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ONIX : an X-ray dee...
ONIX : an X-ray deep-learning tool for 3D reconstructions from sparse views
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- Zhang, Yuhe (författare)
- Lund University,Lunds universitet,NanoLund: Centre for Nanoscience,Annan verksamhet, LTH,Lunds Tekniska Högskola,Synkrotronljusfysik,Fysiska institutionen,Institutioner vid LTH,LTH profilområde: Nanovetenskap och halvledarteknologi,LTH profilområden,LU profilområde: Ljus och material,Lunds universitets profilområden,Other operations, LTH,Faculty of Engineering, LTH,Synchrotron Radiation Research,Department of Physics,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: Nanoscience and Semiconductor Technology,LTH Profile areas,Faculty of Engineering, LTH,LU Profile Area: Light and Materials,Lund University Profile areas
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- Yao, Zisheng (författare)
- Lund University,Lunds universitet,NanoLund: Centre for Nanoscience,Annan verksamhet, LTH,Lunds Tekniska Högskola,Synkrotronljusfysik,Fysiska institutionen,Institutioner vid LTH,LTH profilområde: Nanovetenskap och halvledarteknologi,LTH profilområden,LU profilområde: Ljus och material,Lunds universitets profilområden,Other operations, LTH,Faculty of Engineering, LTH,Synchrotron Radiation Research,Department of Physics,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: Nanoscience and Semiconductor Technology,LTH Profile areas,Faculty of Engineering, LTH,LU Profile Area: Light and Materials,Lund University Profile areas
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- Ritschel, Tobias (författare)
- University College London
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- Villanueva Perez, Pablo (författare)
- Lund University,Lunds universitet,NanoLund: Centre for Nanoscience,Annan verksamhet, LTH,Lunds Tekniska Högskola,Synkrotronljusfysik,Fysiska institutionen,Institutioner vid LTH,LTH profilområde: Nanovetenskap och halvledarteknologi,LTH profilområden,LTH profilområde: Avancerade ljuskällor,LU profilområde: Ljus och material,Lunds universitets profilområden,Other operations, LTH,Faculty of Engineering, LTH,Synchrotron Radiation Research,Department of Physics,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: Nanoscience and Semiconductor Technology,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Photon Science and Technology,Faculty of Engineering, LTH,LU Profile Area: Light and Materials,Lund University Profile areas
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(creator_code:org_t)
- 2023
- 2023
- Engelska 13 s.
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Ingår i: Applied Research. - 2702-4288. ; 2:4
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Time-resolved three-dimensional (3D) X-ray imaging techniques rely on obtaining 3D information for each time point and are crucial for materials-science applications in academia and industry. Standard 3D X-ray imaging techniques like tomography and confocal microscopy access 3D information by scanning the sample with respect to the X-ray source. However, the scanning process limits the temporal resolution when studying dynamics and is not feasible for many materials-science applications, such as cell-wall rupture of metallic foams. Alternatives to obtaining 3D information when scanning is not possible are X-ray stereoscopy and multi-projection imaging, but these approaches suffer from limited volumetric information as they only acquire a very small number of views or projections compared to traditional 3D scanning techniques. Here, we present optimized neural implicit X-ray imaging (ONIX), a deep-learning algorithm capable of retrieving a continuous 3D object representation from only a small and limited set of sparse projections. ONIX is based on an accurate differentiable model of the physics of X-ray propagation. It generalizes across different instances of similar samples to overcome the limited volumetric information provided by limited sparse views. We demonstrate the capabilities of ONIX compared to state-of-the-art tomographic reconstruction algorithms by applying it to simulated and experimental datasets, where a maximum of eight projections are acquired. ONIX, although it does not have access to any volumetric information, outperforms unsupervised reconstruction algorithms, which reconstruct using single instances without generalization over different instances. We anticipate that ONIX will become a crucial tool for the X-ray community by (i) enabling the study of fast dynamics not possible today when implemented together with X-ray multi-projection imaging and (ii) enhancing the volumetric information and capabilities of X-ray stereoscopic imaging.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- NATURVETENSKAP -- Fysik -- Atom- och molekylfysik och optik (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences -- Atom and Molecular Physics and Optics (hsv//eng)
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