Deep Learning-based Inaccuracy Compensation in Reconstruction of High Resolution XCT Data
Abstract While X-ray computed tomography (XCT) is pushed further into the micro- and nanoscale, the limitations of various tool components and object motion become more apparent. For high-resolution XCT, it is necessary but practically difficult to align these tool components with sub-micron precisi...
Guardado en:
Autores principales: | Emre Topal, Markus Löffler, Ehrenfried Zschech |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/801fdcb5db164acfad2569f816afc253 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Deep learning for irregularly and regularly missing data reconstruction
por: Xintao Chai, et al.
Publicado: (2020) -
Deep-learning based reconstruction of the stomach from monoscopic video data
por: Hackner Ralf, et al.
Publicado: (2020) -
Investigating sources of inaccuracy in wearable optical heart rate sensors
por: Brinnae Bent, et al.
Publicado: (2020) -
Fast Hyperparameter Calibration of Sparsity Enforcing Penalties in Total Generalised Variation Penalised Reconstruction Methods for XCT Using a Planted Virtual Reference Image
por: Stéphane Chrétien, et al.
Publicado: (2021) -
The glutamate/cystine xCT antiporter antagonizes glutamine metabolism and reduces nutrient flexibility
por: Chun-Shik Shin, et al.
Publicado: (2017)