35 results about "Computed tomography colography" patented technology

A cross-regional and cross-view learning (C2RV) framework is provided for sparse-view reconstruction in cone-beam computed tomography (CBCT) by advantageously leveraging cross-region and cross-view feature learning to enhance representation of a point in 3D space before estimating an attenuation coefficient of the point. Specifically, multi-scale 3D volumetric representations (MS-3DV) are first introduced, where features are obtained by back-projecting multi-view features at different scales to the 3D space. Explicit MS-3DV enable cross-regional learning in the 3D space, providing richer information that helps better identify different internal anatomy structures. Hence, features of the point can be queried in a hybrid way, i.e. multi-scale voxel-aligned features from MS-3DV and multi-view pixel-aligned features from projections. Instead of considering queried features equally, scale-view cross-attention (SVC-Att) is used to adaptively learn aggregation weights by self-attention and cross-attention. Finally, multi-scale and multi-view features are aggregated to estimate the attenuation coefficient.

The invention relates to a scatter correction method, a cone beam computed tomography (CBCT) system and a computer storage medium. The method comprises the following steps: performing cone beam CT imaging on a to-be-measured object to obtain a first group of projection data of the to-be-measured object, and performing reconstruction according to the first group of projection data to obtain a first volume image containing scattering artifacts; performing fan-beam CT imaging on a plurality of different heights of the to-be-measured object to obtain a plurality of groups of fan-beam projection data of the to-be-measured object, performing reconstruction according to the plurality of groups of fan-beam projection data to obtain a plurality of fan-beam slice images, and using the plurality of fan-beam slice images as reference images without scattering artifacts; extracting a plurality of cone beam slice images from the first volume image; performing image registration on the plurality of fan-beam slice images and the plurality of cone-beam slice images; and training a scattering correction model based on deep learning by using the plurality of registered image pairs to obtain a trained scattering correction model.

Methods and systems are provided for calibrating a single photon emission computed tomography (SPECT) imaging system. In one example, a collimated line source for calibrating a SPECT system comprises a radioactive material aligned along a central axis of the collimated line source and a plurality of collimator plates arranged perpendicularly around the central axis, the collimator plates including a material that substantially absorbs photons striking the collimator plates. The collimator plates may absorb photons emitted by the radioactive material in non-perpendicular directions, which are not used for calibrating the SPECT imaging system, thereby reducing an amount of radiation released in an environment of the SPECT imaging system during calibration. Due to the reduced amount of radiation to which people in the environment are exposed, short time increments between patient examinations may be advantageously used to collect calibration data, reducing an overall amount of time used for calibration.

The broken lines shown in the drawings illustrate portions of the computed tomography scanner that form no part of the claimed design.

A system for low-count quantitative single-photon emission computed tomography (LC-QSPECT) is provided. The system is programmed to a) store a computer tomography (CT) scan of a subject being examining including a plurality of defined volumes of interest (VOIs) of the subject being examined; b) model a system matrix based on the stored CT, wherein the model describes the probability that photons emitted from each of the defined VOIs are detected in different projection bins, wherein a plurality of projection bins are defined around the subject and the defined VOIs; c) adjust the model with analysis of stray-radiation noise around the subject; d) detect, by the one or more sensors, one or more photons being emitted by an alpha-particle-emitting isotope; and e) execute the adjusted model with the one or more detected photons as inputs to determine a source VOI of the detected photons.

To estimate scatter in primary region projection data.SOLUTION: An x-ray imaging apparatus and associated methods are provided to receive measured projection data in a primary region and measured scatter data in asymmetric shadow regions and determine an estimated scatter in the primary region based on the measured scatter data in the shadow region(s). The asymmetric shadow regions can be controlled by adjusting the position of a beam aperture center on a readout area of the detector. Penumbra data may also be used to estimate scatter in the primary region.SELECTED DRAWING: None

A computer system, a non-transitory computer readable medium, and a method for computed tomography (CT) reconstruction. The computer system is configured to perform the method and the computer readable medium is configured to store instructions for performing the method. The method includes obtaining two or more spectral CT measurement data of different materials of a target from one or more 2D or 3D spectral CT images acquired by a CT imaging system using more than one CT imaging energy regimes; and producing a pair reconstructed spectral CT images that are representative of densities of the different materials of the target by performing one or more iterations on the two or more spectral CT measurement data using a trained machine learning network that is trained to performing a diffusion sampling operation and a forward-model-based likelihood on one or more intermediate data sets representing the two or more spectral CT measurement data.

The application discloses a kind of based on the sparse helical CT reconstruction method, system, equipment, medium and program product of back projection tensor interpolation, belong to computer tomography (CT) imaging technical field, specifically includes the following steps: generating the back projection tensor data under sparse angle, the back projection tensor data indicates the intermediate data of back projection process in filter back projection algorithm;Interpolation is carried out to the back projection tensor data of the sparse angle by depth learning network, generates the back projection tensor data of full angle;Image reconstruction is carried out based on the back projection tensor data of full angle, and the final CT image is obtained.The reconstruction method of the application suppresses the generation of strip artifact from the source and directly repairs the key intermediate data for reconstruction, which can better retain and restore the true anatomical structure information than post-processing in the image domain, avoids excessive smoothing or filtering, and has generalization ability, suitable for different CT scanning geometry.

A single photon emission computed tomography (SPECT) imaging system includes a detector configured to receive photons emitted by a radiopharmaceutical in an examination region and convert the received photons to measured projection data and a collimator with septa spatially arranged with respect to each other and the detector to provide channels to pass photons that traverse the channels and absorb photons that impinge the septa. A first sub-set of the photons emitted by the radiopharmaceutical traverse the channels without impinging the septa and are directly received by the detector. A second sub-set of the photons emitted by the radiopharmaceutical traverse the septa and are received by the detector. The SPECT imaging system further includes a reconstructor configured to discard scatter photons and the second sub-set of photons traversing the septa, and iteratively reconstruct an image based on the first subset of photons that are directly received by the detector.

Some examples relate to processor-implemented methods of training a neural network for soft tissue labeling of computed tomography (CT) images, a neural network trained according to one or more of the processor-implemented methods, a processor-implemented method of soft tissue labeling of a CT image, and a system for labeling soft tissue in a computed tomography image.

1. The name of the design product: digital single photon emission computed tomography imaging device. 2. The use of the design product: CT scan imaging for small animals. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

1. Name of the product in this design: Computed Tomography Scanning Device. 2. Purpose of this design: This product is intended for use as a medical device for monitoring and controlling scanning. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model. 5. The bottom surface of this product is a part that is not easily seen or cannot be seen during use, so the bottom view is omitted.

The present disclosure relates to a scatter correction method, a cone-beam computed tomography (CBCT) system and a computer storage medium. The method comprises: performing cone-beam CT imaging on an object to be measured to obtain a first set of projection data of the object to be measured, and reconstructing from the first set of projection data to obtain a first volume image containing scatter artifacts; performing fan-beam CT imaging on multiple different heights of the object to be measured to obtain multiple sets of fan-beam projection data of the object to be measured, and reconstructing from the multiple sets of fan-beam projection data to obtain multiple fan-beam slice images respectively, which are used as reference images not containing scatter artifacts; extracting multiple cone-beam slice images from the first volume image; performing image registration on the multiple fan-beam slice images and the multiple cone-beam slice images; and training a deep learning-based scatter correction model using the multiple registered images to obtain a trained scatter correction model.

Systems and methods for quantification of body composition using contrastive learning in computed tomography (CT) data. A segmentation model is provided that is trained using two stages. An encoder of the segmentation model is pretrained using unlabeled data. The encoder is extended by a decoder which is further trained using labeled data.

One embodiment is a circuit configuration for implementing a baseline restoration ("BLR") circuit for a photon counting computed tomography ("PCCT") signal chain, the circuit configuration comprising: a multi-level discriminator circuit for receiving a shaper voltage from the PCCT signal chain, the discriminator circuit outputting a digital signal indicative of one of a range of voltages within which the shaper voltage falls; a digital-to-analog converter ("DAC") connected to receive the digital signal output from the discriminator circuit, the DAC converting the received digital signal to a corresponding active reference voltage; and a feedback circuit for injecting a cancellation current at an input of the PCCT signal chain that is proportional to the difference between the shaper voltage and the active reference voltage.

A system and method of harmonizing computed tomography (CT) images to reduce variability introduced by differing reconstruction kernels. The method includes acquiring a CT image of a plurality of regions of a patient. The method includes calculating, by a processing device, a noise power spectrum (NPS) of a uniform region among the plurality of regions. The method includes selecting, from a library of reconstruction kernels, a first reconstruction kernel based on the NPS of the uniform region of the plurality of regions. The method includes generating a harmonized CT image based on the first reconstruction kernel and the CT image.

To provide a compact X-ray CT apparatus capable of performing standing position imaging and lying position imaging.SOLUTION: An X-ray CT apparatus includes a gantry, a support, a top plate, a top plate moving mechanism, and a movement control unit. The gantry has an imaging system for imaging a subject and an opening into which the subject can be inserted. The support supports the gantry such that the gantry is movable in a vertical direction and such that a direction of the opening is changeable between the vertical direction and a horizontal direction. The top plate can place the subject thereon and can be inserted into the opening. The top plate moving mechanism is provided in the opening of the gantry and moves the top plate. When the direction of the opening is the vertical direction, the movement control unit controls the top plate moving mechanism so as to move the top plate in a direction opposite a moving direction of the gantry in accordance with the movement of the gantry along the vertical direction.SELECTED DRAWING: Figure 1

A single photon emission computed tomography (SPECT) imaging system includes a detector configured to receive photons emitted by a radiopharmaceutical in an examination region and convert the received photons to measured projection data and a collimator with septa spatially arranged with respect to each other and the detector to provide channels to pass photons that traverse the channels and absorb photons that impinge the septa. A first sub-set of the photons emitted by the radiopharmaceutical traverse the channels without impinging the septa and are directly received by the detector. A second sub-set of the photons emitted by the radiopharmaceutical traverse the septa and are received by the detector. The SPECT imaging system further includes a reconstructor configured to discard scatter photons and the second sub-set of photons traversing the septa, and iteratively reconstruct an image based on the first subset of photons that are directly received by the detector.

A system and method of harmonizing computed tomography (CT) images to reduce variability introduced by differing reconstruction kernels. The method includes acquiring a CT image of a plurality of regions of a patient. The method includes calculating, by a processing device, a noise power spectrum (NPS) of a uniform region among the plurality of regions. The method includes selecting, from a library of reconstruction kernels, a first reconstruction kernel based on the NPS of the uniform region of the plurality of regions. The method includes generating a harmonized CT image based on the first reconstruction kernel and the CT image.

A system for low-count quantitative single-photon emission computed tomography (LC-QSPECT) is provided. The system is programmed to a) store a computer tomography (CT) scan of a subject being examining including a plurality of defined volumes of interest (VOIs) of the subject being examined; b) model a system matrix based on the stored CT, wherein the model describes the probability that photons emitted from each of the defined VOIs are detected in different projection bins, wherein a plurality of projection bins are defined around the subject and the defined VOIs; c) adjust the model with analysis of stray-radiation noise around the subject; d) detect, by the one or more sensors, one or more photons being emitted by an alpha-particle-emitting isotope; and e) execute the adjusted model with the one or more detected photons as inputs to determine a source VOI of the detected photons.

Embodiments determine the density of materials and objects imaged using computed tomography (CT). An embodiment co-images a material of unknown density with a calibrant of known density. The digitally reconstructed CT images are segmented into the unknown material phase and the known material phases of the calibrant. Image intensity histograms of the unknown material are obtained. Using the known calibrant densities, an intensity-density mapping is calculated to determine the unknown material density based on its measured CT intensity. The method described herein utilizes a novel deconvolution of the intensity histograms to correct for CT imaging artifacts that impact the material phase intensities. The density of micronized materials and features such as particles, microspheres, porosity, and multiphase composites which are difficult to measure with conventional techniques can be measured using the embodiments. The design of custom sample holders for co-imaging a material with the calibrant and uncertainty estimation are also described.

According to the shell module and the electronic computed tomography equipment, the shell module comprises a shell fixing part, a shell inclined part, a rack and a connecting mechanism, the shell inclined part is independently arranged relative to the shell fixing part, and a gap is formed between the shell inclined part and the shell fixing part; the rack is provided with a fixed mounting part, and the fixed mounting part is arranged in the shell fixing part and detachably connected with the shell fixing part through a connecting mechanism; the connecting mechanism comprises an unlocking piece, at least part of the unlocking piece is arranged in the area where the gap is located, and when the unlocking piece is stressed, locking between the shell fixing part and the fixed installation part can be relieved. Therefore, the connecting mechanism can be integrally accommodated in the shell module, so that the arrangement of the connecting mechanism does not influence the appearance surface of the shell module so as to ensure the neatness and attractiveness of the appearance, and the subsequent disassembly of the shell fixing part is also facilitated.

1. The name of the design product: head clamp for cone beam computed tomography equipment. 2. The use of the design product: belongs to the field of medical devices, used on oral cone beam computed tomography (CBCT) equipment, used to prevent the patient's head from shaking when scanning images. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

Systems and methods are disclosed for image processing of cone beam computed tomography (CBCT) image data, relating to radiotherapy plans and treatments. Example operations for training a predictive regression model include obtaining a reference medical image of an anatomical region (e.g., from a reference CT image); generating a change image providing changes in the representation of the anatomical region (e.g., from the deformation or geometric transformation); identifying, for each of the plurality of varying images, a projection viewpoint (e.g., a projection capture angle from the CBCT projection space); generating a CBCT projection set and a simulation aspect set of the corresponding CBCT projection at each projection viewpoint; and training an algorithm in the regression model by using the corresponding CBCT projection set and the simulation aspect set of the CBCT projection. Corresponding operations for use of the regression model, including in radiotherapy treatment, are also disclosed.