182results about "Reconstruction from projection" patented technology

Technology is described for handling truncated data in iterative reconstruction. A method comprises iterating on a volume of an object including a non-truncated part based on image data and at least one truncated part representing deficiently imaged data. The volume is represented by voxels. The iterating includes regularizing the non-truncated part of the volume using a first regularizer, and regularizing the truncated part of the volume using a second regularizer different from the first regularizer.

Systems and methods for fast reconstruction of motion-resolved magnetic resonance images using space-time-coil convolutional networks are disclosed. The system can receive a plurality of k-space data sets. The system can detect a motion signal therefrom. The system can classify the k-space data sets according to states of the motion signals. The system can resolve the k-space data set to Euclidean space images. The system can resolve the Euclidean space images to a combined Euclidian space image. For example, the system can use a convolutional network that exploits spatial, temporal and coil correlations without k-space data consistency to minimize computation time.

A method comprising receiving a plurality of images from a multi-modal imaging system; generating a plurality of filtered measurements by performing multi-modal spectral fusion of the plurality of images; and generating, using a physics-informed neural network (PINN) trained based on one or more physical principles associated with X-ray attenuation or scattering, a reconstructed object image based on the plurality of filtered measurements, wherein generating the reconstructed object image comprises (i) generating, using the PINN, a system matrix for an X-ray imaging forward model by refining one or more coefficients of the system matrix based on a physics-informed loss function, and (ii) generating, using the X-ray imaging forward model and based on the plurality of filtered measurements, the reconstructed object image.

A method for generating a motion-corrected MR image dataset of a subject includes: acquiring k-space data of an MR image of a subject in an imaging sequence; acquiring at least two low-resolution scout images of the subject interleaved with the k-space data of the imaging sequence; comparing the scout images with one another in order to detect and / or to estimate subject motion between the scout images; and reconstructing a motion-corrected MR image dataset from the k-space data acquired in the imaging sequence. The reconstruction process includes: estimating the motion trajectory of the subject by comparing the k-space data with at least one of the low-resolution scout images; and estimating the motion-corrected image dataset using the estimated motion trajectory, wherein the estimations involve minimizing the data consistency error between the acquired k-space data and a forward model described by an encoding operator.

A method for training an algorithm for machine learning for a correction of recordings obtained by an imaging apparatus. A number of output images are recorded. Moreover a smaller number of first, high-quality scattered radiation images are simulated from the output images. A corresponding number of second, low-quality scattered radiation images is further simulated, wherein the simulation is undertaken with a number of photons reduced by at least an order of magnitude. The algorithm is trained with the second, low-quality scattered radiation images as input data and the first, high-quality scattered radiation images as output data.

Disclosed are methods, systems, and other implementations, including a unified complex-valued deep learning framework (AFT-Net), which determines the k-space domain to image domain mapping for MRI reconstruction and allows incorporation of existing deep learning models. Embodiments include a computer-implemented method for reconstructing images that includes obtaining resonance (MR) k-space data resulting from a scan performed by an MRI scanner on tissue of a patient, with the MR k-space data including complex-valued data, and processing, by a complex-valued machine learning image reconstruction system, the complex-valued data of the MR k-space data to generate image data representing features of the MR k-space data. The processing may include performing data filtering operations, by one or more machine learning filter blocks implemented according to a CU-Net architecture realized using one or more convolutional neural networks (CNN) configured for complex data processing, on data that is based on the k-space data.

A system for inverse scattering image reconstruction using implicit neural representations (INR) is provided. The system comprises a transmitter control module, a receiver acquisition module, a random spatial sampling module, a permittivity representation module implemented using a first multilayer perceptron (MLP), an induced current representation module implemented using a second MLP, a forward simulation module, a loss computation module, and an optimization module. The system is configured to emit electromagnetic signal data toward a target object, collect scattered signal data, simulate forward electromagnetic propagation, and iteratively update the MLP parameters using loss feedback. Upon convergence, the system outputs a spatial distribution of relative permittivity values to reconstruct the internal structure of the target object.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for machine learning image reconstruction. In some implementations, first input data representing the image of the one or more internal structures generated using a first imaging device is provided as input to a first machine learning model having one or more fully-connected layers. First output data generated by the first machine learning model is obtained and the first output data together with second input data representing a second image of the one or more internal structures generated using a second imaging device is provided to a second machine learning model having one or more convolutional layers. Second output data generated by the second machine learning model is obtained and used to generate rendering data that, when processed by a computing device, causes the computing device to output a reconstructed image.

An apparatus includes a memory to store first volumetric image data representing a first volumetric image of an anatomical region having a volume of interest (VOI). The first volumetric image includes several first voxels at first locations in the first volumetric image. The first volumetric image data includes first uncertainty values corresponding to the several first voxels. The first uncertainty values represent probabilities that the corresponding plurality of first voxels are part of the VOI. The memory is to store second volumetric image data representing a second volumetric image of the anatomical region. The apparatus includes a processing device operatively coupled to the memory. The processing device is to determine a deformation field to map the several first voxels to second locations in the second volumetric image. The deformation field is based in part on the first uncertainty values.

A medical image processing apparatus according to one embodiment includes processing circuitry. The processing circuitry acquires a plurality of pieces of energy bin data that are generated based on execution of photon counting CT scan. The processing circuitry reconstructs a plurality of energy band images based on the plurality of pieces of energy bin data. The processing circuitry generates, based on the plurality of energy band images, a three-dimensional histogram based on a plurality of pixel values and a plurality of energy bands included in the plurality of energy band images.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific organs and / or tissue. In certain embodiments, the accurate identification of one or more such volumes can be used to determine quantitative metrics that measure uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

A computer-implemented method for machine-learning a function configured to take as input a plurality of aligned images of a same patient and each of a different modality among a predetermined set of medical-imaging modalities, and to calculate a fused image. The method includes obtaining a dataset including, for each patient of a plurality of patients and for each modality of a respective at least part of the predetermined set, a respective image, the respective images for a patient being aligned; and training the function based on the dataset. This forms an improved solution for medical imaging.

Various methods and systems are provided for automatic quality control of image reconstruction. In one example, a method comprises obtaining medical image data, reconstructing the medical image data with a baseline reconstruction algorithm to generate one or more baseline reconstruction images and an enhanced reconstruction algorithm to generate one or more enhanced reconstruction images, detecting and localizing a set of features of interest within the one or more baseline reconstruction images, determining image values for each of the features of interest, comparing image values of the one or more baseline reconstruction images to corresponding image values of the one or more enhanced reconstruction images to determine one or more statistical characteristics, comparing the one or more statistical characteristics to predetermined criteria to determine deviations, and automatically modifying one or more parameters of the enhanced reconstruction algorithm based on the deviations.

Systems and methods for training a machine-learning model to generate denoised and dealiased image data are provided. The present disclosure provides techniques for training a machine-learning (ML) model to generate denoised and dealiased imaging data. A method includes (1) training a first ML model using a first training dataset comprising first image data to obtain a second ML model; and (2) training (a) the second ML model or (b) a third ML model using a second training dataset to obtain a fourth ML model. The second training dataset includes (i) the first image data and (ii) training image data obtained by applying at least one of the second ML model or the third ML model to second image data. The denoising and dealiasing ML model may be either the fourth ML model or derived from the fourth ML model.

Systems and methods for reconstruction for a medical imaging system. A scaling factor is used during the reconstruction process to adjust a step size of a gradient update. The adjustment of the step size of the gradient provides the ability to adjust a level of denoising by the reconstruction process.

A method for generating a Birds-Eye-View (BEV) space feature map includes obtaining sensor calibration data related to one or more sensors of a vehicle; generating, based on the sensor calibration data, a list of sample positions within a perspective space of the one or more sensors that correspond to a location within Birds-Eye-View (BEV) space; projecting, based on the list of sample positions, the BEV space onto the perspective space of the one or more sensors using variable sample density; and generating a BEV space feature map using the BEV space projected onto the perspective space of the one or more sensors.

This application describes a method and system for medical 3D imaging reconstruction based on limited-angle 2D image acquisition, which only requires 30˜50% of the data required by traditional scanner-based 3D reconstruction. This approach significantly reduces the radiation dose requirement and improves clinical efficiency. An example method includes: capturing a plurality of 2-dimensional (2D) projections of a target that cover a limited angle of the target; calibrating the plurality of 2D projections of the target to obtain a plurality of calibrated 2D projections; generating a sinogram based on the plurality of calibrated 2D projections; inputting the sinogram into a dual-domain neural network to obtain a 3D volumetric image of the target that is geometrically corrected and calibrated.

A method, system, and device for ultra-low-dose imaging of oral cone-beam computed tomography (CBCT) are provided. The method encompasses high-dose oral CBCT image data acquisition, data segmentation processing, three-dimensional (3D) reconstruction processing, model training, image data input, image enhancement, and image data output. Specifically, the process involves performing 3D reconstruction on 2D projection data of oral CBCT acquired in ultra-low-dose mode to obtain ultra-low-dose oral CBCT 3D reconstructed image data. This ultra-low-dose oral CBCT 3D reconstructed image data is then input into the oral CBCT ultra-low-dose imaging enhancement network model, from which high-quality oral CBCT image data is output for subsequent clinical diagnosis and treatment processes. This method significantly reduces the radiation dose of imaging while ensuring imaging quality, accelerates imaging speed, and enhances the safety of oral CBCT imaging, holding broad clinical application prospects.

Described herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and a motion estimating neural network (122, 700, 800, 900, 1000) configured for outputting trajectory data (130) in response to receiving a trial motion trajectory (128) as input. The execution of the machine executable instructions causes a computational system (104) to: receive (200) measured k-space data (124) descriptive of a subject (318); perform (202) motion estimation of the subject between the sequence of discrete acquisitions by solving an optimization problem to determine a calculated motion trajectory of the subject in the predefined coordinate system, wherein the optimization problem is modified using the trajectory data; and reconstruct (204) a final motion corrected magnetic resonance image (136) from the measured k-space data and the calculated motion trajectory in the predefined coordinate system.

Systems, methods, and devices for generating a corrected image are provided. A first robotic arm may be configured to orient a source at a first pose and a second robotic arm may be configured to orient a detector at a plurality of second poses. An image dataset may be received from the detector at each of the plurality of second poses to yield a plurality of image datasets. The plurality of datasets may comprise an initial image having a scatter effect. The plurality of image datasets may be saved. A scatter correction may be determined and configured to correct the scatter effect. The correction may be applied to the initial image to correct the scatter effect.

The present technology relates to an imaging system. The imaging system can comprise at least one processor configured to apply a projection precomputation algorithm and an x-ray tomography image reconstruction system. The projection precomputation algorithm can be configured to: generate a projection operator matrix that can be used to calculate a plurality of voxels from a plurality of projection measurements before the plurality of projection measurements is acquired and store the projection operator matrix in memory. The projection operator matrix can be at least one of: a compressed matrix, a multi-iteration projection operator matrix, and a combination thereof. The x-ray tomography image reconstruction system can be configured to apply the projection operator matrix to generate a reconstructed three-dimensional image of at least an internal portion of a selected object under a surface of the selected object when the plurality of projection measurements is acquired.

The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and / or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

A medical system 100 is disclosed that includes a memory 110 that stores machine executable instructions 120. The memory 110 further stores a first machine learning module 122 that is trained to output a saliency map 126 as an output in response to receiving a medical image 124 as an input. The saliency map 126 predicts a distribution of a user's attention on the medical image 124. The medical system 100 further includes a computing system 104. Upon execution of the machine executable instructions 120, the computing system 104 receives the medical image 124. The medical image 124 is provided as an input to the trained first machine learning module 122. In response to providing the medical image 124, a saliency map 126 of the medical image 124 is received as an output from the trained first machine learning module 122. The saliency map 126 predicts a distribution of a user's attention on the medical image 124. The saliency map 126 of the medical image 124 is provided.

The invention relates to a control system for controlling optical coherence tomography imaging means for imaging a subject, the control system being configured to perform the following steps of an imaging process: receiving (212) a scan data set from the subject being acquired by means of optical coherence tomography, the scan data set including one or several spectra (270), performing (214) data processing on the spectrum or on each of the several spectra of the scan data set (122), including per spectrum: determining (216) a scaling factor (274) for the spectrum (270, 370, 372, 374), scaling (218) a baseline spectrum (272) with a scaling factor (274), and removing (220) the scaled baseline spectrum (276) from the spectrum (270); and providing (224) a baseline corrected image data set of the subject for an image of the subject to be displayed, to an optical coherence tomography imaging system and to a corresponding method.

A method, system, processing circuitry, and computer program product for providing initial motion correction in magnetic resonant imaging (MRI) data that enables additional image correction to be performed on subsequently processed MRI data in the same imaging set. One such method receives k-space data including a first set of motion corrupted k-space data and a second set of k-space data (different than the first set); generates motion correction data based on the first set of motion corrupted k-space data; and generates an image based on the second set of undersampled k-space data and the motion correction data.

Images are reconstructed from data acquired using an ultra-fast-pitch acquisition with a CT system. As an example, an ultra-fast-pitch acquisition mode in single-source helical CT (≥1.5) can be used to acquire data. A trained machine learning algorithm, such as a neural network, is used to reconstruct images in which artifacts associated with insufficient data acquired in the ultra-fast-pitch mode are reduced. An example neural network can include customized functional modules using both local and non-local operators, as well as the z-coordinate of each image, to effectively suppress the location- and structure-dependent artifacts induced by the ultra-fast-pitch mode. The machine learning algorithm can be trained using a customized loss function that involves image-gradient-correlation loss and feature reconstruction loss.

An imaging device and method of eliminating electromagnetic interference of magnetic resonance are provided. The imaging device includes: means for acquiring magnetic resonance imaging signals, acquiring a first electromagnetic interference signal in an electromagnetic interference affected environment, and acquiring second electromagnetic interference signals in the electromagnetic interference affected environment; means for superposing the magnetic resonance imaging signals with the first electromagnetic interference signal to obtain interfered imaging signals, respectively, taking the interfered imaging signals and corresponding second electromagnetic interference signals as input, taking corresponding magnetic resonance imaging signals as output, and training and obtaining an electromagnetic interference eliminating model; means for inputting a real-time magnetic resonance imaging signal and a real-time electromagnetic interference signal into the electromagnetic interference eliminating model, to obtain a predicted magnetic resonance imaging signal that eliminates electromagnetic interference; means for performing image reconstruction on the predicted magnetic resonance imaging signal to obtain a magnetic resonance image.

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.

The present disclosure relates to a computer-implemented method for automatically generating and displaying a 2D image derived from 3D image data, the 2D image representing a portion of a patient's maxillofacial anatomy, the method comprising: receiving 3D image data including a voxel image volume representing the maxillofacial anatomy, the voxel image volume including the patient's teeth, each voxel of the voxel image volume being associated with a radiation intensity value; detecting one or more anatomical landmarks in the voxel image volume using a first artificial neural network; determining a crown center position for each tooth of a plurality of teeth included in the voxel image volume using the detected anatomical landmarks as reference points using a second artificial neural network; and generating a 2D image as a re-slice image from the 3D image data based on the at least one crown center position.

A medical image processing apparatus includes processing circuitry to receive radiation image data for each of a plurality of different channels, wherein the radiation image data for all of the plurality of channels represents a same anatomical region of a same subject; and, for each of a plurality of positions represented in the radiation image data: estimate, based on data values for the position in each of the plurality of channels, material probabilities which indicate a respective probability of each of a plurality of materials existing at the position; and specify a value for at least one rendering parameter at the position based on the material probabilities.