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Magnetic resonance imaging (MRI) has been used extensively for clinical purposes to depict anatomy because of its non-invasive nature to human body. It is always desirable to enhance the resolution of MR images in order to confirm the presence of any suspicious behavior inside the body while keeping the imaging time short. At present, MR imaging is often limited by high noise levels, significant imaging artifacts and/or long data acquisition (scan) times. Advanced image reconstruction algorithms can mitigate these limitations and improve image quality. In this paper we aim to enhance image quality and shorten imaging time using Compressed sensing (CS) and parallel computing techniques.