The purpose of this paper is to design and implement an efficient iterative reconstruction algorithm for computational
tomography. We accelerate the reconstruction speed of algebraic reconstruction technique (ART), an
iterative reconstruction method, by using the result of filtered backprojection (FBP), a wide used algorithm of
analytical reconstruction, to be an initial guess and the reference for the first iteration and each back projection
stage respectively. Both two improvements can reduce the error between the forward projection of each iteration
and the measurements. We use three methods of quantitative analysis, root-mean-square error (RMSE), peak
signal to noise ratio (PSNR), and structural content (SC), to show that our method can reduce the number of
iterations by more than half and the quality of the result is better than the original ART.
This paper presents a method of X-ray image acquisition for the high-resolution tomography reconstruction
that uses a light source of synchrotron radiation to reconstruct a three-dimensional tomographic volume dataset
for a nanoscale object. For large objects, because of the limited field-of-view, a projection image of an object
should to be taken by several shots from different locations, and using an image stitching method to combine
these image blocks together. In this study, the overlap of image blocks should be small because our light source
is the synchrotron radiation and the X-ray dosage should be minimized as possible. We use the properties of
synchrotron radiation to enable the image stitching and alignment success when the overlaps between adjacent
image blocks are small. In this study, the size of overlaps can reach to 15% of the size of each image block. During
the reconstruction, the mechanical stability should be considered because it leads the misalignment problem in
tomography. We adopt the feature-based alignment
Fourier volume rendering (FVR) is a volume rendering method based on the Fourier slice theorem. With an n × n × n volume data, the FVR algorithm requires O(n2 log n) time to generate a result. Because it requires time less than O(n3) does, FVR is preferred for designing a real-time rendering algorithm with a preprocessing step. We improve upon our previous work. We demonstrate that a B-spline is significantly more useful when designing a transfer function. To design an appropriate transfer function with a spline function, additional control points are required. However, the memory space required for the proposed method increases in linear proportion to the number of control points. We show that the set of control points can be clustered into groups, ensuring the memory required is linearly proportional to the number of groups. The proposed technique supports real-time rendering after adjusting the transfer function for FVR.
Volume rendering is a technique for volume visualization. Given a set of N × N × N volume data, the traditional volume
rendering methods generally need O(N3) rendering time. The FVR (Fourier Volume Rendering), that takes advantage
of the Fourier slice theorem, takes O(N2log N) rendering time once the Fourier Transform of the volume data is
available. Thus the FVR is favor to designing a real-time rendering algorithm with a preprocessing step. But the FVR has
a disadvantage that resampling in the frequency domain causes artifacts in the spatial domain. Another problem is that
the method for designing a transfer function is not obvious. In this paper, we report that by using the spatial domain zero-padding
and tri-linear filtering can reduce the artifacts to an acceptable rendered image quality in spatial domain. To
design the transfer function, we present a method that the user can define a transfer function by using a Bezier curve
first. Based on the linear combination property of the Fourier transform and Bezier curve equation, the volume rendered
result can be obtained by adding the weighted frequency domain signals. That mean, once a transfer function is given,
we don't have to recompute the Fourier transform of the volume data after the transfer function applied. This technique
makes real-time adjustment of transfer function possible.
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