Enhancement of the low resolution image quality using randomly sampled data for multi-slice MR imaging.
Low resolution images are often acquired in in vivo MR applications involving in large field-of-view (FOV) and high speed imaging, such as, whole-body MRI screening and functional MRI applications. In this work, we investigate a multi-slice imaging strategy for acquiring low resolution images by using compressed sensing (CS) MRI to enhance the image quality without increasing the acquisition time. In this strategy, low resolution images of all the slices are acquired using multiple-slice imaging sequence. In addition, extra randomly sampled data in one center slice are acquired by using the CS strategy. These additional randomly sampled data are multiplied by the weighting functions generated from low resolution full k-space images of the two slices, and then interpolated into the k-space of other slices. In vivo MR images of human brain were employed to investigate the feasibility and the performance of the proposed method. Quantitative comparison between the conventional low resolution images and those from the proposed method was also performed to demonstrate the advantage of the method.
1 Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA ; 2 Magwale, Palo Alto, CA, USA ; 3 UCSF/UC Berkeley Joint Group Program in Bioengineering, San Francisco and Berkeley, CA, USA.
Yong
Y
Pang
1 Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA ; 2 Magwale, Palo Alto, CA, USA ; 3 UCSF/UC Berkeley Joint Group Program in Bioengineering, San Francisco and Berkeley, CA, USA.
Baiying
B
Yu
1 Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA ; 2 Magwale, Palo Alto, CA, USA ; 3 UCSF/UC Berkeley Joint Group Program in Bioengineering, San Francisco and Berkeley, CA, USA.
Xiaoliang
X
Zhang
Low resolution image; compressed sensing MRI; interpolated compressed sensing (CS), multiple slice imaging