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10.1088/0031-9155/60/7/2803 http://scihub22266oqcxt.onion/10.1088/0031-9155/60/7/2803 C4394841!4394841 !25779991     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25779991 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Phys+Med+Biol 2015 ; 60 (7 ): 2803-18 Nephropedia Template TP {{tp|p=25779991 |t=2015. Tensor-based dictionary learning for dynamic tomographic reconstruction |pdf=|usr=}}{{25779991 }} gab.com Text Tensor-based dictionary learning for dynamic tomographic reconstruction JO: Phys Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=25779991 #FOAvip In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction. Twit Text FOAVip Tensor-based dictionary learning for dynamic tomographic reconstruction ????Phys Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=25779991 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25779991 #FOAvip English Wikipedia <ref>{{Cite pmid|25779991 }}</ref> Tensor-based dictionary learning for dynamic tomographic reconstruction #MMPMID25779991 Tan S ; Zhang Y ; Wang G ; Mou X ; Cao G ; Wu Z ; Yu H Phys Med Biol 2015[Apr]; 60 (7 ): 2803-18 PMID25779991 show ga In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction. |Algorithms [MESH] |Animals [MESH] |Heart/diagnostic imaging [MESH] |Least-Squares Analysis [MESH] |Mice [MESH] |Models, Statistical [MESH] |Pattern Recognition, Automated [MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods [MESH] |Sheep [MESH] |Signal Processing, Computer-Assisted [MESH]Tensor-based dictionary learning for dynamic tomographic reconstructio(epmc).pdf DeepDyve Pubget Overpricing