{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:31:23Z","timestamp":1760243483159,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2013,3,25]],"date-time":"2013-03-25T00:00:00Z","timestamp":1364169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013107, respectively.<\/jats:p>","DOI":"10.3390\/s130404029","type":"journal-article","created":{"date-parts":[[2013,3,26]],"date-time":"2013-03-26T04:34:12Z","timestamp":1364272452000},"page":"4029-4040","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Support-Based Reconstruction for SENSE MRI"],"prefix":"10.3390","volume":"13","author":[{"given":"Yudong","family":"Zhang","sequence":"first","affiliation":[{"name":"Brain Imaging Lab & MRI Unit, New York State Psychiatry Institute & Columbia University,  New York, NY 10032, USA"}]},{"given":"Bradley","family":"Peterson","sequence":"additional","affiliation":[{"name":"Brain Imaging Lab & MRI Unit, New York State Psychiatry Institute & Columbia University,  New York, NY 10032, USA"}]},{"given":"Zhengchao","family":"Dong","sequence":"additional","affiliation":[{"name":"Brain Imaging Lab & MRI Unit, New York State Psychiatry Institute & Columbia University,  New York, NY 10032, USA"}]}],"member":"1968","published-online":{"date-parts":[[2013,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.mri.2009.11.007","article-title":"SNR-optimized myocardial perfusion imaging using parallel acquisition for effective density-weighted saturation recovery imaging","volume":"28","author":"Gutberlet","year":"2010","journal-title":"Magn. Reson. Imag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.mri.2004.01.010","article-title":"Dual double arterial phase dynamic MR imaging with sensitivity encoding (SENSE): Which is better for diagnosing hypervascular hepatocellular carcinomas, in-phase or opposed-phase imaging?","volume":"22","author":"Yoshioka","year":"2004","journal-title":"Magn. Reson. Imag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1002\/mrm.10171","article-title":"Generalized autocalibrating partially parallel acquisitions (GRAPPA)","volume":"47","author":"Griswold","year":"2002","journal-title":"Magn. Reson. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/S1352-8661(01)00144-2","article-title":"Recent advances in image reconstruction, coil sensitivity calibration, and coil array design for SMASH and generalized parallel MRI","volume":"13","author":"Sodickson","year":"2002","journal-title":"Magn. Reson. Mat. Biol. Phys. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/BF02668182","article-title":"2D SENSE for faster 3D MRI","volume":"14","author":"Weiger","year":"2002","journal-title":"Magn. Reson. Mat. Biol. Phys. Med."},{"doi-asserted-by":"crossref","unstructured":"Liang, D., Liu, B., and Ying, L. (2008, January 20\u201325). Accelerating Sensitivity Encoding Using Compressed Sensing. Vancouver, BC, Canada.","key":"ref_6","DOI":"10.1109\/IEMBS.2008.4649495"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/TMI.2010.2093536","article-title":"Parallel MR image reconstruction using augmented Lagrangian methods","volume":"30","author":"Ramani","year":"2011","journal-title":"IEEE Trans. Med. Imag."},{"doi-asserted-by":"crossref","unstructured":"Allison, M.J., Ramani, S., and Fessler, J.A. (2012, January 2\u20135). Regularized MR Coil Sensitivity Estimation Using Augmented Lagrangian Methods. 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Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1002\/cmr.a.20160","article-title":"A graphical generalized implementation of SENSE reconstruction using Matlab","volume":"36A","author":"Omer","year":"2010","journal-title":"Concept. Magn. Reson. Part A"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1016\/j.mri.2007.01.003","article-title":"Optimization of sensitivity encoding with arbitrary k-space trajectories","volume":"25","author":"Bydder","year":"2007","journal-title":"Magn. Reson. Imag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.mric.2009.09.001","article-title":"Origins of Intraoperative MRI","volume":"18","author":"Mislow","year":"2010","journal-title":"Magn. Reson. Imag. Clin. N. Am."},{"unstructured":"Chaari, L., M\u00e9riaux, S., Badillo, S., Pesquet, J., and Ciuciu, P. Multidimensional Wavelet-Based Regularized Reconstruction for Parallel Acquisition in Neuroimaging. 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