Magnetic resonance imaging (MRI), notwithstanding the vital information they provide , introduces issues in diagnostic work due to inherent noise. The poor signal-to-noise ratio (SNR) in MRI images demonstrates the importance of post-processing procedures. There are numerous strategies for reducing noise, but there is still a need for a solution that has both high accuracy and a fast convergence speed. In this study, we present a total variation (TV) method for noise reduction in MRI images utilizing a modified Barzilai-Borwein method(MBBM) algorithm. The proposed method is tested on three noisy and blurred images (IXI data set 3.1). The findings reveal that the reconstructed images have less noise and sharper edges. In comparison to similar existing iterative methods, Gradient projection method(GP) and Barzilai-Borwein method(BBM), the proposed method achieves higher accuracy in a substantially smaller number of iterations. The MBBM technique enhanced the quality metrics, Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) for the first data set (data 2a). For instance, the PSNR and SSIM for noise level 0.06 improved from 2.62 and 0.22 to 33.12 and 0.90, respectively. This improvement in rating criteria was also seen in the next two experiments(data 2b and data 2c). When compared to similar methods, experimental results reveal that the suggested method is faster, more precise, more successful at preserving edges, and less compu-tationally demanding. Consequently, the presented method is effective and reliable both quantitatively and qualitatively.