63 results about "Streaking Artifact" patented technology

The present invention is a multi-stage detection algorithm using a successive nodule candidate refinement approach. The detection algorithm involves four major steps. First, the lung region is segmented from a whole lung CT scan. This is followed by a hypothesis generation stage in which nodule candidate locations are identified from the lung region. In the third stage, nodule candidate sub-images pass through a streaking artifact removal process. The nodule candidates are then successively refined using a sequence of filters of increasing complexity. A first filter uses attachment area information to remove vessels and large vessel bifurcation points from the nodule candidate list. A second filter removes small bifurcation points. The invention also improves the consistency of nodule segmentations. This invention uses rigid-body registration, histogram-matching, and a rule-based adjustment system to remove missegmented voxels between two segmentations of the same nodule at different times.

The present invention is directed to a method for enabling volumetric image reconstruction from unknown projection geometry of tomographic imaging systems, including CT, cone-beam CT (CBCT), and tomosynthesis systems. The invention enables image reconstruction in cases where it was not previously possible (e.g., custom-designed trajectories on robotic C-arms, or systems using uncalibrated geometries), and more broadly offers improved image quality (e.g., improved spatial resolution and reduced streak artifact) and robustness to patient motion (e.g., inherent compensation for rigid motion) in a manner that does not alter the patient setup or imaging workflow. The method provides a means for accurately estimating the complete geometric description of each projection acquired during a scan by simulating various poses of the x-ray source and detector to determine their unique, scan-specific positions relative to the patient, which is often unknown or inexactly known (e.g., a custom-designed trajectory, or scan-to-scan variability in source and detector position).

The invention discloses an X-ray multi-energy spectrum computed tomography (CT) projection data processing and image reconstruction method, which mainly comprises an X-ray energy spectrum CT projection sinogram processing method and a compressed sensing-based accelerated iterative convergent reconstruction algorithm. The X-ray energy spectrum CT projection sinogram processing method mainly comprises the following steps of: (1) restraining vertical streaking artifacts in a projection sinogram; (2) removing high-brightness noisy points in the projection sinogram. The compressed sensing-based accelerated iterative convergent reconstruction algorithm refers to that image total variation (TV) minimization-based optimal constraint conditions and the ordered-subset simultaneous algebraic reconstruction techniques (OS-SART) are combined. As a number of defects still exist in a traditional X-ray energy spectrum CT detection system (X-ray energy resolution photon counting detector), more noise and artifacts exist in acquired projection data. According to the X-ray multi-energy spectrum CT projection data processing and image reconstruction method, X-ray multi-energy spectrum CT projection data are effectively preprocessed by utilizing a preprocessing means, and meanwhile, a TV-based OS-SART algorithm is introduced into X-ray multi-energy spectrum CT image reconstruction, and therefore, image iterative convergence is accelerated, and the noise and the artifacts in a reconstructed image is well restrained.

CT imaging is enhanced by adaptively filtering x-ray attenuation data prior to image reconstruction. Detected x-ray projection data are adaptively and anisotropically filtered based on the locally estimated orientation of structures within the projection data from an object being imaged at a plurality of rotation positions. The detected x-ray data are uniformly low pass filtered to preserve the local mean values in the data, while the high pass filtering is controlled based on the estimated orientations. The resulting filtered data provide projection data with smoothing along the structures while maintaining sharpness along edges. Image noise and noise induced streak artifacts are reduced without increased blurring along edges in the reconstructed images. The enhanced image allows reduced x-ray dose while maintaining image quality.

A method and system for reducing localized artifacts in imaging data, such as motion artifacts and bone streak artifacts, are provided. The method includes segmenting the imaging data to identify one or more suspect regions in the imaging data near which localized artifacts are expected to occur, defining an artifact-containing region of interest in the imaging data around each suspect region, and applying a local bias field within the artifact-containing regions to correct for the localized artifacts.

The invention discloses a statistical iterative reconstructing method for a low-dose X-ray CT image. The statistical iterative reconstructing method includes reconstructing an image for projection data y<raw> of the low-dose X-ray image of CT equipment to obtain an initial iterative image mu<init>; restoring the projection data y<raw> to obtain the restored projection data y<restored>, reconstructing an image for the restored projection data y<restored> to obtain a reference image mu<ref>; based on the reference image mu<ref> and the initial iterative image mu<init>, constructing an edge-preserving prior R (mu<init>) according to FORMULA (shown in the description), wherein phi () is an energy potential function, and SRNLM (mu<init>) is non-local mean filtering led by the reference image mu<ref>; performing iterative computation according to the edge-preserving prior R (mu<init>) of the initial iterative image mu<init> by means of a statistical iterative formula to obtain an iterative reconstructed image mu<iter>; when the iterative result of the reconstructed image mu<iter> satisfies the iteration stopping condition, stopping iterating, and obtaining the final reconstructed image of the low-dose X-ray CT image. The statistical iterative reconstructing method for the low-dose X-ray CT image is capable of effectively eliminating the image noise, inhibiting the streak artifact and well keeping the detail information of the image.

Streak artifacts generated by at least one X-ray attenuating object arranged outside a reconstruction volume in a three-dimensional image data set showing the reconstruction volume are reduced. The reconstruction volume is reconstructed from two-dimensional projection images recorded from different projection directions. The object is localized in the projection images showing the object. To determine corrected projection images for the reconstruction of the image data set, the image data of the area of each projection image showing the object is corrected to remove the object. The localization of the object is performed taking into account difference images of the measured projection images and from a reconstruction data set of forward-projected comparative images reconstructed from the measured projection images.

The invention discloses a low-dose X-ray CT projection data restoring method. The low-dose X-ray CT projection data restoring method includes acquiring projection data y<raw> of a low-dose X-ray CT image; setting up a data restoring model (shown in the description) based on penalized weighted least absolute for the projection data y raw, restoring the projection data y<raw> to obtain restored projection data y<restored>, wherein p is ideal projection data to be calculated, the parameter lambda is a non-negative real number, w is a weight factor (shown in the description), wherein both the parameters beta and epsilon are non-negative real numbers, and (shown in the description) is variance of the projection data y<raw>; reconstructing an image for the restored projection data y<restored> by means of an analyzing and reconstructing method to obtain the final low-dose X-ray CT image. The low-dose X-ray CT projection data restoring method is capable of restoring the low-dose CT projection data which is capable of reducing the tube current and scanning time and reconstructing the image through the analyzing and reconstructing method, the image noise is eliminated effectively, the streak artifact is inhibited, and the detail information of the image is well kept.

An image processing system, comprising an input port (IN) for receiving a projection image of an object. The image is acquired by a rotational image apparatus (IM) at a position on an imaging trajectory in an adjustable rotation plane (π) around an imaging region. An image artifact extent predictor (AP) of the system is configured to predict for said image a projection area of a reconstruction artifact. A visualizer (VIZ) is configured to visualize, on a display unit (MT), said image with a visual indication of the projection area.

A computed tomography apparatus and method using line data estimated from circle data and scanogram data. An image of a subject is reconstructed using the circle data and the estimated line data. The circle data and scanogram data may be weighted in estimating the line data. The apparatus and method are useful in diminishing or eliminating streak artifacts in reconstructed images such as images including the spine.

A method for the reduction of streak artifacts in an image data set reconstructed from projection images of an X-ray device is provided. The method includes determining a first interim data set by applying a non-linear low-pass filter to pixels that satisfy a selection condition. A second non-linear, high-pass-filtered interim data set is determined by pixel-by-pixel subtraction of the first interim data set from the image data set. The second interim data set is Fourier transformed in order to obtain a spatial frequency data set. Frequency portions attributable to artifacts in the spatial frequency data set are removed, and the processed spatial frequency data set is inverse Fourier transformed, such that a third interim data set is obtained. An artifact-reduced result data set is determined by addition of the third interim data set and the first interim data set.