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10.1038/srep27614 http://scihub22266oqcxt.onion/10.1038/srep27614 C4904377!4904377 !27291259     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 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\27291259 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2016 ; 6 (ä): 27614 Nephropedia Template TP {{tp|p=27291259 |t=2016. Compressed Sensing Electron Tomography for Determining Biological Structure |pdf=|usr=}}{{27291259 }} gab.com Text Compressed Sensing Electron Tomography for Determining Biological Structure JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27291259 #FOAvip There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets. Twit Text FOAVip Compressed Sensing Electron Tomography for Determining Biological Structure ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27291259 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27291259 #FOAvip English Wikipedia <ref>{{Cite pmid|27291259 }}</ref> Compressed Sensing Electron Tomography for Determining Biological Structure #MMPMID27291259 Guay MD ; Czaja W ; Aronova MA ; Leapman RD Sci Rep 2016[Jun]; 6 (ä): 27614 PMID27291259 show ga There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets. |*Algorithms [MESH] |Animals [MESH] |Artifacts [MESH] |Data Compression/*methods [MESH] |Electron Microscope Tomography/instrumentation/*methods [MESH] |Image Processing, Computer-Assisted/*methods/statistics & numerical data [MESH] |Islets of Langerhans/ultrastructure [MESH] |Mice [MESH]Compressed Sensing Electron Tomography for Determining Biological Stru(epmc).pdf DeepDyve Pubget Overpricing