89 results about "Computed tomographic" patented technology

A computed tomography imaging system (102) includes a gantry (104). The computed tomography imaging system also includes a rotating frame 106 rotatably supported in the gantry. The rotating frame comprises an X-ray source (114) and an X-ray radiation sensitive detector (120), the X-ray source being configured to emit X-ray radiation traversing an examination region, the X-ray radiation sensitive detector is disposed opposite the X-ray source across the examination region and is configured to detect X-ray radiation traversing the examination region and to generate a signal indicative of the detected X-ray radiation. The computed tomography imaging system also includes at least one component (202, 214) of the gantry or rotating frame that generates audible noise. The computed tomography imaging system also includes an audible noise reducer (128) configured to reduce the audible noise. The audible noise reducer includes a resonator that is tuned to a first frequency of the audible noise.

The invention relates to a computer-implemented method of liver resection surgery planning, comprising the steps of: processing (E2) preoperative computed tomographic images (10) of a patient to generate liver, liver lesion (ss) and venous vessels segmentations; pruning the venous vessels segmentation to retain only major vessels and determining (E3) a primary hepatic zone (PHZ) from the retained major vessels; determining (E4) at least one quantitative feature from the primary hepatic zone, the liver segmentation and the liver lesion(s) segmentation; processing (E5) the determined quantitative features with a classification model to determine a liver resection complexity score for the patient.

One or more example embodiments relates to a gantry for a computed tomography device, the gantry comprising an opening forming a tunnel extending along a system axis of the gantry; a first gantry part including an X-ray source configured to produce X-ray radiation, the first gantry part annularly surrounding the opening; a second gantry part supporting the first gantry part; a linear guide, the first gantry part is movably mounted via the linear guide such that a translational movement of the first gantry part relative to the second gantry part is executable along the system axis; a threaded spindle, the threaded spindle is rotatably mounted about a spindle rotation axis via a spindle rotary bearing relative to the second gantry part, the spindle rotation axis is parallel to the system axis; a screw drive, the screw drive is configured to convert a rotational movement of the threaded spindle.

Noise-reduced images of a subject are generated from x-ray projection data acquired from the subject using a computed tomography (“CT”) system. A neural network or other machine learning algorithm is trained to receive images reconstructed from the projection data as an input and to generate an output as noise-reduced images. The neural network or other machine learning algorithm is trained using a self-training procedure, in which the training data used to train the neural network or other machine learning algorithm are generated directly from the projection data acquired from the subject using data augmentation (e.g., random rotations of the projection data and / or noise insertion).

A CT apparatus includes a plurality of imaging units, a rotation mechanism, and a CPU. The imaging unit includes a radiation source and a radiation detector. The rotation mechanism rotates the plurality of imaging units around a body axis of the subject while maintaining a disposition interval. An imaging control unit of the CPU controls operations of the plurality of imaging units and the rotation mechanism. An angular interval that is determined by a frame rate of the radiation detector and a rotation speed of the imaging unit and that defines an acquisition time of a projection image based on the radiation is the same for the plurality of imaging units. The plurality of imaging units have different phases in a rotation direction, and positions where the plurality of imaging units acquire the projection images are separated by a set angle that is less than the angular interval.

CT images from the neck, lung, cardiac, abdominal, pelvis, hip, spine or lower extremity areas are obtained and analyzed by a computer to measure trabecular bone density on each level it is available, in the spine and hip. Any bone tissue associated with vertebrae that is fractured is excluded, along with Schmorl's nodes, large blood vessels, and cortical bone, to accurately average the trabecular bone density on a scan. The density of fat, heart, and muscle tissue of a subject is used to calibrate the results. That data may be input to an absolute fracture risk model (Fracture Risk Algorithm calculator). Information such as bone density, T score, Z score, fracture risk, and identification of vertebral fractures (if present) may be determined and reported.

A method of analyzing computed tomography (CT) images comprises receiving an initial CT image of an object, identifying calcium-free regions and a calcified region in the initial CT image of the object, generating a calcium-free image patch, and applying the calcium-free image patch to the initial CT image patch to produce a final CT image. The initial CT image shows a calcium deposit and a target structure in the object. The calcified region in the initial CT image shows the calcium deposit in the object obscuring a portion of the target structure. The calcium-free regions show the remaining portions of the target structure. The calcium-free image patch corresponds to the calcified region in the initial CT image. The final CT image shows the calcium-free image patch and the calcium-free region from the initial CT image. The calcium-free image patch is generated and applied using a convolutional neural network.

State of the art mechanisms being used for achieving diagnostic-quality images under low-dose settings for general CT imaging have the disadvantages that CT images are fixed during the optimization process to generate perfusion maps, which can lead to suboptimal CT images with respect to the perfusion maps generated, although they might appear spatially smooth or denoised. The disclosure herein generally relates to Computer Tomography (CT) scanning, and, more particularly, to a method and system for CT image reconstruction. The system performs modelling an optimization problem for joint estimation of a set of structural CT images and a perfusion map, and further solves the optimization problem for the reconstruction of the CT images of a subject.

The invention relates to a detection system (100) for a computed tomography system (200). The detection system (100) comprises a first detector (110) and a second detector (120), each detector being configured for detecting X-ray radiation of the computed tomography system (200) impinging on a respective detector. The detection system further comprises a repositioning mechanism (130) configured for repositioning at least one of the first detector (110) and the second detector (120) relative to the other of the first detector (110) and the second detector (120).

A system and method for producing a computed tomography (CT) medical image includes receiving x-rays passing through an object with a photon-counting detector system, which includes a plurality of detector pixels configured to generate a photon-counting signal in response to receiving each photon of the x-rays having passed through the object. The method also includes summing a charge associated with each photon received at a given detector pixel of the plurality of pixels to generate a charge integration signal, utilizing the charge integration signal to correct a count of the photon-counting signal for pileup-induced count losses to create a corrected photon-counting signal, and reconstructing an image of the object using the corrected photon-counting signal.

Provided herein are methods, apparatuses, computer program products, and systems for one-shot computed tomography reconstruction using inverse Abel transform. One method can include obtaining a radiograph of at least one object using an X-ray scanner, wherein the at least one object has been manufactured using a manufacturing process; determining an axis of symmetry for at least a portion of the at least one object that is axisymmetric based on a type of the at least one object; generating a reconstruction image for the at least one object by applying an inverse Abel transform on the radiograph using the axis of symmetry; and providing an inspection result for the at least one object by processing the reconstruction image based on the type of the at least one object, wherein the inspection result causes a subsequent step of the manufacturing process to be performed based on the inspection result.

The present disclosure relates to representation learning for treatment response prediction of risk organs and total tumor volume. For predicting the response of radiation therapy, radiomics is used for unsupervised machine training of an encoder-decoder network to predict based on input of image data, such as computed tomography image data, and from segmentation. Then, the trained encoder is used to generate latent representations to be used as input to different classifiers or regressors for predicting the treatment response, such as one classifier for predicting the response for a risk organ and another classifier for predicting another type of response for a risk organ or predicting the response for a tumor.

Concepts are presented for assisting or improving the representation and visualization of CT data. In particular, embodiments provide a method and / or system that normalizes the HU scale of a material of interest (except for air or water) across CT data such that the HU value of the material of interest is substantially constant across multiple CT images.

Embodiments of this application provide a linear scanning CT imaging system and imaging method. The linear scanning CT imaging system includes: a support frame; a transport device mounted on the support frame, the transport device including a drive mechanism, a transmission mechanism, and a circular transport path, the drive mechanism being configured to drive the transmission mechanism to move along the circular transport path, and multiple local linear paths in the circular transport path forming multiple linear scanning segments; at least one carrier mounted on the transmission mechanism, configured to carry at least one scanned object, the carrier being able to follow the transmission mechanism to move along the circular transport path to pass through multiple linear scanning segments; multiple scanning devices, respectively disposed in the multiple linear scanning segments, with multiple X-ray sources in the multiple linear scanning segments emitting multiple X-ray beams from multiple angles relative to the scanned object; and an imaging device for generating a computed tomographic image of the scanned object based on multiple projection data detected by various detectors in the multiple linear scanning segments.

Methods and apparatus for generating a hybrid (e.g., fused) cone beam computed tomography (CBCT) scan using intraoral scan data. Methods and apparatus for more efficiently processing CBCT data are also described herein. Also described herein are methods and apparatus for enhancing dental and / or orthodontic treatment planning using hybrid CBCT and intraoral scan data, methods and apparatus for interactively presenting hybrid CBCT and intraoral scans and generating one or more treatment plans in conjunction with a user and / or a dental instrument for treating a patient according to a treatment plan that takes into account hybrid CBCT and intraoral scans.

A system and method for a lidar guided patient positioning are described. An example imaging system includes a gantry having a bore, a table to move a subject relative to the bore, a radiation source mounted on the gantry and to emit an X-ray beam, and a detector to detect the X-ray beam. The system further includes a light detection and ranging (LiDAR) scanning system to acquire data of the subject. The system further includes processing circuitry to process the LiDAR data to generate a 3D point cloud representing a topography of the subject, estimate a center of a region of interest of the subject, calculate an offset of the center of the region of interest relative to an isocenter of the gantry, and adjust a height of the table based on the offset to align the center of the region of interest with the isocenter of the gantry.

An X-ray computed tomography (CT) scanner includes a plurality of X-Ray sources and detectors mounted about an opening where scanning takes place. The X-Ray sources and detectors are arranged to oscillate back and forth in opposing first and second rotational directions about the opening, or in the same rotational direction about the opening, in order to generate a cross-sectional image of an object located within the opening.

Systems and methods include acquiring a computed tomography image of a subject, determining a linear attenuation coefficient map based on the computed tomography image, acquiring positron emission tomography (PET) data of the subject, determining a tissue image of the subject based on the PET data, determining a scattered tissue image based on the tissue image and the linear attenuation coefficient map, and outputting the scattered tissue image. A scatter-corrected tissue image is determined based on the tissue image and the scatter tissue image, and the computed tomography image and the scatter-corrected tissue image are input to a trained neural network to generate a PET image.

An X-ray computed tomography (CT) scanner includes a plurality of X-Ray sources and detectors mounted about an opening where scanning takes place. The X-Ray sources and detectors are arranged to oscillate back and forth in opposing first and second rotational directions about the opening, or in the same rotational direction about the opening, in order to generate a cross-sectional image of an object located within the opening.

Various methods and systems are provided for a sensor design in a computed tomography (CT) detector of a medical imaging system. In one embodiment, a detector array may include a post-patient collimator assembly arranged between a detector array and an x-ray source, the collimator assembly having a plurality of collimator plates aligned substantially parallel with a radial direction of an imaging system, a plurality of imaging sensors located in the detector array, and a plurality of non-imaging sensors located in the detector array. In another embodiment, a method may include correcting an imaging sensor signal based on a signal output from a non-imaging sensor.

A computed tomography (CT) system includes a scanning unit having: an x-ray emitter; a detector arranged to receive x-rays emitted from the x-ray emitter during a scanning operation; and a manipulation unit arranged to rotate an object about an axis of rotation and to move the object through a plurality of positions along a path defined by the axis of rotation between the x-ray emitter and the detector. The CT system may also include a computing device including a memory storing computer executable instructions and at least one data processor. The processor is arranged to execute the instructions, the instructions causing the processor to perform operations, the operations comprising: receiving first data characterizing the path from the scanning unit; and receiving, from the scanning unit, second data characterizing one or more x-ray images of the object at at least one of the plurality of positions; determining a compensation to be applied to the second data based on the first data; reconstructing an image of the object based on the first data, the second data, and the compensation; and providing the reconstructed image.

An X-ray computed tomography (CT) apparatus includes an X-ray tube configured to radiate X-rays; a detector configured to detect X-rays radiated by the X-ray tube and passed through a subject; a reconstruction processing unit configured to reconstruct imaging data output by the detector and generate CT image data and material decomposition image data; a first inference unit configured to perform inference on the material decomposition image data; a second inference unit configured to perform inference on the CT image data; and a display control unit configured to cause a display unit to display at least one of an inference result of the first inference unit and an inference result of the second inference unit.

A method for generating computed tomography (CT) images of a subject at a predetermined breathing state includes receiving CT image data, including slice images, for a plurality of slice locations from a plurality of free-breathing CT scans. The method further includes receiving breathing surrogate data for the subject including a breathing amplitude for the CT image data associated with each slice location, and receiving a predetermined breathing amplitude. For each slice location, the method further includes determining an amplitude for each slice image associated with the slice location based on the breathing surrogate data, and selecting one of the slice images associated with the slice location by comparing the predetermined breathing amplitude and the determined slice image amplitude for each slice image associated with the slice location. The method further includes generating a CT image at the predetermined breathing amplitude based on the selected slice image for each slice location.

Provided herein are methods, apparatuses, computer program products, and systems for one-shot computed tomography reconstruction using inverse Abel transform. One method can include obtaining a radiograph (142) of at least one object (104) using an X-ray scanner (110), wherein the at least one object (104) has been manufactured using a manufacturing process (130); determining an axis of symmetry (106) for at least a portion of the at least one object (104) that is axisymmetric based on a type of the at least one object (104); generating a reconstruction image (144) for the at least one object (104) by applying an inverse Abel transform (122) on the radiograph (142) using the axis of symmetry (106); and providing an inspection result (146) for the at least one object (104) by processing the reconstruction image (144) based on the type of the at least one object (104), wherein the inspection result (146) causes a subsequent step of the manufacturing process (130) to be performed based on the inspection result (146).

Denoising images rendered from scan data acquired by computed tomography (CT), including receiving CT scan data and generating a grid of pixels, for a first pixel channel, based at least in part on the CT scan data, each pixel having an associated radiance value. Methods include iteratively tracing a plurality of rays originating at a camera position based at least in part on radiance values of intersected pixels to produce a Monte Carlo estimate image and applying a denoising algorithm to the Monte Carlo estimate image to produce a denoised image. Methods further include determining one or more weights based at least in part on the Monte Carlo estimate image and the denoised image. Methods further include blending the Monte Carlo estimate image and the denoised image based at least in part on said one or more weights to produce a rendered image.

The invention relates to an apparatus (10) for correcting computed tomography ("CT") X-ray data acquired at high relative pitch, the apparatus comprising: an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to provide the processing unit with CT X-ray data of a body part of a person acquired at high relative pitch. The processing unit is configured to determine CT slice reconstruction data of the body part of the person without or with reduced high relative pitch operation reconstruction artifacts using a machine learning algorithm. The machine learning algorithm is trained based on CT slice reconstruction data, and wherein the CT slice reconstruction data comprises first CT slice reconstruction data with high relative pitch reconstruction artifacts, and comprises second CT slice reconstruction data without or with less or less severe high relative pitch reconstruction artifacts. The output unit is configured to output the CT slice reconstruction data of the body part of the person.

A computed tomography imaging system includes: a gantry; and a rotating frame rotatably supported in the gantry and including an X-ray source and an X-ray radiation sensitive detector. The computed tomography imaging system further includes: an inflation chamber configured to hold pressurized air; and a cooling system. The cooling system includes: a heat exchanger configured to transfer heat from a coolant cooling at least one component carried by the rotating frame; and a fan system configured to move cold air from the plenum to the heat exchanger and to move heated air away from the heat exchanger. The cooling system further includes at least one of: an intake assembly configured to provide an intake path for the cool air to reach the heat exchanger; and an exhaust assembly configured to provide an exhaust path for the heated air.

A system for combining a plurality of scans in a computed tomography (CT) imaging workflow, the system comprising: a protocol selection module configured to select two or more scan protocols for a patient; a scan combining module configured to combine scans associated with the selected scan protocol into a combined scan, where the scans are continuous or discontinuous based on the anatomical landmark; and a scan execution module configured to control the CT scanner to perform the combined scan on the patient.

The present invention relates generally to assessing abnormal patterns associated with a disease from x-ray images, and in particular to assessing abnormal patterns associated with COVID-19 (coronavirus disease 2019) from x-ray images using a machine learning network trained on DRRs (digital reconstructed radiographs) and ground truth derived from CT (computed tomography). Systems and methods of assessing a disease are provided. An input medical image of a first modality is received. A lung is segmented from the input medical image using a trained lung segmentation network, and an abnormal pattern associated with the disease is segmented from the input medical image using a trained abnormal pattern segmentation network. The trained lung segmentation network and the trained abnormal pattern segmentation network are trained based on 1) synthetic images of the first modality generated from training images of a second modality and 2) target segmentation masks for the synthetic images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lung and the segmented abnormal pattern.