A General Formula for Fan-Beam Lambda Tomography
Lambda tomography (LT) is to reconstruct a gradient-like image of an object only from local projection data. It is potentially an important technology for medical X-ray computed tomography (CT) at a reduced radiation dose. In this paper, we prove the first general formula for exact and efficient fan...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2006-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/IJBI/2006/10427 |
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| Summary: | Lambda tomography (LT) is to reconstruct a gradient-like image of
an object only from local projection data. It is potentially an
important technology for medical X-ray computed tomography (CT) at
a reduced radiation dose. In this paper, we prove the first
general formula for exact and efficient fan-beam LT from data
collected along any smooth curve based on even and odd data
extensions. As a result, an LT image can be reconstructed without
involving any data extension. This implies that structures outside
a scanning trajectory do not affect the exact reconstruction of
points inside the trajectory even if the data may be measured
through the outside features. The algorithm is simulated in a
collinear coordinate system. The results support our theoretical
analysis. |
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| ISSN: | 1687-4188 1687-4196 |