Numerical evaluation of methods for computing tomographic projections

Zhuang, Wenqin; Gopal, Sucharita; Hebert, T.J.

doi:10.1109/23.322963

Cited by 58 publications

(41 citation statements)

References 8 publications

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“…When evaluating projectors for iterative reconstruction, Zhuang et al [20] demonstrated that forward projection of square pixels required separate projection of subpixels to avoid errors in the projection data.…”

Section: Effect Of Interpolation Methods On Reconstructionsmentioning

confidence: 99%

An optimal rotator for iterative reconstruction

Wallis

Miller

1997

IEEE Trans. Med. Imaging

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For implementations of iterative reconstruction algorithms that rotate the image matrix, the characteristics of the rotator may affect the reconstruction quality. Desirable qualities for the rotator include: 1) preservation of global and local image counts; 2) accurate count positioning; 3) a uniform and predictable amount of blurring due to the rotation. A new rotation method for iterative reconstruction is proposed which employs. Gaussian interpolation. This method was compared to standard rotation techniques and is shown to be superior to standard techniques when measured by these qualities. The computational cost was demonstrated to be only slightly more than bilinear interpolation.

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Section: Effect Of Interpolation Methods On Reconstructionsmentioning

confidence: 99%

An optimal rotator for iterative reconstruction

Wallis

Miller

1997

IEEE Trans. Med. Imaging

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“…Backprojection in the FDK has been done in a pixel-driven (or voxel-driven) way (13)(14)(15). A pixel-driven backprojector calculates each voxel value by backward-projecting the voxel center onto the detector plane and by interpolating the projection data there.…”

Section: Introductionmentioning

confidence: 99%

A Fully GPU-Based Ray-Driven Backprojector via a Ray-Culling Scheme with Voxel-Level Parallelization for Cone-Beam CT Reconstruction

Park

Shin

Lee

2014

Technol Cancer Res Treat

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A ray-driven backprojector is based on ray-tracing, which computes the length of the intersection between the ray paths and each voxel to be reconstructed. To reduce the computational burden caused by these exhaustive intersection tests, we propose a fully graphics processing unit (GPU)-based ray-driven backprojector in conjunction with a ray-culling scheme that enables straightforward parallelization without compromising the high computing performance of a GPU. The purpose of the ray-culling scheme is to reduce the number of ray-voxel intersection tests by excluding rays irrelevant to a specific voxel computation. This rejection step is based on an axis-aligned bounding box (AABB) enclosing a region of voxel projection, where eight vertices of each voxel are projected onto the detector plane. The range of the rectangular-shaped AABB is determined by min/max operations on the coordinates in the region. Using the indices of pixels inside the AABB, the rays passing through the voxel can be identified and the voxel is weighted as the length of intersection between the voxel and the ray. This procedure makes it possible to reflect voxel-level parallelization, allowing an independent calculation at each voxel, which is feasible for a GPU implementation. To eliminate redundant calculations during ray-culling, a shared-memory optimization is applied to exploit the GPU memory hierarchy. In experimental results using real measurement data with phantoms, the proposed GPU-based ray-culling scheme reconstructed a volume of resolution 28032803176 in 77 seconds from 680 projections of resolution 10243768 , which is 26 times and 7.5 times faster than standard CPU-based and GPU-based ray-driven backprojectors, respectively. Qualitative and quantitative analyses showed that the ray-driven backprojector provides high-quality reconstruction images when compared with those generated by the Feldkamp-Davis-Kress algorithm using a pixel-driven backprojector, with an average of 2.5 times higher contrast-to-noise ratio, 1.04 times higher universal quality index, and 1.39 times higher normalized mutual information.

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“…Many research studies have been reported on different techniques to implement these operations [1][2][3][4][5][6]. Among these studies, ray-driven line integral technique for the X-ray paths has shown advantages on noise suppression for fan-beam projection modality.…”

Section: Introductionmentioning

confidence: 99%

A comparison study on ray-driven approximation in re-projection and back-projection for CT reconstruction

Fan

et al. 2011

2011 IEEE Nuclear Science Symposium Conference Record

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Ray-driven technique is very important for computed tomography (CT) image reconstruction. It has been reported that the weighting factor in ray-driven technique can be obtained by calculating the intersection length (distance-weighting) or area (area-weighting) of a projection line or strip with an image pixel.Recently, we presented a method which considers the intersection volume as weighting factor (volume-weighting) in fully three dimensional (3D) imaging. In this work, we presented the performances of these three different weighting techniques by computer simulation studies. We first generated a 3D continuous Shepp-Logan phantom and computed the sinogram data analytically as the reference. Then the sinogram data with the three different weighting techniques were simulated. It can be observed that the profile obtained by the analytical solution is smooth while the three weighting methods show noticeable unevenness. The unevenness is inevitable due to the induced error by the digitalization process and finite image resolution.While the volume weighting achieved slightly better performance than the area weighting and both of them showed noticeable improvements compared to the distance weighting.We also compared their reconstruction performances using filtered backprojection (FBP) and Feldkamp (FDK) algorithms for 2D and 3D cases, respectively. The gain by the use of volume weighting was measured, as compared to others, by the standard variation in selected regions of interest in the reconstructions.One drawback about the volume weighting is the need of intensive computation. It costs about ten times more computation as compared to the distance weighting and about three times more computation to the area weighting. Though this should not be a major concern for sinogram restoration and analytical reconstruction, it might limit the use when using iterative algorithms. Optimization of the volume weighting for fast computation and comparison of the ray-driven technique with other methods are under investigation.

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Numerical evaluation of methods for computing tomographic projections

Cited by 58 publications

References 8 publications

An optimal rotator for iterative reconstruction

An optimal rotator for iterative reconstruction

A Fully GPU-Based Ray-Driven Backprojector via a Ray-Culling Scheme with Voxel-Level Parallelization for Cone-Beam CT Reconstruction

A comparison study on ray-driven approximation in re-projection and back-projection for CT reconstruction

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