63 results about "Computed tomographic" patented technology

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).

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.

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.

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.

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).

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 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.

A computer-implemented method includes obtaining an image (1202) of one or more anatomical blood vessels of a subject, wherein the image is a segmentation of the one or more anatomical blood vessels from image data acquired using a three-dimensional medical imaging modality. The computer-implemented method also includes obtaining one or more segments of a plaque in the one or more anatomical blood vessels of the subject (1204). The computer-implemented method also includes obtaining fractional flow reserve (FFR) information of the one or more anatomical blood vessels of the subject (1206). The computer-implemented method further includes generating a composite rendering including the image of the one or more anatomical blood vessels, at least one of the one or more segments of the plaque, and the FFR information (1208). The computer-implemented method also includes displaying the composite rendering in a single viewport.

Various examples are provided related to predicting perfusion images from non-contrast scans. In one example, a method for predicting perfusion images includes generating perfusion maps of an organ of a subject from non-contrast computed tomography (NCCT) slices of the organ; processing the perfusion maps based upon weights determined by a Physicians-in-the-Loop (PILO) module; and generating synthetic computed tomography perfusion (CTP) maps from the processed perfusion maps, the synthetic CTP maps generated by deep learning-based multimodal image translation. In another example, a system includes at least one computing device that can generate prefusion maps of an organ from NCCT slices; process the perfusion maps based upon weights determined by a PILO module; and generate synthetic CTP maps from the processed perfusion maps using deep learning-based multimodal image translation. The CTP maps can be rendered for display to a user.

This invention provides a dental treatment support system, comprising: a measurement point information acquisition unit that acquires measurement point information representing measurement points for orthodontic diagnosis based on images obtained from X-ray imaging of a subject's head, magnetic resonance imaging of a subject's head, or computed tomography imaging of a subject's head; a position information acquisition unit that acquires imaging position information representing the position of the subject's anterior teeth, molars, or jawbone at the time of imaging based on the head measurement point information; a planning position information acquisition unit that acquires planning position information representing the planned position of the teeth or jawbone to be moved; and a movement information generation unit that generates movement information representing the amount of movement of the planned anterior teeth, molars, or jawbone to be moved from the position before movement, based on the measurement point information, the imaging position information, and the planning position information. This invention provides a more efficient and effective process in orthodontic treatment.

This disclosure relates to an X-ray computed tomography (CT) apparatus, an information processing system, an information processing method, and a non-transient computer-readable medium. The X-ray computed tomography (CT) apparatus includes: an X-ray tube configured to irradiate X-rays; a detector configured to detect X-rays irradiated by the X-ray tube and penetrating a subject; a reconstruction processing unit configured to reconstruct imaging data output from the detector and generate CT image data and material breakdown image data; a first inference unit configured to perform inference on the material breakdown image data; a second inference unit configured to perform inference on the CT image data; a third inference unit configured to perform inference on the composite image data; a selection unit configured to select the inference result of the third inference unit if the inference result of the first inference unit differs from the inference result of the second inference unit; and a display control unit configured to display the selected inference result.

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.

One or more systems, methods, and / or computer program products provided herein for use relate to generating a CT image from data collected by an X-ray detector comprising an alternating or asymmetric X-ray detector geometry, wherein the alternating or asymmetric X-ray detector geometry can provide a mechanism to increase scan coverage during CT imaging to achieve faster scanning. Embodiments described herein provide a framework, the framework generates, by employing asymmetric X-ray detector geometry in combination with pre-patient collimator, pre-patient filter, and post-patient filter geometry and image reconstruction techniques to optimally utilize measurements acquired from the asymmetric X-ray detector geometry, image quality parameters, such as temporal resolution, etc. The invention relates to a CT image with spectral resolution or spatial resolution.

In chest imaging it is important to only image the lungs without exposing the thyroid gland or the abdominal organs, so the width of the X-ray beam is restricted so that the projection area matches the size of the lungs of each individual patient. For 2D X-ray systems and traditional computed tomography systems there is only one X-ray emission point so it is easy to use a conventional collimator box. However, when using stationary multi-emitter arrays, using lots of conventional collimator boxes would get in the way of neighbouring X-ray beams. The present invention uses a pair of sliding plates 41, 43 with matching holes 45 therein, such that apertures 47 of variable size are formed for each X-ray emitter.

The invention relates to a method for producing a calibration phantom to support diagnosis in computed tomography and an analysis method, which continuously remains inside a CT device, provides calibration in all scans by fully covering a wide spectrum of the HU scale, enables inter-device data standardisation, and has data processing methods that may be developed within this scope, thereby providing different application areas.

A method and apparatus is provided that uses a deep learning (DL) network to reduce noise and artifacts in reconstructed medical images, such as images generated using computed tomography, positron emission tomography, and magnetic resonance imaging. The DL network can operate either on pre-reconstruction data or on a reconstructed image. The DL network can be an artificial neural network or a convolutional neural network (e.g., using a three-channel volumetric kernel architecture). Different neural networks can be trained depending on the noise level, scanning protocol, or the anatomic, diagnostic or clinical objective of the reconstructed image (e.g., by partitioning the training data into noise-level range and training respective DL networks for each range). Further, the DL networks can be trained to mitigate artifacts, such as the cone-beam artifact.

A computer-implemented method of reducing noise in a computed tomography (CT) image slice (1101.. i,) passing through an anatomical region is provided. The method comprises: receiving CT data (120) representative of the image slices (1101.. i,); adjusting an amount of noise in the image slice (1101.. i,) to provide a denoised image slice () having a similar amount of noise; and outputting the de-noised image slice ().