Review on the Development and Application of Compressed Sensing in MRI for Disease Diagnosis

Magnetic Resonance Imaging (MRI) plays an irreplaceable role in clinical diagnosis due to its superior soft tissue contrast and a wide range of imaging parameters. However, its clinical efficiency is limited by prolonged scan times and sensitivity to motion artifacts. This study focuses on the clinical application of Compressive Sensing (CS) in MRI, employing a combination of systematic literature review and practical case analysis to comprehensively explore its recent developments and applications. The findings indicate that CS, based on the principles of sparsity and incoherent sampling, can achieve high-quality image reconstruction under undersampling conditions, significantly improving imaging efficiency. Nevertheless, challenges remain, including artifact issues at high acceleration rates, the need for algorithm optimization, and computational complexity. With the integration of emerging technologies such as Artificial Intelligence and multi-center clinical validation, CS technology holds promise for broader adoption and standardization in the MRI field, providing strong support for precision medicine and personalized treatment.