155 results about "Cone beam computed tomography" patented technology

A device for imaging an object, such as for breast imaging, includes a gantry frame having mounted thereon an x-ray source, a source grating, a holder or other place for the object to be imaged, a phase grating, an analyzer grating, and an x-ray detector. The device images objects by differential-phase-contrast cone-beam computed tomography. A hybrid system includes sources and detectors for both conventional and differential-phase-contrast computed tomography.

The present disclosure provides a system and method for generating medical images. The method utilizes a novel algorithm to co-register Cone-Beam Computed Tomography (CBCT) volumes and additional imaging modalities, such as optical or RGB-D images.

Described herein are improved methods for correcting cone beam computed tomography signals to reduce scatter contamination contained therein. Generally, the improved methods involve generating a plurality of two-dimensional projection images of a subject from a three-dimensional multi-detector computed tomography image of the subject. This is followed by subtracting the plurality of two-dimensional projection images from a plurality of two-dimensional cone beam projection images of the subject to produce a plurality of two-dimensional estimated error projections that comprise an estimated error in the plurality of two-dimensional cone beam projection images. The plurality of two-dimensional estimated error projection images are subtracted from the plurality of two-dimensional cone beam projection images to generate a plurality of two-dimensional corrected cone beam projection images. A three-dimensional corrected cone beam computed tomography image of the subject is then constructed from the plurality of two-dimensional corrected cone beam projection images.

The present invention discloses a dentition model generation method based on oral cavity scanning data and CBCT (Cone Beam Computed Tomography) data. The method comprises: reading a three-dimensional multi-source data model of an oral cavity; setting and registering a fixed model and a floating model, wherein the fixed model is a triangular mesh model created by means of CBCT, and the floating model is a triangular mesh model of intraoral scanning; manually picking up feature point pairs of the models on the fixed model and the floating model, correcting the feature point pairs according to point cloud curvature of the models, moving feature points to a point with the largest curvature in a surrounding neighborhood, and performing preliminary registration on the feature point pairs by using a point-to-point ICP (Iterative Closest Point) algorithm; calculating a point cloud error of the models after preliminary registration, and by combination with medical parameter requirements, setting a parameter of precise registration; performing precise registration by using an optimized point-to-surface ICP algorithm; and fusing the CBCT data and intraoral scanning data after registration, and outputting tooth root and crown models after registration and fusion. Rapid and precise registration and fusion of data of different devices or data of different postures of patients are implemented.

An apparatus for cone beam computed tomography of an extremity has a digital radiation detector and a first device to move the detector along a circular detector path extending so that the detector moves both at least partially around a first extremity of the patient and between the first extremity and a second, adjacent extremity. The detector path has radius R1 sufficient to position the extremity approximately centered in the detector path. There is a radiation source with a second device to move the source along a concentric circular source path having a radius R2 greater than radius R1, radius R2 sufficiently long to allow adequate radiation exposure of the first extremity for an image capture by the detector. A first circumferential gap in the source path allows the second extremity to be positioned in the first circumferential gap during image capture.

The invention discloses a geometrical parameter calibration method of an X-ray cone beam computed tomography system and belongs to the field of the X-ray cone beam computed tomography systems (a cone beam CT for short). By means of a camera calibration technique, a geometrical position relationship among an X-ray source, a panel detector and a rotary shaft in a cone beam CT system is reproduced, thereby geometrical parameters and errors of the cone beam CT can be directly solved, the geometrical parameter calibration method is a systematized measurement solving method, the cone beam CT can beabstracted to be a basic camera system model, and then a plurality of correlative geometrical parameters in the system can be simultaneously solved. Compared with prior calibration methods, the geometrical parameter calibration method has the advantages that the geometrical parameters are not required to be measured one by one, repeated adjustment for the parameters one by one is not required, thereby the complexity level of calibration measurement of the whole system is greatly simplified, and simultaneously the stability of the cone beam CT geometrical calibration is improved.

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.

The invention discloses a cone-beam computed tomography image and X-ray image registration method; the method uses a regression model based on a mixing residual error convolution nerve network to build associations between two-dimension X-ray images and three dimensional cone-beam CT image non-rigid deformation parameters, and uses a mixing residual error convolution nerve network and deformation parameter iteration optimization method to realize reliable online two-dimension three dimensional image registration; the method comprises the following steps: extracting to obtain an image channel; training the regression model based on the mixing residual error convolution nerve network; carrying out two-dimension three dimensional non-rigid registration based on regression; iterating and optimizing deformation parameters; obtaining a final body image determined by the deformation parameters, and thus realizing two-dimension three dimensional image registration based on iteration regression. The method can realize reliable online two-dimension three dimensional image registration, can be applied to oral cavity clinics, and can evaluate treatments and analyze craniofacial growth according to two-dimension three dimensional image registrations.

Disclosed are embodiments of methods of and systems for detecting and biopsying lesions with a cone beam computed tomography (CBCT) system. The system comprises, in some embodiments, a CBCT device configured to output a cone beam CT image of at least a portion of a patient's breast and a multi-axis transport module, having at least three degrees of freedom and configured to position a biopsy needle within a 3D frame of reference based on inputs received from the cone beam CT device. The transport module can be configured to place the biopsy needle adjacent to, or within, a target of interest within the breast. Also disclosed are embodiments of methods of and systems for testing CBCT systems with phantoms.

The invention provides an image denoising method specific to cone beam computed tomography (CBCT) and relates to the technical field of radiation imaging. An area similar to the projection data is searched in adjacent projection data (similarity is calculated and determined through SSIM) specific to each pixel to be denoised and neighbourhood of the pixel in CBCT projection data according to data redundancy of the projection data, weighting average calculation is carried out on the pixel-to-be-denoised by utilizing the data in the similar areas so as to effectively reduce noises of the projection data, and three-dimensional reconstruction is preformed after denoising process is finished. The method is characterized by utilizing high similarity and data redundancy of the adjacent projection data. Compared with a traditional image denoising method, the image denoising method can achieve a better denoising effect, effectively improves the signal to noise ratio, and maintains image edge and detail information to the maximum extent. Meanwhile, the sequence image denoising concept is also applied to the filed of visible light imaging, such as the fields of denoising and the like.

A method of providing a corrected reconstructed computed tomography image accesses image data for computed tomography images of a subject, identifying a subset of the computed tomography images that contain high density features. At least one high density feature is detected in each of the identified subset. The high density feature is classified and a compensation image is formed by distributing pixels representative of tissue over the classified high density feature. A difference sinogram is generated for each image in the identified subset of images by subtracting a first sinogram of the high density feature from a second sinogram of the original image. A resultant sinogram is generated for each image in the identified subset by adding a third sinogram generated according to the compensation image to the difference sinogram. The corrected reconstructed computed tomography image is formed according to the resultant sinogram generated for each image in the identified subset of images.

The present invention is directed to a system and method for dual-energy (DE) or multiple-energy (spectral) cone-beam computed tomography (CBCT) using a configuration of multiple x-ray sources and a single detector. The x-ray sources are operated to produce x-ray spectra of different energies (peak kilovoltage (kVp) and/or filtration). Volumetric 3D image reconstruction and dual or triple energy 3D image decomposition can be executed using data from the CBCT scan. The invention allows for a variety of selections in energy and filtration associated with each source and the order of pulsing for each source (“firing pattern”). The motivation for distributing the sources along the z direction in CBCT includes extension of the longitudinal field of view and reduction of cone-beam artifacts.

An apparatus for cone beam computed tomography can include a support structure, a scanner assembly coupled to the support structure for controlled movement in at least x, y and z orientations, the scanner assembly can include a DR detector configured to move along at least a portion of a detector path that extends at least partially around a scan volume with a distance D1 that is sufficiently long to allow the scan volume to be positioned within the detector path; a radiation source configured to move along at least a portion of a source path outside the detector path, the source path having a distance D2 greater than the distance D1, the distance D2 being sufficiently long to allow adequate radiation exposure of the scan volume for an image capture by the detector; and a first gap in the detector path.

Various methods and systems are provided for collision avoidance in cone-beam computed tomography (CT) systems. In one embodiment, a method for an imaging apparatus comprises calculating a likelihood of a collision between the imaging apparatus and a subject and/or its support based on a model of the subject, performing a scan of the subject responsive to the likelihood below a threshold, and not performing the scan otherwise. In this way, collisions between the patient and the imaging apparatus can be avoided.

Disclosed are methods, systems, and computer-product programs for increasing accuracy in cone-beam computed tomography.

The invention discloses an offline dose verification method based on improved CBCT (cone beam computed tomography) images. The offline dose verification method includes: subjecting the CBCT images of an individual patient to scatter correction on the basis of Monte-Carlo simulation to acquire a first CBCT image; rectifying the first CBCT image with a planned CT (computed tomography) image to acquire target-region deformation field information; verifying outline information of the planned CD image according to the target-region deformation field information, transplanting the verified outline information to the CBCT images to acquire a second CBCT image; establishing HU-ED calibration curves of the CBCT images according to average HU value of a particular tissue area of the individual patient in the second CBCT image and ED value of a particular tissue area of the planned CT image; performing dose calculation and planned validation on the basis of the second CBCT image, the HU-ED calibration curves, the planned CT and standard CT-ED calibration curves. By the technical scheme, treatment time and cost of the patient can be saved, and accurate adaptive radiation therapy and individual radiation therapy are realized.