A Review on Accelerated Magnetic Resonance Imaging Techniques: Parallel Imaging, Compressed Sensing, and Machine Learning.

Abstract

A concise overview of three major advancements in fast magnetic resonance imagine (MRI) reconstruction techniques is presented, focusing on their roles in enhancing image quality and reducing acquisition times. The first set of methods, parallel imaging techniques, includes sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE utilizes spatial sensitivity information from multiple receiver coils to accelerate image acquisition by undersampling k-space data and reconstructing images using coil sensitivity profiles, allowing for faster scans. GRAPPA, another parallel imaging method, uses estimated weights from a calibration scan to fill in missing data in undersampled k-space and then reconstructs unaliased images. Additionally, this review explores sparse reconstruction techniques such as compressed sensing, which leverages the sparsity of images in a transformed domain to reconstruct high quality images from significantly fewer measurements, thus reducing scan times. The latest developments in machine learning applications for MRI acquisition are also discussed, highlighting how advanced algorithms are being used to improve image reconstruction, enhance diagnostic accuracy, and simplify workflow processes.