{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T05:50:49Z","timestamp":1725256249756},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T00:00:00Z","timestamp":1665878400000},"content-version":"am","delay-in-days":349,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.com.au","clinicalkey.es","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1016\/j.compbiomed.2021.104775","type":"journal-article","created":{"date-parts":[[2021,8,21]],"date-time":"2021-08-21T14:21:14Z","timestamp":1629555674000},"page":"104775","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":14,"special_numbering":"C","title":["Self-supervised CT super-resolution with hybrid model"],"prefix":"10.1016","volume":"138","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5333-1394","authenticated-orcid":false,"given":"Zhicheng","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3412-2159","authenticated-orcid":false,"given":"Shaode","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Wenjian","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Xiaokun","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Yaoqin","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Guohua","family":"Cao","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.compbiomed.2021.104775_bib1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/cancers12061672","article-title":"Recommendations for implementing lung cancer screening with low-dose computed tomography in Europe","volume":"12","author":"Veronesi","year":"2020","journal-title":"Cancers"},{"issue":"1","key":"10.1016\/j.compbiomed.2021.104775_bib2","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1080\/22221751.2020.1750307","article-title":"Diagnostic value and key features of computed tomography in Coronavirus Disease 2019","volume":"9","author":"Li","year":"2020","journal-title":"Emerg. Microb. Infect."},{"key":"10.1016\/j.compbiomed.2021.104775_bib3","doi-asserted-by":"crossref","first-page":"102702","DOI":"10.1016\/j.micron.2019.102702","article-title":"A review of high-resolution X-ray computed tomography applied to petroleum geology and a case study","volume":"124","author":"Zhang","year":"2019","journal-title":"Micron"},{"issue":"2","key":"10.1016\/j.compbiomed.2021.104775_bib4","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1007\/s10439-014-0974-z","article-title":"Recent and future directions in CT imaging norbert","volume":"42","author":"Pelc","year":"2014","journal-title":"Ann. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.compbiomed.2021.104775_bib5","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1097\/RLI.0000000000000601","article-title":"Recent and upcoming technological developments in computed tomography: high speed, low dose, deep learning, multienergy","volume":"55","author":"Lell","year":"2020","journal-title":"Invest. Radiol."},{"key":"10.1016\/j.compbiomed.2021.104775_bib6","article-title":"The role of multidetector computed tomography in characterization and differentiation of retroperitoneal masses","author":"M Abdelhay","year":"2021","journal-title":"Zagazig Univ. Med. J."},{"issue":"1","key":"10.1016\/j.compbiomed.2021.104775_bib7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-50711-1","article-title":"Ex vivo evaluation of an atherosclerotic human coronary artery via histology and high-resolution hard X-ray tomography","volume":"9","author":"Buscema","year":"2019","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.compbiomed.2021.104775_bib8","doi-asserted-by":"crossref","first-page":"213","DOI":"10.2967\/jnmt.106.037846","article-title":"Principles of CT: radiation dose and image quality","volume":"35","author":"Goldman","year":"2007","journal-title":"J. Nucl. Med. Technol."},{"issue":"10","key":"10.1016\/j.compbiomed.2021.104775_bib9","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1016\/j.ejmp.2016.06.016","article-title":"A novel design of ultrafast micro-CT system based on carbon nanotube: a feasibility study in phantom","volume":"32","author":"Zhang","year":"2016","journal-title":"Phys. Med."},{"issue":"May","key":"10.1016\/j.compbiomed.2021.104775_bib10","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ejmp.2020.05.002","article-title":"Spatial resolution compensation by adjusting the reconstruction kernels for iterative reconstruction images of computed tomography","volume":"74","author":"Sugisawa","year":"2020","journal-title":"Phys. Med."},{"key":"10.1016\/j.compbiomed.2021.104775_bib11","series-title":"Sinogram Rebinning and Frequency Boosting for High Resolution Iterative CT Reconstruction with Focal Spot Deflection","author":"Wang","year":"2014"},{"issue":"1","key":"10.1016\/j.compbiomed.2021.104775_bib12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1097\/RCT.0000000000000963","article-title":"Effect of ultra high-resolution computed tomography and model-based iterative reconstruction on detectability of simulated submillimeter artery","volume":"44","author":"Morisaka","year":"2020","journal-title":"J. Comput. Assist. Tomogr."},{"issue":"4","key":"10.1016\/j.compbiomed.2021.104775_bib13","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theor."},{"issue":"4","key":"10.1016\/j.compbiomed.2021.104775_bib14","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1002\/mrm.28245","article-title":"Improved compressed sensing and super-resolution of cardiac diffusion MRI with structure-guided total variation","volume":"84","author":"Teh","year":"2020","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.compbiomed.2021.104775_bib15","doi-asserted-by":"crossref","first-page":"3084","DOI":"10.1109\/TIP.2021.3058590","article-title":"Spatial-spectral structured sparse low-rank representation for hyperspectral image super-resolution","volume":"30","author":"Xue","year":"2021","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"10.1016\/j.compbiomed.2021.104775_bib16","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1049\/iet-ipr.2019.1312","article-title":"Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm","volume":"14","author":"Deeba","year":"2020","journal-title":"IET Image Process."},{"issue":"5","key":"10.1016\/j.compbiomed.2021.104775_bib17","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1002\/mp.14785","article-title":"Modularized data-driven reconstruction framework for nonideal focal spot effect elimination in computed tomography","volume":"48","author":"Zhang","year":"2021","journal-title":"Med. Phys."},{"issue":"7553","key":"10.1016\/j.compbiomed.2021.104775_bib18","first-page":"436","volume":"521","author":"LeCun","year":"2015","journal-title":"\u201cDeep learning,\u201d Nature"},{"key":"10.1016\/j.compbiomed.2021.104775_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102156","article-title":"Incorporating the hybrid deformable model for improving the performance of abdominal CT segmentation via multi-scale feature fusion network","author":"Liang","year":"2021","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.compbiomed.2021.104775_bib20","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0176632","article-title":"A shallow convolutional neural network for blind image sharpness assessment","volume":"12","author":"Yu","year":"2017","journal-title":"PloS One"},{"issue":"5","key":"10.1016\/j.compbiomed.2021.104775_bib21","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","article-title":"Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks","volume":"35","author":"Setio","year":"2016","journal-title":"IEEE Trans. Med. Imag."},{"issue":"6","key":"10.1016\/j.compbiomed.2021.104775_bib22","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TMI.2018.2833635","article-title":"Image reconstruction is a new frontier of machine learning","volume":"37","author":"Wang","year":"2018","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.compbiomed.2021.104775_bib23","doi-asserted-by":"crossref","DOI":"10.1002\/mp.15119","article-title":"Noise2Context: context-assisted learning 3D thin-layer for low dose CT","author":"Zhang","year":"2021","journal-title":"Med. Phys."},{"key":"10.1016\/j.compbiomed.2021.104775_bib24","series-title":"TransCT: Dual-Path Transformer for Low Dose Computed Tomography","first-page":"1","author":"Zhang","year":"2021"},{"issue":"1","key":"10.1016\/j.compbiomed.2021.104775_bib25","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TMI.2019.2922960","article-title":"CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble(GAN-CIRCLE)","volume":"39","author":"You","year":"2019","journal-title":"IEEE Trans. Med. Imag."},{"issue":"2","key":"10.1016\/j.compbiomed.2021.104775_bib26","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compbiomed.2021.104775_bib27","series-title":"Accurate Image Super-resolution Using Very Deep Convolutional Networks","first-page":"1646","author":"Kim","year":"2016"},{"key":"10.1016\/j.compbiomed.2021.104775_bib28","series-title":"Deeply-Recursive Convolutional Network for Image Super-resolution","first-page":"1637","author":"Kim","year":"2016"},{"key":"10.1016\/j.compbiomed.2021.104775_bib29","series-title":"Residual Dense Network for Image Super-resolution","author":"Zhang","year":"2018"},{"key":"10.1016\/j.compbiomed.2021.104775_bib30","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/TMM.2020.3006414","article-title":"Supervised pixel-wise GAN for face super-resolution","volume":"23","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.compbiomed.2021.104775_bib31","series-title":"ACM International Conference on Multimedia","first-page":"1891","article-title":"PCA-SRGAN: incremental orthogonal projection discrimination for face super-resolution","author":"Dou","year":"2020"},{"key":"10.1016\/j.compbiomed.2021.104775_bib32","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neunet.2020.01.029","article-title":"PET image super-resolution using generative adversarial networks","volume":"125","author":"Song","year":"2020","journal-title":"Neural Network."},{"key":"10.1016\/j.compbiomed.2021.104775_bib33","first-page":"1","article-title":"Dual-path deep fusion network for face image hallucination","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.compbiomed.2021.104775_bib34","series-title":"Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition","first-page":"690","article-title":"Attention-aware face hallucination via deep reinforcement learning","author":"Cao","year":"2017"},{"key":"10.1016\/j.compbiomed.2021.104775_bib35","first-page":"2160","article-title":"Multi-step reinforcement learning for single image super-resolution","author":"Vassilo","year":"2020","journal-title":"in CVPR"},{"issue":"10","key":"10.1016\/j.compbiomed.2021.104775_bib36","doi-asserted-by":"crossref","first-page":"2734","DOI":"10.1109\/TMM.2019.2960586","article-title":"ATMFN: adaptive-threshold-based multi-model fusion network for compressed face hallucination","volume":"22","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Multimed."},{"issue":"11","key":"10.1016\/j.compbiomed.2021.104775_bib37","doi-asserted-by":"crossref","first-page":"4694","DOI":"10.1109\/TCYB.2018.2890149","article-title":"Ensemble super-resolution with a reference dataset","volume":"50","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.compbiomed.2021.104775_bib38","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/TCI.2020.2964201","article-title":"MRI super-resolution with ensemble learning and complementary priors","volume":"6","author":"Lyu","year":"2020","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"4","key":"10.1016\/j.compbiomed.2021.104775_bib39","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1109\/TMI.2015.2508780","article-title":"Relaxed linearized algorithms for faster X-ray CT image reconstruction","volume":"35","author":"Nien","year":"2016","journal-title":"IEEE Trans. Med. Imag."},{"issue":"12","key":"10.1016\/j.compbiomed.2021.104775_bib40","doi-asserted-by":"crossref","first-page":"5904","DOI":"10.1109\/TIP.2018.2860685","article-title":"Single image super-resolution via multiple mixture prior models","volume":"27","author":"Huang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.compbiomed.2021.104775_bib41","first-page":"2032","article-title":"Deblurring techniques - a comprehensive survey","author":"Sankaraiah","year":"2018","journal-title":"in ICPCSI"},{"issue":"2","key":"10.1016\/j.compbiomed.2021.104775_bib42","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s11263-008-0197-6","article-title":"Fields of experts","volume":"82","author":"Roth","year":"2009","journal-title":"Int. J. Comput. Vis."},{"issue":"10","key":"10.1016\/j.compbiomed.2021.104775_bib43","article-title":"Convolutional networks for images, speech, and time-series","volume":"3361","author":"Yann","year":"1995","journal-title":"Handb. brain theory neural networks"},{"issue":"6","key":"10.1016\/j.compbiomed.2021.104775_bib44","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/TMI.2018.2805692","article-title":"LEARN: learned experts assessment-based reconstruction network for sparse-data CT","volume":"37","author":"Chen","year":"2018","journal-title":"IEEE Trans. Med. Imag."},{"issue":"1","key":"10.1016\/j.compbiomed.2021.104775_bib45","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1097\/00004728-198102000-00016","article-title":"Attenuation correction in gamma emission computed tompgraphy","volume":"5","author":"Walters","year":"1981","journal-title":"J. Comput. Assist. Tomogr."},{"key":"10.1016\/j.compbiomed.2021.104775_bib46","doi-asserted-by":"crossref","first-page":"110023","DOI":"10.1016\/j.chaos.2020.110023","article-title":"Modeling and prediction of COVID-19 pandemic using Gaussian mixture model","volume":"138","author":"Singhal","year":"2020","journal-title":"Chaos, Solit. Fractals"},{"issue":"6","key":"10.1016\/j.compbiomed.2021.104775_bib47","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1109\/TMI.2018.2823338","article-title":"A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution","volume":"37","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Med. Imag."},{"issue":"1","key":"10.1016\/j.compbiomed.2021.104775_bib48","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss functions for image restoration with neural networks","volume":"3","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"4","key":"10.1016\/j.compbiomed.2021.104775_bib49","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s10278-017-0033-z","article-title":"Application of super-resolution convolutional neural network for enhancing image resolution in chest CT","volume":"31","author":"Umehara","year":"2018","journal-title":"J. Digit. Imag."},{"issue":"August","key":"10.1016\/j.compbiomed.2021.104775_bib50","doi-asserted-by":"crossref","first-page":"104314","DOI":"10.1016\/j.cageo.2019.104314","article-title":"CT-image of rock samples super resolution using 3D convolutional neural network","volume":"133","author":"Wang","year":"2019","journal-title":"Comput. Geosci."},{"issue":"4","key":"10.1016\/j.compbiomed.2021.104775_bib51","doi-asserted-by":"crossref","first-page":"331","DOI":"10.5624\/isd.2020.50.4.331","article-title":"Very deep super-resolution for efficient cone-beam computed tomographic image restoration","volume":"50","author":"Hwang","year":"2020","journal-title":"Imaging Sci. Dent."},{"issue":"July","key":"10.1016\/j.compbiomed.2021.104775_bib52","doi-asserted-by":"crossref","first-page":"106261","DOI":"10.1016\/j.petrol.2019.106261","article-title":"Enhancing resolution of digital rock images with super resolution convolutional neural networks","volume":"182","author":"Da Wang","year":"2019","journal-title":"J. Petrol. Sci. Eng."},{"key":"10.1016\/j.compbiomed.2021.104775_bib53","article-title":"Enhanced deep residual networks for single image super-resolution","author":"Lim","year":"2017","journal-title":"in CVPR"},{"key":"10.1016\/j.compbiomed.2021.104775_bib54","first-page":"3118","article-title":"Zero-shot super-resolution using deep internal learning","author":"Shocher","year":"2018","journal-title":"IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn."},{"issue":"2","key":"10.1016\/j.compbiomed.2021.104775_bib56","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/TMI.2017.2741259","article-title":"Physics model-based scatter correction in multi-source interior computed tomography","volume":"37","author":"Gong","year":"2018","journal-title":"IEEE Trans. Med. Imag."},{"issue":"4","key":"10.1016\/j.compbiomed.2021.104775_bib57","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"10.1016\/j.compbiomed.2021.104775_bib58","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1109\/TIP.2005.859389","article-title":"An information fidelity criterion for image quality assessment using natural scene statistics","volume":"14","author":"Sheikh","year":"2005","journal-title":"IEEE Trans. Image Process."},{"issue":"5","key":"10.1016\/j.compbiomed.2021.104775_bib59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1118\/1.4800795","article-title":"A simple approach to measure computed tomography ( CT ) modulation transfer function ( MTF ) and noise-power spectrum ( NPS ) using the American College of Radiology ( ACR ) accreditation phantom","volume":"40","author":"Friedman","year":"2013","journal-title":"Med. Phys."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482521005692?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482521005692?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T19:48:38Z","timestamp":1706125718000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482521005692"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":58,"alternative-id":["S0010482521005692"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104775","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2021,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Self-supervised CT super-resolution with hybrid model","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104775","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"104775"}}  <script>parent.window.postMessage('ixistenz.url:https://api.crossref.org/works/10.1016%2FJ.COMPBIOMED.2021.104775', '*'); </script> <div id='browserdiffcontainer' style='position: fixed; right: 10px; top: 10vh; min-width: 60px;  border: 1px solid white; background: rgb(251, 189, 0);; opacity: 0.9; font-family: helvetica, arial, sans serif;  font-size: 12px; z-index: 20001; padding-bottom: 20px;'><div onclick="toggle('browserdiffcontainerlist'); return false;" style='background: rgb(151, 89, 0);; color: white; '><strong>&nbsp; <span class='glyphicon glyphicon-minus'></span> NODES</strong></div><div id='browserdiffcontainerlist' style='padding-left: 10px; padding-right: 5px; max-height: 50vh; overflow: auto; '><div><a href='#diffid884' style='color: white;' onClick="parent.window.postMessage('ixistenz.opendiff:884', '*');; return false;" style='text-decoration: none; '>Idea </a> <a style='color: white;' href='#diffid884'><sup>1</sup></a></div><div><a href='#diffid119' style='color: white;' onClick="parent.window.postMessage('ixistenz.opendiff:119', '*');; return false;" style='text-decoration: none; '>idea </a> <a style='color: white;' href='#diffid119'><sup>1</sup></a></div><div><a href='#diffid123' style='color: white;' onClick="parent.window.postMessage('ixistenz.opendiff:123', '*');; return false;" style='text-decoration: none; '>INTERN </a> <a style='color: white;' href='#diffid123'><sup>2</sup></a></div><div><a href='#diffid115' style='color: white;' onClick="parent.window.postMessage('ixistenz.opendiff:115', '*');; return false;" style='text-decoration: none; '>Project </a> <a style='color: white;' href='#diffid115'><sup>1</sup></a></div></div></div>
<script>
function toggle( divname ) {
  var x = document.getElementById( divname );
  if (x.style.display === 'none') {
    x.style.display = 'block';
  } else {
    x.style.display = 'none';
  }
} </script>