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MR images reconstruction need many samples that are sequentially sampled by phase encoding gradients in MRI system. MRI takes long scan time, therefore, many researchers have been studied to reduce scan time. Especially, the Compressed Sensing (CS) that is used sparse images and reconstruction from fewer sampling data which the k-space is not fully sampled. Recently, an iterative technique based on Bregman method is developed for denoising. The Bregman iteration method improves on the Total Variation (TV) regularization by gradually recovering the fine scale structures that are usually lost in the TV regularization. In this study, we studied sparse sampling image reconstruction using Bregman iteration at low tesla MRI system for improving the temporal resolution and validated the usefulness. The image was obtained at 0.32T MRI scanner (Magfinder II, Genpia, Korea) using 2D T1-weighed spin-echo pulse sequence with phantom and in-vivo human brain in the head coil. We applied the random k-space sampling and sampling ratios are determined by half of fully sampled k-space. The Bregman iteration was used to generate the final images based on the reduced data. The number of Bregman iterations used for the reconstruction was minimum 1 to maximum 100. We also calculated Root Mean Square Error (RMSE) values from error images that were performed according to number of bregman iterations. The results which are reconstructed images using the bregman iteration to sparse sampling image shown well reconstruction images compared with original images. Moreover, the RMSE values can be seen that sparse reconstructed phantom image and human images are converge to the original image. We confirmed the feasibility of sparse sampling image reconstruction methods using Bregman iteration at low tesla MRI system and obtained good results. Although our results used half of sampling ratio, this method will helpful to increase the temporal resolution at low tesla MRI system.