{"title":"Real-time micro-CT reconstruction with zooming to features of interest (Conference Presentation)","authors":"Viktor Nikitin","doi":"10.1117/12.2636637","DOIUrl":"https://doi.org/10.1117/12.2636637","url":null,"abstract":"","PeriodicalId":308963,"journal":{"name":"Developments in X-Ray Tomography XIV","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116479332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Croton, K. Crossley, C. Mcdonald, K. Morgan, T. Penny, R. Galinsky, S. Hooper, G. Ruben, J. Pollock, F. Schaff, M. Croughan, A. Sutherland, M. Kitchen
{"title":"Pre-clinical phase-contrast imaging of the whole brain in situ: from traumatic to diffuse white matter injury","authors":"L. Croton, K. Crossley, C. Mcdonald, K. Morgan, T. Penny, R. Galinsky, S. Hooper, G. Ruben, J. Pollock, F. Schaff, M. Croughan, A. Sutherland, M. Kitchen","doi":"10.1117/12.2633340","DOIUrl":"https://doi.org/10.1117/12.2633340","url":null,"abstract":"","PeriodicalId":308963,"journal":{"name":"Developments in X-Ray Tomography XIV","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133585007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Doherty, Savvis Savidis, A. Astolfo, L. Massimi, Nargiza Djurabekova, Carlos Navarrete Leon, M. Gerli, F. Iacoviello, P. Shearing, D. Norman, M. Williams, A. Olivo, M. Endrizzi
{"title":"X-ray dark-field tomography using edge-illumination","authors":"A. Doherty, Savvis Savidis, A. Astolfo, L. Massimi, Nargiza Djurabekova, Carlos Navarrete Leon, M. Gerli, F. Iacoviello, P. Shearing, D. Norman, M. Williams, A. Olivo, M. Endrizzi","doi":"10.1117/12.2632731","DOIUrl":"https://doi.org/10.1117/12.2632731","url":null,"abstract":"X-ray dark-field imaging is used to visualise the ultra-small angle x-ray scattering signal that originates from sub-resolution density fluctuations within the sample microstructure. Dark-field tomography using the edge-illumination x-ray imaging system is presented as a tool for measuring this scattering signal in a sample in three dimensions. Its applicability to different fields is shown through example images of a multi-material phantom, a tissue-engineered oesophagus, a pouch cell battery and a short-fibre reinforced composite material. The multi-channel contrast available in edge-illumination helps with material identification, with high contrast at boundaries enhancing dark-field reconstructions","PeriodicalId":308963,"journal":{"name":"Developments in X-Ray Tomography XIV","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128812488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Shallow U-Net deep learning approach for phase retrieval in propagation-based phase-contrast Imaging","authors":"Samuel Z. Li, Matthew G. French, K. Pavlov, H. Li","doi":"10.1117/12.2644579","DOIUrl":"https://doi.org/10.1117/12.2644579","url":null,"abstract":"X-Ray Computed Tomography (CT) has revolutionised modern medical imaging. However, X-Ray CT imaging requires patients to be exposed to radiation, which can increase the risk of cancer. Therefore there exists an aim to reduce radiation doses for CT imaging without sacrificing image accuracy. This research combines phase retrieval with the ShallowU-Net CNN method to achieve the aim. This paper shows that a significant change in existing machine learning neural network algorithms could improve the X-ray phase retrieval in propagation-based phase-contrast imaging. This paper applies deep learning methods, through a variant of the existing U-Net architecture, named ShallowU-Net, to show that it is possible to perform two distance X-ray phase retrieval on composite materials by predicting a portion of the required data. ShallowU-Net is faster in training and in deployment. This method also performs data stretching and pre-processing, to reduce the numerical instability of the U-Net algorithm thereby improving the phase retrieval images.","PeriodicalId":308963,"journal":{"name":"Developments in X-Ray Tomography XIV","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133856691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Iuso, S. Chatterjee, R. Heylen, S. Cornelissen, J. De Beenhouwer, Jan Sijbers
{"title":"Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing","authors":"D. Iuso, S. Chatterjee, R. Heylen, S. Cornelissen, J. De Beenhouwer, Jan Sijbers","doi":"10.1117/12.2633318","DOIUrl":"https://doi.org/10.1117/12.2633318","url":null,"abstract":"Additive manufacturing (AM) is increasingly gaining interest as a low-waste production technique, capable of producing objects using a computer-aided design file. It is particularly interesting for rapid prototyping of parts and manufacturing objects that have complex shapes. However, as in the case of AM through selective laser melting (SLM), manufactured objects may contain defects that can seriously alter their properties. These defects may appear as pores, which can be detected by X-ray computed tomography (X-CT) in a non-destructive manner. CT images can simply be segmented by thresholding or through more advanced techniques such as discrete X-CT reconstruction or machine learning techniques. Nevertheless, these techniques are vulnerable to image reconstruction artefacts. In this work, we evaluate the performance of state-of-the-art, deeply supervised 3D deep learning networks (UNet++, UNet 3+ and UNet-MSS) in terms of segmentation performance of pores from X-ray CT images. The networks have been trained on a real CT dataset, with (noisy) labels produced from both conventional thresholding of the CT images as well as more advanced discrete polychromatic reconstructions. Furthermore, the performance of the networks was evaluated on a test dataset with severe CT artefacts. Pore segmentation from real CT images, which include noise and reconstruction artefacts, revealed that the best performing network was UNet++ with an average Sørensen–Dice score of 0.869 ± 0.006.","PeriodicalId":308963,"journal":{"name":"Developments in X-Ray Tomography XIV","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124695796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}