing a hybrid software architecture that integrates quantum computing into the GRAPPA reconstruction process. Our method exploits the computational capabilities of quantum computing to accelerate the calibration phase, thereby enabling real-time processing speeds. Through experimentation, we demonstrate that the quantum-enhanced approach can expedite the calibration process to around 10–20 milliseconds of Quantum Processing Unit (QPU) programming time for each Linear Time-Invariant (LTI) system solved. The method maintains the integrity of calibration outcomes, achieving results on par with conventional central processing unit (CPU)-based processes. The result represents the progress towards real-time MRI reconstruction by reducing clinical MRI workflows times, improving patient throughput, and potentially enabling new diagnostic capabilities. Solving LTI system with more attributes on D-Wave quantum computer will be studied to show advantages of QPU to CPU in the future work.