چکیده مقاله
Compressive sensing CS utilizes sparsity of MRI images for accurate reconstruction of under sampled k space data In order to use MRI in CS, k space image should be sampled and then CS techniques should be applied Although most common sampling methods in CS framework may have good properties, they are not optimal in image reconstruction due to their finite data In this paper, a new method will be presented for adaptive sampling consisting of two updating steps: namely as sampling method and image update steps Given reconstructions are used in sampling update step and fixed sampling method are used in image update step besides convergence in PSNR Wavelet transform and image blocking are also applied The blocks used in the adaptive stage are chosen spirally leading to less calculations and maintaining low frequency image information in the centre of k space Simulation results indicated 7 5dB improvement in PSNR reconstruction using adaptive sampling
کلیدواژهها
نویسندگان
MohammadReza Ghavidel Aghdam
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Tohid Yousefi Rezaii
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
شیوه ارجاع
Ghavidel Aghdam, MohammadReza and Yousefi Rezaii, Tohid,1396,An Adaptive Method for Under-sampling of MRI Images Based on Compressive Sensing,Fifth International Conference on Electrical and Computer Engineering with Emphasis on Indigenous Knowledge,Tehran
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی مهندسی برق و کامپیوتر با تاکید بر دانش بومی19 بهمن 1396 · تهران