MRI Image Compressed Sensing Using Bregman Algorithm

In this article, we explore the application of Bregman algorithm for compressed sensing of MRI images. MRI (Magnetic Resonance Imaging) represents a crucial medical imaging technique that assists physicians in diagnosing and treating various diseases. However, MRI images typically contain substantial amounts of data, posing challenges for storage and transmission. To address this issue, researchers have developed various image compression algorithms, among which the Bregman algorithm stands out. The Bregman algorithm is a convex optimization-based approach that reduces data storage and transmission requirements while maintaining image quality. Through compressed sensing of MRI images, we can represent them as sparse coefficient vectors, enabling efficient image encoding and decoding. From an implementation perspective, this typically involves formulating the problem using L1-norm minimization with appropriate constraints, where the Bregman iteration handles the regularization terms through splitting techniques. This methodology not only reduces storage and transmission demands but also delivers high-quality reconstructed images. Thus, employing the Bregman algorithm for MRI compressed sensing represents a promising research direction. Through in-depth study and optimization of this algorithm, we can further enhance MRI compression efficiency while improving image quality and visualization. Key implementation considerations include proper parameter tuning for the regularization term and designing efficient iteration stopping criteria. These advancements will positively impact the development and application of medical imaging, enabling physicians to perform more accurate diagnoses and treatments. In summary, the Bregman algorithm demonstrates significant potential in the field of MRI compressed sensing. By implementing this algorithm for MRI image compression, we achieve efficient processing and transmission of image data, thereby providing better support for medical diagnosis and treatment. The algorithm's core strength lies in its ability to handle non-smooth optimization problems through Bregman distance minimization, making it particularly suitable for medical image reconstruction tasks.