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Article overview
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Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy | | Ruijiang Li
; Xun Jia
; John H. Lewis
; Xuejun Gu
; Michael Folkerts
; Chunhua Men
; Steve B. Jiang
; | | Date: |
31 Mar 2010 | | Abstract: | Purpose: To develop an algorithm for real-time volumetric image
reconstruction and 3D tumor localization based on a single x-ray projection
image for lung cancer radiotherapy. Methods: Given a set of volumetric images
of a patient at N breathing phases as the training data, we perform deformable
image registration between a reference phase and the other N-1 phases,
resulting in N-1 deformation vector fields (DVFs). These DVFs can be
represented efficiently by a few eigenvectors and coefficients obtained from
principal component analysis (PCA). By varying the PCA coefficients, we can
generate new DVFs, which, when applied on the reference image, lead to new
volumetric images. We then can reconstruct a volumetric image from a single
projection image by optimizing the PCA coefficients such that its computed
projection matches the measured one. The 3D location of the tumor can be
derived by applying the inverted DVF on its position in the reference image.
Our algorithm was implemented on graphics processing units (GPUs) to achieve
real-time efficiency. We generated the training data using a realistic and
dynamic mathematical phantom with 10 breathing phases. The testing data were
360 cone beam projections corresponding to one gantry rotation, simulated using
the same phantom with a 50% increase in breathing amplitude. Results: The
average relative image intensity error of the reconstructed volumetric images
is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 mm +/- 0.5 mm.
On an NVIDIA Tesla C1060 GPU card, the average computation time for
reconstructing a volumetric image from each projection is 0.24 seconds (range:
0.17 and 0.35 seconds). Conclusions: We have shown the feasibility of
reconstructing volumetric images and localizing tumor positions in 3D in near
real-time from a single x-ray image. | | Source: | arXiv, 1004.0014 | | Services: | Forum | Review | PDF | Favorites | |
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