Abstract: Noise assessment is important to image quality evaluation as well as image processing. For example, the noise level estimation is used as criteria for terminating an iterative noise reduction process. To determine a meaningful noise level, the pixels in featureless regions are separated from the rest of the image. A new concept of pseudo-standard deviation (PSD) is introduced to automatically determine simple and reliable noise level estimates. Furthermore, a histogram of PSD is constructed with fine bins to calculate the moving average of the histogram. The first peak in filtered histogram gives the most representative noise measure as a desired approximation of true standard deviation.

Abstract: Cone beam artifacts arise in circular CT reconstruction. The cone beam artifacts are substantially removed by reconstructing a reference image from measured data at circular source trajectory, generating synthetic data by forward projection of the reference image along a pre-determined source trajectory, which supplements the circular source trajectory to a theoretically complete trajectory, reconstructing a correction image from the synthetic data and substantially reducing the cone beam artifacts by generating a corrected image using the reference image and the correction image.

Abstract: Streak artifacts arise in helical CT reconstruction with cone beam weighting (CBW) with helical pitch ratio between 0.5 and 1.0 in a prevalent 2PI mode. The sreak artifacts are substantially removed by applying upsampling to the measured data in the segment direction before weighting. Furthermore, by making the upsampling adaptive to the view Z-position, an amount of extra processing is greatly reduced to near 1%.

Abstract: The image generation method and system generates an image using a predetermined iterative reconstruction technique from cone beam data that has been expanded by adding additional data, and an instance of the iteration process is weighted according to a corresponding validation weight during the reconstruction. Optionally, an instance of the iteration process is weighted according to a combination of weights during the reconstruction. The predetermined combination of the weights includes axial weights based upon a validity value of the expanded data and statistical weights.

Abstract: One technique performs noise removal substantially removing dot noise based upon pseudo-standard deviation (PSD). Another technique performs noise removal including the substantial removal of dot noise based upon Z-score with a punctured approach. The techniques are not limited to particular kernels and include both uniform and non-uniform kernels.

Abstract: An image is reconstructed based upon a filtered backprojection (FBP) technique using a reconstruction filter which is customized or shaped by parameters including a weight value that is determined for weighing projection data according to a predetermined noise model. The weight value is determined based upon rays or views in the projection data. A regularization term is also combined with the noise-weighted FBP.

Abstract: The image reconstruction method and system reconstructs a multi-scale image using a set of predetermined nested 3D grids, and a zoomed image is reconstructed without losing details. The multiple steps uses a decreasing grid size to reconstruct an ultimately zoomed image in region of interest without degrading image quality.

Abstract: The CT imaging system optimizes its image generation by updating an image with the current application of a data fidelity update and a regularization update together in a single step in an iterative reconstruction algorithm. The iterative reconstruction algorithm includes the ordered subsets simultaneous algebraic reconstruction technique (OS SART) and the simultaneous algebraic reconstruction technique (SART). The data fidelity update and the regularization update are independently obtained using some predetermined statistical information such as noise and or error in matching the real data.

Abstract: The CT imaging system optimizes its image generation by adaptively changing parameters in an iterative reconstruction algorithm based upon certain information such as statistical information. The coefficients for the parameters include at least a first coefficient for a predetermined data fidelity process and a second coefficient for a predetermined regularization process in an iterative reconstruction algorithm. The iterative reconstruction algorithm includes the ordered subsets simultaneous algebraic reconstruction technique (OSSART) and the simultaneous algebraic reconstruction technique (SART). The first coefficient and the second coefficient are independently determined using some predetermined statistical information such as noise and or error in matching the real data.

Abstract: The methods and systems of the present invention is an algorithm which estimates motion inside objects that change during the scan. The algorithm is flexible and can be used for solving the misalignment correction problem and, more generally, for finding scan parameters that are not accurately known. The algorithm is based on Local Tomography so it is faster and is not limited to a source trajectory for which accurate and efficient inversion formulas exist.

Abstract: The image generation method and system generates an image using a predetermined iterative reconstruction technique, and an instance of the iteration process is weighted according to a predetermined combination of weights during the reconstruction. The predetermined combination of the weights includes weights based upon a predetermined noise model and a predetermined window function to improve image quality.

Abstract: Photon starvation causes streaks and noise and seriously impairs the diagnostic value of the CT imaging. To reduce streaks and noise, a new scheme of adaptive Gaussian filtering relies on the diffusion-derived scale-space concept in one embodiment of the current invention. In scale-space view, filtering by Gaussians of different sizes is similar to decompose the data into a sequence of scales. As the scale measure, the variance of the filter linearly relates to the noise standard deviation of a predetermined noise model in the new filtering method. The new filter has only one optional parameter that remains stable once tuned. Although single-pass processing using the new filter generally achieves desired results, iterations are optionally performed.

Abstract: Photon starvation causes streaks and noise and seriously impairs the diagnostic value of the CT imaging. To reduce streaks and noise, a new scheme of adaptive Gaussian filtering relies on the diffusion-derived scale-space concept in one embodiment of the current invention. In scale-space view, filtering by Gaussians of different sizes is similar to decompose the data into a sequence of scales. As the scale measure, the variance of the filter linearly relates to the noise standard deviation of a predetermined noise model in the new filtering method. The new filter has only one optional parameter that remains stable once tuned. Although single-pass processing using the new filter generally achieves desired results, iterations are optionally performed.

Abstract: Spatial resolution is substantially improved by simulating a system blur kernel including an angle variable in the forward projection during a predetermined iterative reconstruction technique. The iterative reconstruction acts as a deconvolution, which overcomes certain restrictions of system optics. In general, resolution is substantially improved with cone beam and helical data without a large increase in noise.

Abstract: The image reconstruction method and system reconstructs a multi-scale image using a set of predetermined nested 3D grids, and a zoomed image is reconstructed without losing details. The multiple steps uses a decreasing grid size to reconstruct an ultimately zoomed image in region of interest without degrading image quality.

Abstract: Cone beam artifacts arise in circular CT reconstruction. The cone beam artifacts are substantially removed by reconstructing a reference image from measured data at circular source trajectory, generating synthetic data by forward projection of the reference image along a pre-determined source trajectory, which supplements the circular source trajectory to a theoretically complete trajectory, reconstructing a correction image from the synthetic data and substantially reducing the cone beam artifacts by generating a corrected image using the reference image and the correction image.

Abstract: The image generation method and system generates an image using a predetermined iterative reconstruction technique, and an instance of the iteration process is weighted according to a predetermined combination of weights during the reconstruction. The predetermined combination of the weights includes weights based upon a predetermined noise model and a predetermined window function to improve image quality.

Abstract: Streak artifacts arise in helical CT reconstruction with cone beam weighting (CBW) with helical pitch ratio between 0.5 and 1.0 in a prevalent 2PI mode. The sreak artifacts are substantially removed by applying upsampling to the measured data in the segment direction before weighting. Furthermore, by making the upsampling adaptive to the view Z-position, an amount of extra processing is greatly reduced to near 1%.

Abstract: The image generation method and system generates an image using a predetermined iterative reconstruction technique from cone beam data that has been expanded by adding additional data, and an instance of the iteration process is weighted according to a corresponding validation weight during the reconstruction. Optionally, an instance of the iteration process is weighted according to a combination of weights during the reconstruction. The predetermined combination of the weights includes axial weights based upon a validity value of the expanded data and statistical weights.

Abstract: Noise assessment is important to image quality evaluation as well as image processing. For example, the noise level estimation is used as criteria for terminating an iterative noise reduction process. To determine a meaningful noise level, the pixels in featureless regions are separated from the rest of the image. A new concept of pseudo-standard deviation (PSD) is introduced to automatically determine simple and reliable noise level estimates. Furthermore, a histogram of PSD is constructed with fine bins to calculate the moving average of the histogram. The first peak in filtered histogram gives the most representative noise measure as a desired approximation of true standard deviation.