TU-B-201B-04: Four-Dimensional Cone-Beam Computed Tomography and Digital Tomosynthesis Using Motion Signals Extracted from Fiducial Marker Inserted for Liver Cancer Radiation Therapy

2010 ◽  
Vol 37 (6Part6) ◽  
pp. 3378-3378
Author(s):  
J Park ◽  
S Park ◽  
J Kim ◽  
Z Liu ◽  
W Watkins ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Radiation Therapy ◽  
Liver Cancer ◽  
Cone Beam Computed Tomography ◽  
Fiducial Marker ◽  
Cone Beam ◽  
Digital Tomosynthesis ◽  
Dimensional Cone

scholarly journals Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
You Zhang ◽  
Xiaokun Huang ◽  
Jing Wang
Keyword(s):  
Computed Tomography ◽  
Radiation Therapy ◽  
Deep Learning ◽  
Image Quality ◽  
Cone Beam Computed Tomography ◽  
Adaptive Radiation ◽  
Cone Beam ◽  
Biomechanical Modeling ◽  
Reconstruction Accuracy ◽  
Dimensional Cone

Abstract4-Dimensional cone-beam computed tomography (4D-CBCT) offers several key advantages over conventional 3D-CBCT in moving target localization/delineation, structure de-blurring, target motion tracking, treatment dose accumulation and adaptive radiation therapy. However, the use of the 4D-CBCT in current radiation therapy practices has been limited, mostly due to its sub-optimal image quality from limited angular sampling of cone-beam projections. In this study, we summarized the recent developments of 4D-CBCT reconstruction techniques for image quality improvement, and introduced our developments of a new 4D-CBCT reconstruction technique which features simultaneous motion estimation and image reconstruction (SMEIR). Based on the original SMEIR scheme, biomechanical modeling-guided SMEIR (SMEIR-Bio) was introduced to further improve the reconstruction accuracy of fine details in lung 4D-CBCTs. To improve the efficiency of reconstruction, we recently developed a U-net-based deformation-vector-field (DVF) optimization technique to leverage a population-based deep learning scheme to improve the accuracy of intra-lung DVFs (SMEIR-Unet), without explicit biomechanical modeling. Details of each of the SMEIR, SMEIR-Bio and SMEIR-Unet techniques were included in this study, along with the corresponding results comparing the reconstruction accuracy in terms of CBCT images and the DVFs. We also discussed the application prospects of the SMEIR-type techniques in image-guided radiation therapy and adaptive radiation therapy, and presented potential schemes on future developments to achieve faster and more accurate 4D-CBCT imaging.

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