KEYWORDS: Signal to noise ratio, X-ray computed tomography, Data modeling, X-rays, Sensors, Smoothing, Calibration, Linear filtering, Image filtering, Scanners
To treat the noise in low-dose x-ray CT projection data more accurately, analysis of the noise properties of the data and development of a corresponding efficient noise treatment method are two major problems to be addressed. In order to obtain an accurate and realistic model to describe the x-ray CT system, we acquired thousands of repeated measurements on different phantoms at several fixed scan angles by a GE high-speed multi-slice spiral CT scanner. The collected data were calibrated and log-transformed by the sophisticated system software, which converts the detected photon energy into sinogram data that satisfies the Radon transform. From the analysis of these experimental data, a nonlinear relation between mean and variance for each datum of the sinogram was obtained. In this paper, we integrated this nonlinear relation into a penalized likelihood statistical framework for a SNR (signal-to-noise ratio) adaptive smoothing of noise in the sinogram. After the proposed preprocessing, the sinograms were reconstructed with unapodized FBP (filtered backprojection) method. The resulted images were evaluated quantitatively, in terms of noise uniformity and noise-resolution tradeoff, with comparison to other noise smoothing methods such as Hanning filter and Butterworth filter at different cutoff frequencies. Significant improvement on noise and resolution tradeoff and noise property was demonstrated.
Colon cancer is the second leading cause of cancer-related death in the United States. Earlier detection and removal of polyps can dramatically reduce the chance of developing malignant tumor. Due to some limitations of optical colonoscopy used in clinic, many researchers have developed virtual colonoscopy as an alternative technique, in which accurate colon segmentation is crucial. However, partial volume effect and existence of residue make it very challenging. The electronic colon cleaning technique proposed by Chen et al is a very attractive method, which is also kind of hard segmentation method. As mentioned in their paper, some artifacts were produced, which might affect the accurate colon reconstruction. In our paper, instead of labeling each voxel with a unique label or tissue type, the percentage of different tissues within each voxel, which we call a mixture, was considered in establishing a maximum a posterior probability (MAP) image-segmentation framework. A Markov random field (MRF) model was developed to reflect the spatial information for the tissue mixtures. The spatial information based on hard segmentation was used to determine which tissue types are in the specific voxel. Parameters of each tissue class were estimated by the expectation-maximization (EM) algorithm during the MAP tissue-mixture segmentation. Real CT experimental results demonstrated that the partial volume effects between four tissue types have been precisely detected. Meanwhile, the residue has been electronically removed and very smooth and clean interface along the colon wall has been obtained.
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.
In this work, we have developed a new method for SPECT (single photon emission computed tomography) image reconstruction, which has shown the potential to provide higher resolution results than any other conventional methods using the same projection data. Unlike the conventional FBP- (filtered backprojection) and EM- (expectation maximization) type algorithms, we utilize as much system response information as we can during the reconstruction process. This information can be pre-measured during the calibration process and stored in the computer. By selecting different sampling schemes for the point response measurement, different system kernel matrices are obtained. Reconstruction utilizing these kernels generates a set of reconstructed images of the same source. Based on these reconstructed images and their corresponding sampling schemes, we are able to achieve a high resolution final image that best represents the object. Because a uniform attenuation, resolution variation and some other effects are included during the formation of the system kernel matrices, the reconstruction from the acquired projection data also compensates for all these effects correctly.
Segmentation of magnetic resonance (MR) images plays an important role in quantitative analysis of brain tissue morphology and pathology. However, the inherent effect of image-intensity inhomogeneity renders a challenging problem and must be considered in any segmentation method. For example, the adaptive fuzzy c-mean (AFCM) image segmentation algorithm proposed by Pham and Prince can provide very good results in the presence of the inhomogeneity effect under the condition of low noise levels. Their results deteriorate quickly as the noise level goes up. In this paper, we present a new fuzzy segmentation algorithm to improve the noise performance of the AFCM algorithm. It achieves accurate segmentation in the presence of inhomogeneity effect and high noise levels by incorporating the spatial neighborhood information into the objective function. This new algorithm was tested by both simulated experimental and real clinical MR images. The results demonstrated the improved performance of this new algorithm over the AFCM in the clinical environment where the inhomogeneity and noise levels are commonly encountered.
In this paper, we propose a new computer aided detection (CAD) technique to utilize both global and local shape information of the colon wall for detection of colonic polyps. Firstly, the whole colon wall is extracted by our mixture-based image segmentation method. This method uses partial volume percentages to represent the distribution of different materials in each voxel, so it provides the most accurate information on the colon wall, especially the mucosa layer. Local geometrical measure of the colon mucosa layer is defined by the curvature and gradient information extracted from the segmented colon-wall mixture data. Global shape information is provided by applying an improved linear integral convolution operation to the mixture data. The CAD technique was tested on twenty patient datasets. The local geometrical measure extracted from the mixture segmentation represents more accurately the polyp variation than that extracted from conventional label classification, leading to improved detection. The added global shape information further improves the polyp detection.
Based on Kunyansky's and our previous work, an efficient, analytical solution to the reconstruction problem of myocardial perfusion SPECT has been developed that allows simultaneous compensation for non-uniform attenuation, scatter, and system-dependent resolution variation, as well as suppression of signal-dependent Poisson noise. To avoid reconstructed images being corrupted by the presence of Poisson noise, a Karhunen-Loeve (K-L) domain adaptive Wiener filter is applied first to suppress the noise in the primary- and scatter-window measurements. The scatter contribution to the primary-energy-window measurements is then removed by our scatter estimation method, which is energy spectrum based, modified from the triple-energy-window acquisition protocol. The resolution variation is corrected by the depth-dependent deconvolution, which, being based on the central-ray approximation and the distance-frequency relation, deconvolves the scatter-free data with the accurate detector-response kernel in frequency domain. Finally, the deblurred projection data are analytically reconstructed with non-uniform attenuation by an algorithm based on Novikov's explicit inversion formula. The preliminary Monte Carlo simulation results using a realistic human thoracic phantom demonstrate that, for parallel-beam geometry, the proposed analytical reconstruction scheme is computationally comparable to filtered backprojection and quantitatively equivalent to iterative maximum a posteriori expectation-maximization reconstruction. Extension to other geometries is under progress.
KEYWORDS: Image segmentation, 3D image processing, 3D modeling, Ultrasonography, 3D displays, Image processing algorithms and systems, Computer simulations, Visualization, Surgery, Data modeling
Stenosis of the carotid is the most common cause of the stroke. The accurate measurement of the volume of the carotid and visualization of its shape are helpful in improving diagnosis and minimizing the variability of assessment of the carotid disease. Due to the complex anatomic structure of the carotid, it is mandatory to define the initial contours in every slice, which is very difficult and usually requires tedious manual operations. The purpose of this paper is to propose an automatic segmentation method, which automatically provides the contour of the carotid from the 3-D ultrasound image and requires minimum user interaction. In this paper, we developed the Geometrically Deformable Model (GDM) with automatic merge function. In our algorithm, only two initial contours in the topmost slice and four parameters are needed in advance. Simulated 3-D ultrasound image was used to test our algorithm. 3-D display of the carotid obtained by our algorithm showed almost identical shape with true 3-D carotid image. In addition, experimental results also demonstrated that error of the volume measurement of the carotid based on the three different initial contours is less that 1% and its speed was a very fast.
By analyzing the noise properties of calibrated low-dose Computed Tomography (CT) projection data, it is clearly seen that the data can be regarded as approximately Gaussian distributed with a nonlinear signal-dependent variance. Based on this observation, the penalized weighted least-square (PWLS) smoothing framework is a choice for an optimal solution. It utilizes the prior variance-mean relationship to construct the weight matrix and the two-dimensional (2D) spatial information as the penalty or regularization operator. Furthermore, a K-L transform is applied along the z (slice) axis to further consider the correlation among different sinograms, resulting in a PWLS smoothing in the K-L domain. As a tool for feature extraction and de-correlation, the K-L transform maximizes the data variance represented by each component and simplifies the task of 3D filtering into 2D spatial process slice by slice. Therefore, by selecting an appropriate number of neighboring slices, the K-L domain PWLS smoothing fully utilizes the prior statistical knowledge and 3D spatial information for an accurate restoration of the noisy low-dose CT projections in an analytical manner. Experimental results demonstrate that the proposed method with appropriate control parameters improves the noise reduction without the loss of resolution.
An optical heterodyne system for the measurement of profile and roughness has been developed. Several improved techniques are employed. The optical system was designed with entire common path. The effect of sample vibration and the thermal drift could be eliminated. A modified objective was used to perform respectively the measurement beam and the reference beam. The detected signals were processed with phase comparison technique to give a high accuracy. The optical system can be developed to an accessory of the Zeeman laser interferometers.
The paper introduces a new method to measure the flatness of the disk
with the laser heterodyne interferometer. Its most important advantage is
that it can measure the large and intermediate scale disk profile at the
same time, without sacrificing the measurement accuracy. The resolution
of the interferometer is 0.3 nm, the accuracy better than 0.04 jim, the
dynamic range larger than 5 mm. Otherwise the interferometer can nieasure
the disk global and local profile in tangential and radial direction.
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