Comparison of Rigid and Adaptive Methods of Propagating Gross Tumor Volume Through Respiratory Phases of Four-Dimensional Computed Tomography Image Data Set

2008 ◽  
Vol 71 (1) ◽  
pp. 290-296 ◽  
Author(s):  
Muthuveni Ezhil ◽  
Bum Choi ◽  
George Starkschall ◽  
M. Kara Bucci ◽  
Sastry Vedam ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Gross Tumor Volume ◽  
Tumor Volume ◽  
Image Data ◽  
Adaptive Methods ◽  
Data Set ◽  
Tomography Image ◽  
Computed Tomography Image ◽  
Respiratory Phases

scholarly journals Physics-informed Deep Learning for Dual-Energy Computed Tomography Image Processing

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Maarten G. Poirot ◽  
Rick H. J. Bergmans ◽  
Bart R. Thomson ◽  
Florine C. Jolink ◽  
Sarah J. Moum ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Signal To Noise Ratio ◽  
Image Data ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Decomposition Algorithms ◽  
Generation Process ◽  
Tomography Image ◽  
Computed Tomography Image ◽  
Anatomic Information

AbstractDual-energy CT (DECT) was introduced to address the inability of conventional single-energy computed tomography (SECT) to distinguish materials with similar absorbances but different elemental compositions. However, material decomposition algorithms based purely on the physics of the underlying attenuation process have several limitations, leading to low signal-to-noise ratio (SNR) in the derived material-specific images. To overcome these, we trained a convolutional neural network (CNN) to develop a framework to reconstruct non-contrast SECT images from DECT scans. We show that the traditional physics-based decomposition algorithms do not bring to bear the full information content of the image data. A CNN that leverages the underlying physics of the DECT image generation process as well as the anatomic information gleaned via training with actual images can generate higher fidelity processed DECT images.

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