86 / 2021-10-25 13:51:35
Ultrafast multidimensional MRI data acquisition with genetic algorithm
multidimensional MRI,data acquisition,sparse sampling,genetic algorithm
终稿
栎宪 王 / State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China
Fangrong Zong / Institute of Biophysics Chinese Academy of Science Beijing, China
东标 孙 / State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China
昭怡 游 / School of Life Sciences Nankai University Tianjin, China
彦 卓 / State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China;CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
蓉 薛 / State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China;Beijing Institute for Brain Disorders, Beijing, China
A new strategy for accelerating multidimensional MRI data acquisition was introduced in this paper. It is based on the Genetic Algorithm (GA) to optimize the sparse data sampling strategy by maximizing the singular values of the kernel functions. The under-sampled data were then processed using Enhanced Discerning Multidimensional Inverse Laplace Transform (EDMILT) to obtain high-resolution spectroscopic imaging results. An 80 per cent reduction in acquisition time was achieved by applying the proposed framework while retaining image quality. The feasibility of the proposed framework was validated by using a numerical simulation protocol in different brain tissue models. Our results demonstrate the performance of the proposed framework in identifying the fluid contents in brain tissue models, offering a high probability of applying multidimensional MRI techniques in diagnosing neurological diseases.
重要日期
  • 会议日期

    11月13日

    2021

    11月14日

    2021

  • 09月30日 2021

    报告提交截止日期

  • 11月14日 2021

    注册截止日期

主办单位
IEEE北京分会
中国生物医学工程学会医学物理分会
中国电子学会生命电子学分会
承办单位
中国科学技术大学
安徽省生物医学工程学会
联系方式
移动端
在手机上打开
小程序
打开微信小程序
客服
扫码或点此咨询