Leveraging Low-Rank and Local Low-Rank Matrix Approximation Techniques to Enhance Medical Imaging Analysis and Reconstruction

Low-rank matrix approximation (LRMA) and its extension, local low-rank matrix approximation (LLRMA), have demonstrated significant potential in addressing the challenges faced in medical imaging, such as noise, high dimensionality, and large data volumes. These techniques enable efficient compression, denoising, reconstruction, and analysis of medical images across various modalities, including MRI, CT, X-ray, ultrasound, and PET.