1386results about "Reconstruction from projection" patented technology

A computer-implemented method of providing a temporal sequence of 3D angiographic images (110) representing a flow of a contrast agent through a region of interest (120), is provided. The method includes: inputting (S130) volumetric image data (130a, 130b), and a temporal sequence of 2D angiographic images (140) into a neural network (NN1); and generating (S140) the predicted temporal sequence of 3D angiographic images (110) representing the flow of the contrast agent through the region of interest (120) in response to the inputting.

Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

A method of anonymizing medical images includes acquiring or receiving a first medical image; separating an identification area containing anatomical structure information and a de-identification area containing a skin area in the first medical image; generating a de-identified image including a plurality of pattern areas with a predetermined size for the de-identification area; and generating a second medical image by replacing the de-identification area with the de-identified image.

Disclosed are a method of anonymizing medical images, the method comprising: acquiring or receiving a first medical image; separating an identification area containing anatomical structure information and a de-identification area containing a skin area in the first medical image; generating a de-identified image including a plurality of pattern areas with a predetermined size for the de-identification area; and generating a second medical image by replacing the de-identification area with the de-identified image.

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

Computer-implemented methods and systems are provided for quantitative hemodynamic flow analysis, which involves retrieving patient specific image data. A 3D reconstruction of a vessel of interest can be created from the patient specific image data. Geometric information can be extracted from the 3D reconstruction. A lesion position can be determined. Patient specific data can be obtained. Hemodynamic results can be calculated based on the geometric information, the lesion position and the patient specific data.

An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (“FFR”) by calculating a ratio of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.

Denoised magnetic resonance images are generated using a two-step process. An initial set of images is first denoised on a per channel basis using a locally low-rank-based denoising technique. The denoised coil channel images are transformed back into k-space and the denoised k-space data are then applied to a nonlinear image reconstruction. In some instances, the nonlinear image reconstruction can be implemented using a trained neural network. The neural network may be trained using a self-supervised learning technique.

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

A medical image processing device of an embodiment includes first and second image acquirers, a generator, and a calculator. The first image acquirer acquires a first fluoroscopic image of a patient. The second image acquirer acquires a second fluoroscopic image according to radiation of the patient at a time point different from that of acquisition of the first image using a detector. The generator generates a reconstructed image by reproducing the second image from the first image virtually arranged in a three-dimensional space on the basis of a position of the detector in the three-dimensional space. The calculator obtains a suitable position on the first image in the three-dimensional space on the basis of a degree of similarity between the second image and the reconstructed image. The generator generates the reconstructed image for use in the calculator and has a range larger than a range corresponding to the second image.

Computer-implemented methods and systems are provided for quantitative hemodynamic flow analysis, which involves retrieving patient specific image data. A 3D reconstruction of a vessel of interest can be created from the patient specific image data. Geometric information can be extracted from the 3D reconstruction. A lesion position can be determined. Patient specific data can be obtained. Hemodynamic results can be calculated based on the geometric information, the lesion position and the patient specific data.

A system, method, and apparatus for displaying a fused reconstructed image with a multidimensional image are disclosed. An example imaging system receives a selection corresponding to a portion of a displayed multidimensional visualization of a surgical site. At the selected portion of the multidimensional visualization, the imaging system displays a portion of a three-dimensional image which corresponds to the selected multidimensional visualization such that the displayed portion of the at least one of the three-dimensional image or model is fused with the displayed multidimensional visualization.

Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

A system and method for using augmented reality device for use during a surgical procedure are described. A system may include an augmented reality device to present a virtual indication, such as a virtual indication of a surgical instrument, a force vector, a direction, or the like. The augmented reality device may present a virtual aspect of a procedure, such as a virtual animation of a step of a procedure. The augmented reality device may present a virtual object, indication, aspect, etc., within a surgical field while permitting the surgical field or aspects of the surgical field to be viewed through the augmented reality display (e.g., presenting virtual objects mixed with real objects).

The present disclosure relates to a cone beam computed tomography (CT) imaging system, an image processing method, and a device therefor. An image processing method performed by a cone beam computed tomography (CT) imaging apparatus including a source and a detector that rotate around an object may include: obtaining a plurality of projection images projected on the detector for the object; obtaining a correction projection matrix; and reconstructing a three-dimensional image by back-projecting the plurality of projection images based on the correction projection matrix.

Described here are systems and methods for generating microvessel images from image data acquired with an ultrasound system while analyzing the image data in realtime, or retrospectively, to generate a performance descriptor that can be used to assess data quality and / or motion correction quality; to adaptively suppress noise in the data; or both.

A calibration assembly, including: a base; and a plurality of calibration wires dispersedly connected to the base. An absorption capacity of the calibration wire for X rays is greater than that of the base for X rays. Through a specific structure design of a plurality of calibration wires in the calibration assembly, the calibration wires are dispersedly connected to the base, and taking advantage of the characteristics that the absorption capacity of the calibration wire for X rays is greater than that of the base for X rays, the calibration wires are applied to the calibration phantom, and the calibration phantom is scanned in the scanning system. By continuously adjusting the geometric parameter values in the imaging method, the optimal geometric parameter values that are closest to the real scanning system structure may be obtained, thereby improving the imaging effect of the scanning system.

A method, a system, and a computer-readable medium for motion detection and correction of a medical image are provided, including segmenting a medical image about a target organ into a plurality of frame images in accordance with list mode data, and analyzing a plurality of centers of mass of a volume of interest in the plurality of frame images to compute a motion curve of the target organ during scanning, and executing a reconstruction optimization of the medical image based on the motion curve, and therefore being able to take into account the movement of a human organ or a lesion without installing an additional monitoring device in order to perform motion detection and correction of the medical image.

The present disclosure provides various systems and methods for magnetic resonance imaging. In one aspect, a method for magnetic resonance imaging can include receiving k-space data sets acquired by radiofrequency (RF) coils. Each of the k-space data sets can correspond to a different one of the RF coils. Each of the k-space data sets can be truncated and / or under sampled. The method can further include generating partial images of a field of view based on the k-space data sets and generating an initial image based on the partial images. The initial image can be full image of the field of view. The method can further include applying an iterative image reconstruction technique to generate an updated image based on the initial image.

The invention relates to a method for reconstructing a 3D medical image of a region of interest from a set of 2D X-ray images acquired by an X-ray imaging system comprising an X-ray source(S) and an image detector (D), comprising the steps of: —computing n paths of the X-ray imaging system, n being an integer greater than or equal to 2, said paths being adapted to acquire n respective subsets of 2D X-ray images together forming the set of 2D X-ray images, each image (IA, IB, IC) of a subset defining a respective cone of projection (CA, CB, CC) of the region of interest onto the image detector, such that, for a same position of the X-ray source(S) along the n paths, the cone of projection of an image of any subset is contiguous to the cone of projection of an image of at least one other subset, wherein computing said n paths comprises: —placing a virtual isocenter (O) of the X-ray imaging system at a given position or set of positions relative to a center (C) of the region of interest, and —defining, for each position of the X-ray source along the n paths, the n cones of projection such that the center of a height of an enlarged cone formed by the union of the n cones of projection is said virtual isocenter (O): —implementing the computed n paths to acquire the n subsets of images: —reconstructing the 3D medical image by intersecting the cones of projection defined by each image of the n subsets.

A computer-implemented method for image reconstruction using a learned iterative scheme, the method may comprise processing measured dual space data to reduce the resolution, initiating a reconstructed image in the primal space, determining dual and primal parameters defined in the dual and primal spaces, and identifying primal patches and dual patches. The method may further include iteratively updating the reconstructed image by applying a dual-space learned invertible operator to the dual parameters, applying a primal-space learned invertible operator to the primal parameters, updating the reconstructed image based on the updated primal parameters and, when necessary, based on upsampled primal parameters, upsampled dual parameters, and the upsampled reconstructed image. The method may also include outputting the updated reconstructed image.

A system and method for reducing scan time of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging include acquiring a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data includes a plurality of parallel phase encoding lines sampled in a phase encoding order. Each blade of the plurality of blades of k-space data is undersampled. The system and method include utilizing a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades. The system and method include utilizing a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.

A pharmaceutical hyperspectral reconstruction method based on a coded aperture snapshot spectral imaging (CASSI) system includes: collecting and processing original pharmaceutical hyperspectral images to obtain augmented pharmaceutical hyperspectral images; performing simulated spatial encoding on the augmented pharmaceutical hyperspectral images to obtain encoded measurement images; performing spectral inverse shift on the encoded measurement images, then performing inverse encoding to obtain inversely encoded three-dimensional hyperspectral images, using the augmented pharmaceutical hyperspectral images as target images, and constructing a training set and a testing set according to the inversely encoded three-dimensional hyperspectral images and the target images; constructing a deep symmetric neural reconstruction network, and training and testing the deep symmetric neural reconstruction network; and deploying a tested deep symmetric neural reconstruction network onto the CASSI system, real-time collecting pharmaceutical measurement images using the snapshot coded imaging system, and performing computational reconstruction on the pharmaceutical measurement images to obtain reconstructed three-dimensional hyperspectral images.

A system (100) for reconstructing digital subtraction angiography, DSA, images (110) representing a region of interest (120) in an object (130), is provided. The system includes one or more processors (140) configured to: receive (S110) cone beam projection data (150) acquired during a portion of a revolution of a source-detector arrangement (160s, 160d) of a cone beam X-ray imaging system (170) around the object (130); reconstruct (S120) the cone beam projection data (150) acquired from one or more orientations within a first angular range (Dq1), into a 3D mask image (170) representing the region of interest (120); and reconstruct (S130) the cone beam projection data (150) acquired from one or more orientations within a second angular range (Dq2), into a temporal sequence of 3D DSA images (110) representing the region of interest (120), based on the reconstructed 3D mask image (170).

A medical image processing method includes obtaining a first set of projection data by performing, with a first CT apparatus including a first detector with a first pixel size, a first CT scan of an object in a first imaging region of the first detector; obtaining a first CT image with a first resolution by reconstructing the first set of projection data; obtaining a processed CT image with a resolution higher than the first resolution by applying a machine-learning model for resolution enhancement to the first CT image; and displaying the processed CT image or outputting the processed CT image for analysis. The machine-learning model is obtained by training using a second CT image based on a second set of projection data acquired by a second CT scan of the object in a second imaging region with a second CT apparatus including a second detector with a second pixel size.

Systems, methods, and devices for medical imaging are disclosed herein. In some embodiments, a method for imaging an anatomic region includes receiving, from a detector carried by an imaging arm of an x-ray imaging apparatus, a plurality of images of the anatomic region. The images can be obtained during manual rotation of the imaging arm. The imaging arm can be stabilized by a shim structure during the manual rotation. The method can also include receiving, from at least one sensor coupled to the imaging arm, pose data of the imaging arm during the manual rotation. The method can further include generating, based on the images and the pose data, a 3D representation of the anatomic region.

Described herein are systems, methods, and instrumentalities associated with using a multi-layer perceptron (MLP) neural network to process medical images of an anatomical structure. The processing may include padding an input image in accordance with the training of the MLP neural network, splitting the input image (e.g., the padded input image) into patches of a same size, and processing the patches through the MLP neural network over one or more iterations. During an iteration of the processing, the patches may be processed separately and re-combined into an intermediate image before the intermediate image is shifted to concatenate portions of the image that are derived from different patches. This way, global features of the anatomical structure may be learned and used to improve the quality of the image generated by the MLP neural network, without incurring significant computation or memory costs.

In some embodiments, the present invention comprises a new metrology device (apparatus) comprising a camera, an optical imaging system, and a reference target for the non-destructive evaluation of samples as well as methods and systems thereto. In some embodiments, the device includes a methodology to measure vision rays that enter the optical system at a specific angle by analyzing the patterns of a reference target at different locations. This apparatus also allows measuring the vision rays deflected by samples placed in front of the optical system. The deflection may have occurred through refraction by transparent samples or by reflection of reflective samples. The obtained vision rays provide information on the geometric wavefront, and hence, the sample surface.