Abstract
In parallel magnetic resonance imaging (MRI), the quality of image reconstruction is very important. The signal to noise ratio (SNR) of reconstruction image would be obviously reduced with the increase of the acceleration factors because of the ill-posed problem in the sensitivity encoding (SENSE) reconstruction. Through in-depth analysis of the total variation (TV) regularized SENSE reconstruction model, an efficient Bregman iteration algorithm was introduced to obtain the optimal solution and improve the image reconstruction quality. In this paper, the simulation experiments were performed on the real cardiac data and brain data of MRI, respectively. The experimental results demonstrated that the proposed algorithm can alleviate the aliasing artifacts and has a better image reconstruction effect. Compared with the conventional TV regularized SENSE reconstruction algorithm, under the condition of the higher acceleration factors, the SNR has also been improved and the normalized mean squared error (NMSE) of reconstruction image is also decreased. This shows that the proposed iteration TV SENSE image reconstruction algorithm is effective and feasible.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
