Next Article in Journal / Special Issue
Evaluation of Moisture-Related Attenuation Coefficient and Water Diffusion Velocity in Human Skin Using Optical Coherence Tomography
Previous Article in Journal
DNA-Based Sensor for Real-Time Measurement of the Enzymatic Activity of Human Topoisomerase I
Previous Article in Special Issue
Rank Awareness in Group-Sparse Recovery of Multi-Echo MR Images
Sensors 2013, 13(4), 4029-4040; doi:10.3390/s130404029
Article

A Support-Based Reconstruction for SENSE MRI

,
 and *
Received: 1 March 2013; in revised form: 22 March 2013 / Accepted: 22 March 2013 / Published: 25 March 2013
(This article belongs to the Special Issue Medical & Biological Imaging)
View Full-Text   |   Download PDF [605 KB, updated 21 June 2014; original version uploaded 21 June 2014]   |   Browse Figures
Abstract: A novel, rapid algorithm to speed up and improve the reconstruction of sensitivity encoding (SENSE) MRI was proposed in this paper. The essence of the algorithm was that it iteratively solved the model of simple SENSE on a pixel-by-pixel basis in the region of support (ROS). The ROS was obtained from scout images of eight channels by morphological operations such as opening and filling. All the pixels in the FOV were paired and classified into four types, according to their spatial locations with respect to the ROS, and each with corresponding procedures of solving the inverse problem for image reconstruction. The sensitivity maps, used for the image reconstruction and covering only the ROS, were obtained by a polynomial regression model without extrapolation to keep the estimation errors small. The experiments demonstrate that the proposed method improves the reconstruction of SENSE in terms of speed and accuracy. The mean square errors (MSE) of our reconstruction is reduced by 16.05% for a 2D brain MR image and the mean MSE over the whole slices in a 3D brain MRI is reduced by 30.44% compared to those of the traditional methods. The computation time is only 25%, 45%, and 70% of the traditional method for images with numbers of pixels in the orders of 103, 104, and 105–107, respectively.
Keywords: parallel imaging; sensitivity encoding; magnetic resonance imaging; region of support; sensitivity maps; polynomial model; morphological operator parallel imaging; sensitivity encoding; magnetic resonance imaging; region of support; sensitivity maps; polynomial model; morphological operator
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Zhang, Y.; Peterson, B.S.; Dong, Z. A Support-Based Reconstruction for SENSE MRI. Sensors 2013, 13, 4029-4040.

AMA Style

Zhang Y, Peterson BS, Dong Z. A Support-Based Reconstruction for SENSE MRI. Sensors. 2013; 13(4):4029-4040.

Chicago/Turabian Style

Zhang, Yudong; Peterson, Bradley S.; Dong, Zhengchao. 2013. "A Support-Based Reconstruction for SENSE MRI." Sensors 13, no. 4: 4029-4040.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert