Abstract: A set of image-space data is reconstructed from a set of k-space data. The set of image-space data is generated by minimizing a cost functional by an iterative non-linear conjugate gradient process. The iterative process may be accelerated by introducing k-space weighting to the cost functional. With proper choice of k-space weighting, a block-Toeplitz matrix is generated which permits use of Fast Fourier Transform techniques. An image is rendered from the set of image-space data.

Abstract: Certain exemplary embodiments can comprise a method, which can comprise providing machine instructions adapted to render a final enhanced image of an object based upon a final enhanced set of data. The final enhanced set of data can be derived from a detected set of data obtained from an imaging device, such as a medical imaging device.

Abstract: For resonance image data of an imaged subject, a method that first detects and estimates the dominant motions of k-space data (i.e., the motion vectors) and then constructs a graphical model for each estimated motion vector. The segments of the k-space that are determined to be corrupted by motion are restored by minimizing the energy associated with the corresponding graphical model. Consequently, the MR image of the imaged subject becomes free of motion artifacts.

Abstract: A method for temporally filtering medical images during a fluoroscopy guided intervention procedure includes providing a mask image, a fluoroscopy intervention image acquired at a current time during a medical intervention procedure, forming a subtraction image by subtracting the mask image from the intervention image, calculating a motion image of a moving structure in the subtraction image, forming a residual image by subtracting the motion image from the subtraction image, temporally filtering the residual image with a filtered image from a previous time, and adding the motion image to the temporally filtered residual image.

Abstract: A method for estimating feature values from a digitized image includes calculating a gradient of an image, defining a filter weighting function, convolving the gradient with the filter weighting function, integrating over a subdomain of the convolved gradient to calculate components of a cost matrix ?, wherein the cost matrix minimizes a distance metric in a frequency domain of the image, solving for the eigenvalues of the cost matrix, and calculating one or more features from the cost matrix eigenvalues. One or more predesigned bandpass filters can be applied to the image, and cost matrix eigenvalues are analyzed to select one or more appropriate bandpass filters to be applied to the image.

Abstract: A method of designing filters for filtering digitized images includes constructing a set of difference-of-Gaussian (DoG) filters that partition a frequency space, forming a linear combination of said DoG filters to form a trial filter, convolving said trial filter with a training image to obtain a trial image, summing a square difference of said trial image and a target image, wherein if said sum is a minimum, outputting said trial filter as a final filter, and using a singular value decomposition on said final filter wherein an eigenvector corresponding to the largest eigenvalue of the final filter is obtained.

Abstract: A method and system for image quality assessment is disclosed. The image quality assessment method is a no-reference method for objectively assessing the quality of medical images. This method is guided by the human vision model in order to accurately reflect human perception. A region of interest (ROI) of medical image is divided into non-overlapping blocks of equal size. Each of the blocks is categorized as a smooth block, a texture block, or an edge block. A perceptual sharpness measure, which is weighted by local contrast, is calculated for each of the edge blocks. A perceptual noise level measure, which is weighted by background luminance, is calculated for each of the smooth blocks. A sharpness quality index is determined based on the perceptual sharpness measures of all of the edge blocks, and a noise level quality index is determined based on the perceptual noise level measures of all of the smooth blocks.

Abstract: A method and system for correcting butting artifacts in x-ray images is disclosed. In order to correct a butting artifact in an x-ray image, a butting artifact region in the x-ray image is normalized. Multiple intensity shift estimators are calculated for each pixel of each line of the butting artifact. Confidence intervals are calculated for each intensity shift estimator. A multiple hypothesis hidden Markov model (MH-HMM) is formulated based on the intensity shift operators and confidence measures subject to a smoothness constraint, and the MH-HMM is solved to determine intensity shift values for each pixel. A corrected image is generated by adjusting the intensity of each pixel of the butting artifact based on the intensity shift value for that pixel.

Abstract: Image data processing methods, hard imaging devices, and articles of manufacture are described. According to one embodiment, an image data processing method includes accessing image data of an image and comprising a plurality of pixels, defining a plurality of subsets of the pixels, defining an overlapping region comprising image data of pixels located adjacent to a boundary intermediate one and an other of the subsets, independently processing the image data of the one and the other of the subsets individually comprising comparing the image data for respective ones of the pixels with a plurality of thresholds corresponding to respective ones of the pixels, and modulating the thresholds of the pixels of the overlapping region using a common modulation pattern for both the processing of the one and the other subsets.

Abstract: A method and system for structure enhancement and noise reduction of medical images using adaptive filtering is disclosed. The method utilizes feature estimation methods to determine multiple feature values for each pixel in an input image. Each pixel is then filtered using a filter type selected based on the feature values for that pixel.

Abstract: A set of image-space data is reconstructed from a set of k-space data. The set of image-space data is generated by minimizing a cost functional by an iterative non-linear conjugate gradient process. The iterative process may be accelerated by introducing k-space weighting to the cost functional. With proper choice of k-space weighting, a block-Toeplitz matrix is generated which permits use of Fast Fourier Transform techniques. An image is rendered from the set of image-space data.

Abstract: A system and method for feature detection in ultrasound images is disclosed. The method estimates speckle distributions in windows on opposing sides of a pixel of an ultrasound image. The divergence is calculated for the pixel between the estimated speckle distributions in the windows. These steps are performed for each pixel in the ultrasound image, and a feature map is generated based on the divergence calculated between the estimated speckle distributions for each pixel.

Abstract: A method for processing image data includes inputting image data, determining a plurality of quadrature filter pairs based on filter parameter values to detect features of interest in the image data, applying the quadrature filter pairs to the image data to obtain a set of filter responses, and processing the filter responses to obtain the features of interest in the image data.

Abstract: A method for reconstructing an image generated from radial trajectory data in frequency or k-space using a GPU. The method includes using a vertex shader of the GPU to transform coordinates of a window aligned with the radial trajectory data and using a pixel shader of the GPU to combine data along the radial trajectory with the coordinate transformed widow to distribute the data along the radial trajectory fed to the pixel shader into cells of a Cartesian coordinate system.

Abstract: A method for estimating feature values from a digitized image includes calculating a gradient of an image, defining a filter weighting function, convolving the gradient with the filter weighting function, integrating over a subdomain of the convolved gradient to calculate components of a cost matrix ?, wherein the cost matrix minimizes a distance metric in a frequency domain of the image, solving for the eigenvalues of the cost matrix, and calculating one or more features from the cost matrix eigenvalues. One or more predesigned bandpass filters can be applied to the image, and cost matrix eigenvalues are analyzed to select one or more appropriate bandpass filters to be applied to the image.

Abstract: A method of designing filters for filtering digitized images includes constructing a set of difference-of-Gaussian (DoG) filters that partition a frequency space, forming a linear combination of said DoG filters to form a trial filter, convolving said trial filter with a training image to obtain a trial image, summing a square difference of said trial image and a target image, wherein if said sum is a minimum, outputting said trial filter as a final filter, and using a singular value decomposition on said final filter wherein an eigenvector corresponding to the largest eigenvalue of the final filter is obtained.