Biomedical subjects

Zikuan Chen

Publications and source records attributed to Zikuan Chen.

9 recordsLinked to original sources

PubMed2006

Volume fusion for two-circular-orbit cone-beam tomography.

By using the Feldkamp-Davis-Kress (FDK) algorithm, we can efficiently produce a digital volume, called the FDK volume, from cone-beam data acquired along a circular scan orbit. Due to the insufficiency of the cone-beam data set, the FDK volume suffers from nonuniform reproduction exactness. Specifically, the midplane (on the scan-orbit plane) can be exactly reproduced, and the reproduction exactness of off-midplanes decreases as the distance from the midplane increases. We describe the longitudinal falling-off degradation by a hatlike function and the spatial distribution over the object domain by an exactness volume. With two orthogonal circular scan orbits, we can reconstruct two FDK volumes and generate two exactness volumes. We propose a volume fusion scheme to combine the two FDK volumes into a single volume. Let Va and Vb denote the two FDK volumes, let Ea and Eb denote the exactness volumes for orbits Gamma(a) and Gamma(b), respectively, then the volume fusion is defined by Vab=VaWa+VbWb, with Wa=Ea/(Ea+Eb) and Wb=1-Wa. In the result, the overall reproduction exactness of Vab is expected to outperform that of Va, or Vb, or (Va+Vb)/2. In principle, this volume-fusion scheme is applicable for general cone-beam tomography with multiple nonorthogonal and noncircular orbits.

Algorithms↗

PubMed2005

Supergridded cone-beam reconstruction and its application to point-spread function calculation.

In cone-beam computed tomography (CBCT), the volumetric reconstruction may in principle assume an arbitrarily fine grid. The supergridded cone-beam reconstruction refers to reconstructing the object domain or a subvolume thereof with a grid that is finer than the proper computed tomography sampling grid (as determined by gantry geometry and detector discreteness). This technique can naturally reduce the voxelization effect, thereby retaining more details for object reproduction. The grid refinement is usually limited to two or three refinement levels because the detail pursuit is eventually limited by the detector discreteness. The volume reconstruction is usually targeted to a local volume of interest due to the cubic growth in a three-dimensional (3D) array size. As an application, we used this technique for 3D point-spread function (PSF) measurement of a CBCT system by reconstructing edge spread profiles in a refined grid. Through an experiment with a Teflon ball on a CBCT system, we demonstrated the supergridded volume reconstruction (based on a Feldcamp algorithm) and the CBCT PSF measurement (based on an edge-blurring technique). In comparison with a postreconstruction image refinement technique (upsampling and interpolation), the supergridded reconstruction could produce better PSFs (in terms of a smaller FWHM and PSF fitting error).

Algorithms↗

PubMed2005

Forest representation of vessels in cone-beam computed tomographic angiography.

Cone-beam computed tomographic angiography (CBCTA) provides a fast three-dimensional (3D) vascular imaging modality, aiming at digitally representing the spatial vascular structure in an angiographic volume. Due to the finite coverage of cone-beam scan, as well as the volume cropping in volumetric image processing, an angiographic volume may fail to contain a whole vascular tree, but rather consist of a multitude of vessel segments or subtrees. As such, it is convenient to represent multitudinal components by a forest. The vessel tracking issue then becomes component characterization/identification in the forest. The forest representation brings several conveniences for vessel tracking: (1) to sort and count the vessels in an angiographic volume, for example, according to spatial occupancy and skeleton pathlength; (2) to single out a vessel and perform in situ 3D measurement and 3D visualization in the support space; (3) to delineate individual vessels from the original angiographic volume; and (4) to cull the forest by getting rid of non-vessels and small vessels. A 3D skeletonization is used to generate component skeletons. For tree construction from skeletons, we suggest a pathlength-based procedure, which lifts the restrictions of unit-width skeleton and root determination. We experimentally demonstrate the forest representation of a dog's carotid arteries in a CBCTA system. In principle, the forest representation is useful for managing vessels in both 2D angiographic images and 3D angiographic volumes.

Angiography↗

PubMed2004

Three-dimensional point spread function measurement of cone-beam computed tomography system by iterative edge-blurring algorithm.

With separability assumed, we decompose a three-dimensional point spread function (3D PSF) into two-dimensional (2D) PSFs and further into one-dimensional (ID) PSFs. Based on the observation of the location invariance of a step edge under convolution, we propose a rectification procedure to automatically establish the step-edge function from a blurred edge profile. The ID PSF is modelled as a single-parameter Gaussian function, which is determined by iteratively blurring a step-edge function into a spread edge profile. A plastic solid ball (diameter approximately 6 mm) is used to provide double-edged rectangular functions along scanlines passing through the ball centre, and correspondingly, the reconstructed digital volume provides the blurred rectangular profiles. Experimenting with a cone-beam computed tomography system, we demonstrate the iterative edge-blurring algorithm for PSF measurement. By repositioning the ball phantom in the object support space, we measure the system's spatial variance in terms of full-width-at-half-maximum (FWHM) of the local PSFs. Specifically, we obtained the FWHMs for three specific locations at (0, 0, -40 mm), (0, 0, 0) and (0, 0, 40 mm), which are given by 0.92 +/- 0.10 mm, 0.65 +/- 0.08 mm and 0.93 +/- 0.10 mm, respectively.

Algorithms↗

PubMed2004

Breast volume denoising and noise characterization by 3D wavelet transform.

Breast imaging through cone-beam computed tomography provides a digital breast volume, with which the three-dimensional (3D) breast tissues can be analyzed. Data denoising, as a preprocessing step for subsequent volumetric breast segmentation is always needed. In this paper, we report a volumetric denoising technique by a separable 3D wavelet transform (WT), i.e. a '2D WT plus 1D WT' scheme. Specifically, the scheme performs two-dimensional (2D) wavelet denoising on a stack of slice images of the breast volume, followed by one-dimensional (1D) wavelet denoising along the stacking direction. The denoising is achieved by wavelet decomposition, high-pass subband attenuation, and wavelet synthesis. A one-level 3D WT produces eight subbands occupying the octants of the 3D wavelet space. Multilevel WT also provides a multiresolution representation of breast volume, i.e. a sequence of low-pass subbands. In general, most noise and irregularity features are imparted into the high-pass subbands, which are removed or reduced for denoising purpose. Meanwhile, the information in a subband can be characterized in terms of energy, variance, and entropy. Through 3D visualization, the spatial structure in a subband can also be visually perceived. Experimental demonstration with the breast volume reconstructed from a specimen is provided.

Mammography↗

PubMed2003

Filling the Radon domain in computed tomography by local convex combination.

Radon data interpolation is a necessary procedure in computed tomography (CT), especially for reconstruction from divergent beam scanning. In a polar-grid representation, the Radon data of a fanbeam projection are populated on an arc, rather on a radial line. Collectively, the Radon data generated from a fanbeam CT system are unevenly populated: The population becomes sparser as the polar distance increases. In CT reconstruction, the Fourier central slice theorem requires a radial scanline full of Radon data. Therefore the vacant entries of a scanline must be filled by interpolation. In addition, interpolation is also required in polar-to-Cartesian conversion. In this paper we propose a practical interpolation technique for filling the vacant entries by local convex combination. It is a linear interpolant that generates a value for a grid point from the available data lying in its neighborhood, by a weighted average, with the weights corresponding to the inverse distances. In fact, the linear convex combination serves as a general flat-smoothing operation in filling a vacancy. Specifically, this technique realizes a variety of linear interpolations, including nearest-neighbor replication, two-point collinear, three-point triangulation, and four-point quadrilateral, and local extrapolation, in a unified framework. Algorithms and a simulation demonstration are provided.

Algorithms↗

PubMed2003

Why should breast tumour detection go three dimensional?

Although x-ray mammography is widely developed for breast tumour detection, it suffers from spatial superposition in its two-dimensional (2D) representation of a three-dimensional (3D) breast structure. Accordingly, 3D breast imaging, such as cone-beam computed tomography (CT), arises at the historic moment. In this paper, we theoretically elucidate the spatial superposition effect associated with x-ray mammography on breast tumour detection. This explanation is based on the line integral of x-ray traversing a composite breast model. As a result, we can characterize the difficulty of detecting small tumours in terms of local intensity contrast in x-ray images. In comparison, we also introduce cone-beam CT breast imaging for 3D breast volume representation, which offers advantages for breast mass segmentation and measurement. The discussion is demonstrated with an experiment with a breast surgical specimen. In conclusion, we strongly believe that 3D volumetric representation allows for more accurate breast tumour detection.

Algorithms↗

PubMedDate not supplied

Vascular tree object segmentation by deskeletonization of valley courses.

In this paper, we propose a valley-course-based image segmentation technique for tree-like object delineation, as an alternative to the traditional centerline-based methods. This technique consists of valley-course extraction, skeleton pruning and deskeletonization. Valley courses, constructed from valley points that are obtained by star-pattern scanning over an image, offer a natural manner of identifying tree skeletons. Unattached segments are removed using morphological operations. A structured tree is then constructed from the skeletons by using a tree pruning/spanning algorithm. A fleshy tree-like object is obtained by a deskeletonization procedure, which consists of extracting tree boundary in vicinity of the skeletons in the original image. The tree boundary is determined by identifying paired edge points at a valley point. A derivative-free edge identification approach is proposed, which defines an edge point at a side-slope by a relative intensity drop with respect to the local background. An empirical formula using a logarithmic function of local intensity contrast offers desirable characteristics of adaptability and stability. The adaptability of edge points to the local background is attributed to the compression behavior of logarithmic function. Furthermore, stability to noise is resulted because derivative operations are not used. The segmentation technique was validated using coronary angiographic images.

Algorithms↗

PubMedDate not supplied

Automatic 3D vascular tree construction in CT angiography.

This study presents an automatic method for 3D reconstruction of vascular trees using computed-tomography angiographic (CTA) images. The program starts with the CTA slices, performs a sequential procedure of 3D image formation, preprocessing, segmentation, thinning, skeleton pruning and tree construction. It ends with vascular trees along with quantitative data about the trees such as values of diameter, length and bifurcation angles. All the involved algorithms are presented with the emphasis given to the skeleton pruning and tree construction algorithms. The skeletons obtained using a 3D thinning algorithm may contain cycles, spurs, isolated sticks, and non-unit-width parts, which hinder tree construction. As a solution to this problem, a skeleton pruning and tree construction algorithm is proposed. At each stage of the automatic procedure, 3D rendering is provided for visual inspection of the computed results. In the final output, the constructed vascular trees are visualized by rendering the 3D trees and the 3D binary image together in a transparent display mode. The program is carried out in a fully automatic fashion, with a few default settings. Occasionally, user intervention is needed at the 3D segmentation stage to impose an appropriate threshold when the automatic 3D segmentation is obviously sub-optimal for vessel delineation. Experimental demonstrations on both coronary artery phantom and a cast of coronary artery tree of a swine animal model are provided.

Angiography↗