&lt;title&gt;Exact local regions-of-interest reconstruction in spiral cone-beam filtered-backprojection CT: theory&lt;/title&gt;

Tam, K. C.

doi:10.1117/12.387711

Cited by 30 publications

(25 citation statements)

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“…The method can be formulated as an FBP-based algorithm [3], [4]. For the first and last turn of the spiral scan, a combination of half-and full-line integrals are required in step 2) as well as an additional correction term which is fast and easy to compute.…”

Section: B Local Roi Methodsmentioning

confidence: 99%

“…In analogy to the rampfilter design in conventional CT, a convolution kernel in spatial domain is deduced by a modification of the analytical expression of the derivative with a window function in Fourier space. Thus, the derivative of any function can be computed as (3) with the convolution kernel as a Fourier inverse transform (4) The digital filter kernel must be calculated by analytically evaluating the integral in (4) and by discretizing the resulting function . The alternative approach of using a discrete Fourier transform to evaluate (4) would cause aliasing.…”

Section: Kernel Designmentioning

confidence: 99%

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Optimization of derivative kernels for exact cone-beam ROI reconstruction in spiral computed tomography

Lauritsch

Sourbelle²,

Tam³

2002

IEEE Trans. Nucl. Sci.

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A recent comparison study showed that exact filtered backprojection (FBP)-based solutions of the long object problem are numerically stable and that their image quality is mostly affected by filter design. In this paper, the impact of the kernel design on image quality is demonstrated systematically in the case of the local region-of-interest (ROI) algorithm. First of all, the implementation is improved by the use of the Linogram technique to eliminate interpolations in the filter step and to speed up computation. In the case of the local ROI method, favorable kernels for one-dimensional (1-D) partial derivatives have to be designed. Finite differences are fast to compute but cause ringing artifacts. More sophisticated kernels can be designed in a straightforward manner in Fourier space, identifying the window function most appropriate to a given application. No ringing artifacts are observed for the windowed kernels under investigation. In addition, windowed kernels are superior to the finite differences regarding the spatial resolution versus noise properties. For partial derivatives in directions with data truncation the projection data have to be extrapolated before convolution. Alternatively, very short derivative kernels can be used, generally at a decreased image quality. Kernels of different design and length can be combined.Index Terms-Cone-beam computed tomography (CT), Cone-beam filtered backprojection (FBP) algorithms, filter design, finite differences, linogram, local region-of-interest (ROI) algorithm, numerical derivative, ringing artifacts, spiral CT, three-dimensional (3-D) radon inversion, windowed filter kernels.

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Section: B Local Roi Methodsmentioning

confidence: 99%

Section: Kernel Designmentioning

confidence: 99%

Optimization of derivative kernels for exact cone-beam ROI reconstruction in spiral computed tomography

Lauritsch

Sourbelle²,

Tam³

2002

IEEE Trans. Nucl. Sci.

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“…The Katsevich proved that for a given X, the t-plane is uniquely determined if the projection of i' (called iz) onto the detector plane lies in the Tam-Danielsson window [6]. Let the t-line be the line of intersection of the detector and a r-plane.…”

Section: Notationsmentioning

confidence: 99%

An Improved Exact FBP Algorithm for Image Reconstruction in Cone-beam Helical CT

Chen

2006

2006 International Conference on Computational Intelligence and Security

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Recently, Katsevich proposed an exact FBP algorithm and its improved versionfor image reconstruction in helical cone-beam CT In this paper, we present a new FBP image reconstruction algorithm by modifying Katsevich's original algorithm. This new algorithm is easier to implement and more efficient than the improved version of Katsevich's algorithm. Numerical simulations are performed to validate the proposed exact FBP algorithm. Results in these studies confirm the observation that the proposed algorithm can improve the image resolution over Katsevich's original algorithm with noiseless and noise projection data.

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“…The concept of PI lines plays an important role in image reconstruction from helical cone-beam data, [10][11][12] and it has been generalized to image reconstruction from fan-beam data. 5 In fan-beam scan, a PI-line segment is a straight line segment joining two points labeled by the scanning angles 1 and 2 on the source trajectory, as shown in Fig.…”

Section: A Pi-line Segments In a Fan-beam Scanmentioning

confidence: 99%

Image reconstruction in peripheral and central regions‐of‐interest and data redundancy

2005

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Algorithms have been developed for image reconstruction within a region-of-interest (ROI) from fan-beam data less than that required for reconstructing the entire image. However, these algorithms do not admit truncated data. In this work, we investigate exact ROI-image reconstruction from fan-beam data containing truncations by use of the so-called fan-beam backprojection-filtration (BPF) algorithm. We also generalize the fan-beam BPF algorithm to exploit redundant information inherent in the truncated fan-beam data. Because the parallel-beam scan can be interpreted as a special case of the fan-beam scan, based upon the fan-beam BPF algorithm, we derive a parallel-beam BPF algorithm for exactly reconstructing ROI images from truncated parallel-beam data. Furthermore, we investigate image reconstruction within two types of distinctive ROIs, which are referred to as the peripheral and central ROIs, respectively, from fan-beam data containing truncations and discuss their potential clinical applications. The results can readily be generalized to reconstructing 3D ROI images from data acquired in circular and helical cone-beam scan. They can also be extended to address ROI-image-reconstruction problems in parallel-, fan-, and cone-beam scans with general trajectories. The work not only has significant implications for clinical and animal-imaging applications of CT, but also may find applications in other imaging modalities.

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<title>Exact local regions-of-interest reconstruction in spiral cone-beam filtered-backprojection CT: theory</title>

Cited by 30 publications

References 0 publications

Optimization of derivative kernels for exact cone-beam ROI reconstruction in spiral computed tomography

Optimization of derivative kernels for exact cone-beam ROI reconstruction in spiral computed tomography

An Improved Exact FBP Algorithm for Image Reconstruction in Cone-beam Helical CT

Image reconstruction in peripheral and central regions‐of‐interest and data redundancy

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