Comput Math Methods Med - Energy preserved sampling for compressed sensing MRI.

Tópicos

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Resumo

The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI). Simple random sampling patterns did not take into account the energy distribution in k-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD) based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS) method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA) to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2D in vivo MR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time.

Resumo Limpo

sampl pattern cost function reconstruct algorithm play import role optim compress sens magnet reson imag csmri simpl random sampl pattern take account energi distribut kspace result suboptim reconstruct mr imag therefor varieti variabl densiti vd base sampl pattern develop improv propos novel energi preserv sampl epress method besid improv cost function introduc phase correct region support matrix propos iter threshold algorithm ita solv improv cost function evalu propos epress sampl method improv cost function ita reconstruct algorithm d digit phantom d vivo mr brain healthi volunt assess demonstr propos epress method perform better vd power bko improv cost function can achiev better reconstruct qualiti convent cost function ita faster sista competit fista term comput time

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