Yuxiang Xing

Channelized hotelling and human observer study of optimal smoothing in SPECT MAP reconstruction

We compared the performance of a channelized Hotelling observer (CHO) to that of human observers to determine an optimal smoothing parameter /spl beta/ for an SKE/BKE detection task in a SPECT MAP (maximum a posteriori) reconstruction. The study is motivated in part by the recent development of theoretical methods that can rapidly predict CHO signal-to-noise ratios (SNRs) for MAP reconstructions. We found that a CHO not adjusted for internal noise effects was less predictive of the optimal smoothing parameters than one that used human observer data to tune the CHO for internal noise. We used a three-channel, square profile, radially symmetric channel structure, and, for internal noise, a method that altered the diagonal elements of the channel covariance matrix. The human observer study for two different signals A and B showed that /spl beta/ in the range 0.5-10.0 produced high detectability as measured by high d/sub A//sup 2/, while the CHO without internal noise showed high SNR/sup 2/ for /spl beta/ in the wider range 0.01-10.0. The CHO at location A was modified by internal noise utilizing human data at A, so that the d/sub A//sup 2/ and SNR/sup 2/ overlapped well, but when these internal noise parameters from A were applied at B, the curves did not overlap well. Nevertheless, both modified CHOs predicted a /spl beta/ range in accord with human data. We conclude that CHOs may need some way of incorporating internal noise without having to conduct a human study in the first place to determine internal noise parameters.

Metal artifact reduction in CT images by sinogram TV inpainting

In this paper, a total variation (TV) inpainting method is proposed for metal artifact reduction in medical computed tomography (CT). Digital inpainting is an image processing method to fill in the lost image information in a consistent way. In our work, projection data with metal projection region (MPR) are treated as a damaged image, and TV inpainting is applied to “inpaint” the information missing region. Compared to conventional interpolation methods, the advantage of our algorithm lies in dealing with complicate cases such as an image with multiple metal objects. In numerical experiments, both TV inpainting and linear interpolation method are performed on noise-free and additive noisy projection of a modified Shepp-Logan phantom. Results show that the algorithm proposed fills metal projection gaps more smoothly and accurately than linear interpolation, hence produces images of superior quality after reconstruction. Relevant practical issues including the limitation of the algorithm and possible improvements for future work are discussed.

Few-View Projection Reconstruction With an Iterative Reconstruction-Reprojection Algorithm and TV Constraint

In applications of tomographic imaging, insufficient data problems can take various forms, such as few-view projection imaging which enables rapid scanning with lower X-ray dose. In this work, an iterative reconstruction-reprojection (IRR) algorithm with total variation (TV) constraint is developed for few-view projections. The IRR algorithm is used to estimate the missing projection data by iterative extrapolation between projection and image space. TV minimization is a popular image restoration method with edge preserving. In recent studies, it has been successfully used for reconstructing images from sparse samplings, such as few-view projections. Our method is derived from this work. The combination of IRR and TV achieves both estimation in projection space and regularization in image space, which accelerates the convergence of the iterations. To improve the quality of the image reconstructed from few-view fan-beam projections, a short-scan type IRR is also approached to reduce the redundancy of projection data. An improved weighting function is proposed for few-view short-scan projection reconstruction by the filtered backprojection (FBP) algorithm. Numerical simulations show that the IRR-TV algorithm is effective for the few-view problem of reconstructing sparse-gradient images.

A general region-of-interest image reconstruction approach with truncated Hilbert transform

This paper presents a novel data sufficiency condition that unique and stable ROI reconstruction can be achieved from a more flexible family of data sets. To the interior problem, it allows the ROI (Region-of-interest) can be reconstructed from the line integrals passing through this ROI and a small region B located anywhere as long as the image is known on B. Especially, ROI reconstruction can be achieved without any other a priori knowledge when region B is placed outside the object support. We also develop a general reconstruction algorithm with the DBP-POCS (Differentiated backprojection-projection onto convex sets) method. Finally, both numerical and real experiments were done to illustrate the new data sufficiency condition and the good stability of the ROI reconstruction algorithm.

Rapid calculation of detectability in Bayesian single photon emission computed tomography

We consider the calculation of lesion detectability using a mathematical model observer, the channelized Hotelling observer (CHO), in a signal-known-exactly/background-known-exactly detection task for single photon emission computed tomography (SPECT). We focus on SPECT images reconstructed with Bayesian maximum a posteriori methods. While model observers are designed to replace time-consuming studies using human observers, the calculation of CHO detectability is usually accomplished using a large number of sample images, which is still time consuming. We develop theoretical expressions for a measure of detectability, the signal-to-noise-ratio (SNR) of a CHO observer, that can be very rapidly evaluated. Key to our expressions are approximations to the reconstructed image covariance. In these approximations, we use methods developed in the PET literature, but modify them to reflect the different nature of attenuation and distance-dependent blur in SPECT. We validate our expressions with Monte Carlo methods. We show that reasonably accurate estimates of the SNR can be obtained at a computational expense equivalent to approximately two projection operations, and that evaluating SNR for subsequent lesion locations requires negligible additional computation.

Adaptive noise reduction toward low-dose computed tomography

An efficient noise treatment scheme has been developed to achieve low-dose CT diagnosis based on currently available CT hardware and image reconstruction technologies. The scheme proposed includes two main parts: filtering in sinogram domain and smoothing in image domain. The acquired projection sinograms were first treated by our previously proposed Karhunen-Loeve (K-L) domain penalized weighted least-square (PWLS) filtering, which fully utilizes the prior statistical noise property and three-dimensional (3D) spatial information for an accurate restoration of the low-dose projections. To treat the streak artifacts due to photon starvation, we also incorporated an adaptive filtering into our PWLS framework, which selectively smoothes those channels contributing most to the streak artifacts. After the sinogram filtering, the image was reconstructed by the conventional filtered backprojection (FBP) method. The image is assumed as piecewise regions each has a unique texture. Therefore, an edge-preserving smoothing (EPS) with locally-adaptive parameters to the noise variation was applied for further noise reduction in image domain. Experimental phantom projections acquired by a GE spiral computed tomography (CT) scanner under 10 mAs tube current were used to evaluate the proposed smoothing scheme. The reconstructed imaged demonstrated that the smoothing scheme with appropriate control parameters provides a significant improvement on noise suppression without sacrificing the spatial resolution.

Metal artifact reduction in dual energy CT by sinogram segmentation based on active contour model and TV inpainting

In dual energy computerized tomography (DECT) which is widely used in industrial areas and security inspection, metal artifact reduction (MAR) is a troublesome problem. Pronounced streaks appear in the atomic number reconstruction and the value appears to be highly inaccurate when metal objects are present. In this article, a practical MAR method for DECT is proposed. Firstly, sinogram segmentation based on active contour model is implemented to obtain the metal projection region (MPR). Then, TV inpainting for sinogram was applied before reconstruction. Experiments demonstrate that, with our MAR method, the accuracy and image quality of the atomic number can be greatly improved.

Beam Hardening Correction for Middle-Energy Industrial Computerized Tomography

In this paper, a new beam hardening correction (BHC) method for middle-energy industrial computerized tomography (CT) is presented. Our method is derived from linearization and is straightforward without iteration involved. The linearization is commonly used as a preprocessing method in BHC. Conventionally, only one-material objects can be conveniently corrected by linearization. In industrial CT, two-material objects, especially cylinders with high-Z material outside and low-Z material inside are frequently encountered. Our approach focuses on this kind of objects. The new method works well as long as the two-material object meets the conditions that the thickness of the outer material (usually wall) is thick enough and the second-order item of the Taylor expansion of the linearization is relatively small. We pointed out and proved that there is an approximately constant scaling factor difference between our linearization step and an ideal correction based on prior knowledge of objects. The scaling factor magnifies the attenuation coefficient of the inner material after reconstruction. Therefore, a weighting function is introduced into our algorithm as a restoration. To sum up, there are three steps in our method: 1) correct raw projections by the mapping function of the outer material; 2) reconstruct the cross-section image from the modified projections; 3) scale the image by a weighting function. With this method, the beam hardening artifacts are greatly reduced and the overall attenuation coefficients are accurately obtained. We also presented a compensation step to remove the countercupping artifacts in case that the conditions are not fully met. Our method is well verified in both numerical simulations and practical experiments on a 450-KeV CT system

Direct filtered-backprojection-type reconstruction from a straight-line trajectory

A computed tomography (CT) imaging configuration with a straight-line trajectory is investigated, and a direct filtered backprojection (FBP) algorithm is presented. This kind of system may be useful for industrial applications and security inspections. Projections from a straight-line trajectory have a special property where data from each detector element correspond to a parallel-beam projection of a certain view angle. However, the sampling steps of parallel beams differ from view to view. Rebinning raw projections into uniformly sampled parallel-beam projections is a common choice for this type of reconstruction problem. However, the rebinning procedure suffers a loss of spatial resolution because of interpolations. Our reconstruction method is first derived from the Fourier slice theorem, where a coordinate transform and geometrical relations in projection and backprojection are used. It is then extended to 3-D scanning geometry. Finally, data-shift preprocessing is proposed to reduce computation and memory requirements by removing useless projections in raw data. In this method, the spatial resolution is better preserved and the reconstruction is less sensitive to data truncation than in the rebinning-to-parallel-beam method. To deal with limited angle problem, an iterative reconstruction reprojection method is introduced to estimate missing data and improve the image quality.

A curve-filtered FDK (C-FDK) reconstruction algorithm for circular cone-beam CT

Circular cone-beam CT is one of the most popular configurations in both medical and industrial applications. The FDK algorithm is the most popular method for circular cone-beam CT. However, with increasing cone-angle the cone-beam artifacts associated with the FDK algorithm deteriorate because the circular trajectory does not satisfy the data sufficiency condition. Along with an experimental evaluation and verification, this paper proposed a curve-filtered FDK (C-FDK) algorithm. First, cone-parallel projections are rebinned from the native cone-beam geometry in two separate directions. C-FDK rebins and filters projections along different curves from T-FDK in the centrally virtual detector plane. Then, numerical experiments are done to validate the effectiveness of the proposed algorithm by comparing with both FDK and T-FDK reconstruction. Without any other extra trajectories supplemental to the circular orbit, C-FDK has a visible image quality improvement.