69 results about "Computed tomography colography" patented technology

The present invention relates to the field of x-ray imaging. More particularly, embodiments of the invention relate to methods, systems, and apparatus for imaging, which can be used in a wide range of applications, including medical imaging, security screening, and industrial non-destructive testing to name a few. Specifically provided as embodiments of the invention are systems for x-ray imaging comprising: a) a first collimator-and-detector assembly having a first operable configuration to provide at least one first dataset comprising primary x-ray signals as a majority component of its data capable of being presented as a first image of an object subjected to x-ray imaging; b) a second collimator-and-detector assembly having a second operable configuration or wherein the first collimator-and-detector assembly is adjustable to a second configuration to provide at least one second dataset comprising primary and dark-field x-ray signals as a majority component of its data capable of being presented as a second image of the object; and c) a computer operably coupled with the collimator-and-detector assemblies comprising a computer readable medium embedded with processing means for combining the first dataset and the second dataset to extract the dark-field x-ray signals and produce a target image having higher contrast quality than the images based on the first or second dataset alone. Such systems can be configured to comprise at least two collimator-and-detector assemblies or configurations differing with respect to collimator height, collimator aperture, imaging geometry, or distance between an object subjected to the imaging and the collimator-and-detector assembly.

A deliverable four dimensional (4D) intensity modulated radiation therapy (IMRT) planning method is disclosed, for delivery using a linear accelerator with a dynamic multi-leaf collimator (DMLC). A 4D computed tomography (CT) scan is used for segmenting tumor anatomy on a reference phase of periodic motion of the tumor. Deformable registration of the 4D CT data is used to generate corresponding anatomical structures on other phases. Preferably, the collimator for each beam position is aligned using the gross tumor volume (GTV) centroid motion corresponding to the periodic motion of the tumor, as determined from the two dimensional (2D) projection of a given beam position. A deliverable IMRT plan is created on the 4D CT image set in which the MLC leaf positions and beam on / off status can vary as a function of respiratory phase by solving a four dimensional optimization problem. The mechanical constraints of the MLC leaves are included in the optimization.

A 3D metal artifacts correction technique corrects the streaking artifacts generated by titanium implants or other similar objects. A cone-beam computed tomography system is utilized to provide 3D images. A priori information (such as the shape information and the CT value) of high density sub-objects is acquired and used for later artifacts correction. An optimization process with iterations is applied to minimize the error and result in accurate reconstruction images of the object.

In CT (computed tomography) images, streak artifacts caused by the presence of metal implants and inhomogeneous estimation of tissue density are reduced or eliminated. The algorithm has two basic steps: 1) illumination correction and 2) adaptive 3D filtering. The algorithm starts by estimating the direction of the streak and the degree of inhomogeneous densities by gray scale morphology dilatation. Then, it proceeds to estimate the correct densities based on the estimations and to reduce the streak by an adaptive 3D filtering whose parameters depend on the streak direction and the local image contrast.

A method of and a system for identifying objects using histogram segment features from multi-energy CT images are provided. The multi-energy CT images include a CT image, which approximates density measurements of scanned objects, and a Z image, which approximates effective atomic number measurements of scanned objects. The method comprises: computing a density histogram for each potential threat object; smoothing the density histogram using a low-pass filter; identifying peaks in the smoothed density histogram; assigning a segment to each peak; computing histogram segment features for each segment; classifying each potential threat object into a threat or a non-threat using computed features.

The invention belongs to a method for quickly detecting the water content of bamboo on the basis of an X-ray CT (computed tomography) technology. The method comprises steps as follows: standard specimens for detecting the water content of to-be-detected bamboo are selected as required; the manufactured specimens are put into dryers containing different saturated salt solutions, the mass of the specimens in different water content gradient states and the mass of the totally dry specimens are weighed according to Testing Methods for Physical and Mechanical Properties of Bamboos GB / T 15780-1995 when the water content of the specimens is stable and does not change, the specimens in different water content states are subjected to CT scanning in the axial direction of moso bamboo by means of the X-ray CT technology, appropriate scanning parameters are selected according to different bamboo species, the number of scanned layers is determined according to the thickness of the scanned layer and the interval between the adjacent layers, a fitting equation is established according to a fitting curve, and the water content of the to-be-detected sample is calculated. The method is environment-friendly and efficient, can be used for detecting the water content of the bamboo conveniently, quickly and intactly and has the advantages of accuracy, environment-friendliness, low cost and simplicity and convenience in operation.

A method for imaging unknown objects in a computed tomography (CT) system, comprising determining ray gain for a known object is provided. A CT reconstruction is performed with the known object to obtain reconstructed values. Ideal values are obtained for pixels of the known object. An error related to a difference between the reconstructed values and the ideal values is generated. A ray gain is estimated that reduces the error.

A method and apparatus are disclosed for high-sensitivity Single-Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET). The apparatus includes a two-dimensional (2D) gamma detector array that moves to different positions in a three-dimensional (3D) volume space near an emission source and records a data vector g. In particular, the 3D volume space in which emission data g is measured extends substantially along a radial direction r pointing away from the emission source and each photon detector element in the 2D gamma detector array is provided with a very large collimator aperture. Data g is related to the 3D spatial density distribution f of the emission source, noise vector n, and a system matrix H of the SPECT / PET apparatus through the linear system of equations g=Hf+n. This equation is solved for f by a method that reduces the effect of noise.

The present application discloses an x-ray detector for use in a computed tomography (CT) imaging system. The x-ray detector assembly comprises an array of detector cells coupled between detector rails. The present invention provides a self regulating heating element having a body that, when current is passed therethrough, radiates heat until a specific reference temperature is reached, at which point the resistance of the PTC heater increases, thus reducing the current through the PTC heater, and, as a result, the radiative heating of the PTC heater.

A method and apparatus for automated reconstruction of a 3D computed tomography (CT) volume from a small number of X-ray images is disclosed. A sparse 3D volume is generated from a small number of x-ray images using a tomographic reconstruction algorithm. A final reconstructed 3D CT volume is generated from the sparse 3D volume using a trained deep neural network. A 3D segmentation mask can also be generated from the sparse 3D volume using the trained deep neural network.