Abstract: CT and PET are registered, providing a spatial alignment to be used in attenuation correction for PET reconstruction. A model for machine learning is defined to generate a deformation field. The model is trained with loss based, in part, on the attenuation corrected PET data rather than or in addition to loss based on the uncorrected PET or the generated deformation field. Due to the nature of the mapping from CT to attenuation, a separate, pre-trained network is used to form the attenuation corrected PET data in training the model.

Abstract: Embodiments of the present systems and methods may provide fissure detection in CT images, with improved performance, accuracy, and specificity. For example, in an embodiment, a method may comprise imaging, using a computed tomography system, at least one lung, to generate, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, at least one computed tomography image of the at least one lung, determining, at the computer system, at least one approximate fissure region of interest in the at least one lung image, determining, at the computer system, a more precise fissure location within the at least one region of interest, and generating an image of the lung including indication of the determined fissure location.

Abstract: A surgical system for applying an energy applicator to a target tissue and methods operating the same are disclosed. The energy applicator extends from a surgical instrument. The surgical system comprises a sensor to measure external forces and torques placed on the surgical instrument. A surgical manipulator is configured to move the energy applicator in a manual mode in response to the external forces and torques. At least one controller is configured to: model the surgical instrument and the energy applicator as a virtual rigid body having a virtual mass; calculate, using impulse modeling, constraining forces and torques to be applied to the virtual rigid body; determine total forces and torques based on the external forces and torques and the constraining forces and torques; and advance the energy applicator in the manual mode based on the total forces and torques.

Abstract: In part, the disclosure relates to an automated method of branch detection with regard to a blood vessel imaged using an intravascular modality such as OCT, IVUS, or other imaging modalities. In one embodiment, a representation of A-lines and frames generated using an intravascular imaging system is used to identify candidate branches of a blood vessel. One or more operators such as filters can be applied to remove false positives associated with other detections.

Abstract: The present disclosure provides a method for optimizing T1-weighted magnetic resonance imaging of infant brains. Firstly, T1 and PD maps of infant brains at 0-12 months old are collected to obtain average T1 and PD values of WM and GM of the infant brains, and infants are classified into three age groups according to the characteristics of WM and GM T1 values of the infant brains. Then, the theoretical signal strength of the WM and GM of the infant brains generated from a 3D T1-weighted imaging sequence is calculated through Bloch simulation, and a theoretical optimal TI scheme of each group is determined according to the simulated WM/GM contrast characteristics under different TIs. Finally, the theoretical optimal TI scheme is applied to a target infant brain according to the designated age group for 3D T1-weighted magnetic resonance imaging.

Abstract: Systems and methods for generating images with a magnetic resonance imaging (“MRI”) system, in which the images have been corrected for receive coil nonuniformities are described. Improved data acquisition schemes for fat saturation are also described.

Abstract: Embodiments include controlling a processor to access a radiological image of a region of lung tissue, where the radiological image includes a ground glass (GGO) nodule; define a tumoral region by segmenting the GGO nodule, where defining the tumoral region includes defining a tumoral boundary; define a peri-tumoral region based on the tumoral boundary; extract a set of radiomic features from the peri-tumoral region and the tumoral region; provide the set of radiomic features to a machine learning classifier trained to distinguish minimally invasive adenocarcinoma (MIA) and adenocarcinoma in situ (AIS) from invasive adenocarcinoma; receive, from the machine learning classifier, a probability that the GGO nodule is invasive adenocarcinoma, where the machine learning classifier computes the probability based on the set of radiomic features; generate a classification of the GGO nodule as MIA or AIS, or invasive adenocarcinoma, based, at least in part, on the probability; and display the classification.

Abstract: Methods, devices, and systems are provided for quantifying an extent of various pathology patterns in scanned subject images. The detection and quantification of pathology is performed automatically and unsupervised via a trained system. The methods, devices, and systems described herein generate unique dictionaries of elements based on actual image data scans to automatically identify pathology of new image data scans of subjects. The automatic detection and quantification system can detect a number of pathologies including a usual interstitial pneumonia pattern on computed tomography images, which is subject to high inter-observer variation, in the diagnosis of idiopathic pulmonary fibrosis.

Abstract: Image guided surgery includes capturing a primary modality image of a surgical field of a patient with a camera system, obtaining a secondary modality image of the surgical field registered to the primary image, generating a surgical guidance image based at least in part upon the secondary modality image, and projecting the surgical guidance image onto the patient. The surgical guidance image presents visual augmentations on or over the patient to inform a medical practitioner during a surgical procedure.

Abstract: An image processing apparatus includes projection unit configured to generate first two-dimensional image data by applying a projection process to three-dimensional image data, acquisition unit configured to acquire a second parameter related to image processing based on a first parameter related to the projection process, and image processing unit configured to generate second two-dimensional image data by applying the image processing to the first two-dimensional image data using the at least one or more second parameters.

Abstract: A region clipping method for clipping a region of a target object out of an image is performed by a computer. The method includes: referencing a memory storing three-dimensional positional information of the target object; and calculating, by using information on an imaging position for the image and orientation for the image, the region of the target object within the image from the three-dimensional positional information.

Abstract: A system for computer-aided triage includes a router, a remote computing system, and a client application. Additionally or alternatively, the system 100 can include any number of computing systems, servers, storage, lookup table, memory, and/or any other suitable components. A method for computer-aided triage includes receiving a data packet associated with a patient and taken at a first point of care; checking for a suspected condition associated with the data packet; in an event that the suspected condition is detected, determining a recipient based on the suspected condition; and transmitting information to a device associated with the recipient.

Abstract: A method for processing breast tissue image data includes processing image data of a patient's breast tissue to generate a high-dimensional grid depicting one or more high-dimensional objects in the patient's breast tissue; determining a probability or confidence of each of the one or more high-dimensional objects depicted in the high-dimensional grid; and modifying one or more aspects of at least one of the one or more high-dimensional objects based at least in part on its respective determined probability or confidence to thereby generate a lower-dimensional format version of the one or more high-dimensional objects. The method may further include displaying the lower-dimensional format version of the one or more high-dimensional objects in a synthesized image of the patient's breast tissue.

Abstract: A method for exposure control setup for a volume radiographic imaging apparatus obtains a reconstructed image volume of a subject acquired from the imaging apparatus using a set of x-ray technique settings. An image slice from the reconstructed image volume displays in at least a first rendering having a first corresponding noise factor and a second rendering having a second corresponding noise factor, different from the first noise factor. An operator instruction selects one of the at least first and second renderings and stores the corresponding noise factor for the set of x-ray technique settings. An automatic exposure control of the imaging apparatus is configured according to the stored noise factor.

Abstract: Preoperative planning techniques are described such as for hip surgery. Rather than pre-operatively planning by reorienting a model of the boundaries of the acetabulum derived from a 3D medical image, the proposed solution reorients portions of the 3D medical image itself and simulates one or more x-ray images using the reoriented 3D data. Optionally, simulated x-ray(s) of the un-modified CT scan may also be generated for comparison purposes. The user then measures acetabular metrics on the simulated x-ray(s) in order to determine the radiographic outcomes that a given magnitude and direction of reorientation would achieve. By iteratively selecting a reorientation and measuring the simulated x-ray(s), an optimal reorientation plan is determined by the user.

Abstract: Management of machine-learned data between machine-learning devices is facilitated by a processor(s) obtaining a machine-learned data set of a first device, with the machine-learned data set of the first device being categorized machine-learned information. The processor(s) determines one or more device hardware requirements to use the machine-learned data set, and based on receiving a request to provide the machine-learned data set to a second device, determines whether the second device meets the one or more device hardware requirements to use the machine-learned data set of the first device. Based on determining that the second device meets the one or more device hardware requirements, the processor(s) provides the machine-learned data set of the first device to the second device to provide the categorized machine-learned information of the first device to the second device for use by the second device.

Abstract: The present disclosure is directed to systems and methods for visualizing low-intensity pathologies or anatomical structures. The method can include applying, via a MRI machine, an RF pulse configured to suppress the imaging of fat in the subject and acquiring, via the MRI machine, an image of the subject. A subtle intensity graduating homomorphic transform is applied to the acquired image to provide improved visualized of the low-intensity pathology or anatomical structure. The resulting transformed image can be output to the user.

Abstract: A method for processing breast tissue image data includes obtaining image data of a patient's breast tissue, processing the image data to generate a set of image slices, the image slices collectively depicting the patient's breast tissue; feeding image slices of the set through each of a plurality of object-recognizing modules, each of the object-recognizing modules being configured to recognize a respective type of object that may be present in the image slices; combining objects recognized by the respective object-recognizing modules to generate a synthesized image of the patient's breast tissue; and displaying the synthesized image.

Abstract: For non-invasive EP mapping, a sparse number of electrodes (e.g., 10 in a typical 12-lead ECG exam setting) are used to generate an EP map without requiring preoperative 3D image data (e.g. MR or CT). An imager (e.g., a depth camera) captures the surface of the patient and may be used to localize electrodes in any positioning on the patient. Two-dimensional (2D) x-rays, which are commonly available, and the surface of the patient are used to segment the heart of the patient. The EP map is then generated from the surface, heart segmentation, and measurements from the electrodes.

Abstract: For registration of medical images with deep learning, a neural network is designed to include a diffeomorphic layer in the architecture. The network may be trained using supervised or unsupervised approaches. By enforcing the diffeomorphic characteristic in the architecture of the network, the training of the network and application of the learned network may provide for more regularized and realistic registration.

Abstract: Methods and systems for computed tomography provide advances in efficiency. The methods operating within the parallel-beam geometry, rather than a divergent beam geometry, for the majority of the operations. In back projection methods perform digital image coordinate transformations on selected intermediate-images, the parameters of one or more coordinate transformations being chosen such that Fourier spectral support of the intermediate-image is modified so that a region of interest has an increased extent along one or more coordinate direction and the aggregates of the intermediate-images can be represented with a desired accuracy by sparse samples.

Abstract: The present application belongs to the technical field of oil and gas exploration and development, and specifically provides a rock stratification identification method and apparatus, a device and a storage medium. The method includes: acquiring multiple sets of CT slice images of a rock, where each set of the CT slice images include a first CT slice image and a second CT slice image, the first CT slice image and the second CT slice image are acquired by scanning a same depth section of the rock, scanning energy corresponding to the first CT slice image is different form scanning energy corresponding to the second CT slice image; determining multiple dual energy indices of the rock according to the multiple sets of CT slice images; and determining a rock stratification according to the multiple dual energy indices, which can identify stratification of a large-scale rock by using CT.

Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML modal(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Abstract: A method may include obtaining a first set of projection data with respect to a first dose level; reconstructing, based on the first set of projection data, a first image; determining a second set of projection data based on the first set of projection data, the second set of projection data relating to a second dose level that is lower than the first dose level; reconstructing a second image based on the second set of projection data; and training a first neural network model based on the first image and the second image. In some embodiments, the trained first neural network model may be configured to convert a third image to a fourth image, the fourth image exhibiting a lower noise level and corresponding to a higher dose level than the third image.

Abstract: The invention relates, in part, to systems and methods for scoring a sample containing tumor tissue from a cancer patient. The score obtained from these methods can be indicative of a likelihood that a patient may respond positively to immunotherapy. The invention also relates, in part, to methods of deriving a value for % biomarker positivity (PBP) for all cells or optionally, one or more subsets thereof, present in a field of view of a tissue sample from a cancer patient. The values for PBP can be indicative of a patient's response to immunotherapy.

Abstract: Methods for reconstructing images of a subject from data acquired with a medical imaging system, such as a magnetic resonance imaging (“MRI”) system, are described. In general, the image reconstructions implement a primal-dual optimization strategy that, in each iteration, stochastically updates a dual variable.

Abstract: Techniques for representing a scene or map based on statistical data of captured environmental data are discussed herein. In some cases, the data (such as covariance data, mean data, or the like) may be stored as a multi-resolution voxel space that includes a plurality of semantic layers. In some instances, individual semantic layers may include multiple voxel grids having differing resolutions. Multiple multi-resolution voxel spaces may be merged to generate combined scenes based on detected voxel covariances at one or more resolutions.

Abstract: Two or more learning images generated for a first subject or a second subject and one or more correct answer images generated for the second subject or a third subject are received. In a case where pixel values of corresponding pixels of the two or more learning images are synthesized by using a synthesis parameter value, the parameter value at which the synthesized pixel values are close to a pixel value of a corresponding pixel of the correct answer image is obtained. An image generated for the first subject and having the same type as the two or more learning images is received as an examination target image. A synthesized image desired by a user is generated by synthesizing pixel values of corresponding pixels of the two or more examination target images by using the synthesis parameter value.

Abstract: A method of constructing a patient-specific orthopedic implant comprising: (a) comparing a patient-specific abnormal bone model, derived from an actual anatomy of a patient's abnormal bone, with a reconstructed patient-specific bone model, also derived from the anatomy of the patient's bone, where the reconstructed patient-specific bone model reflects a normalized anatomy of the patient's bone, and where the patient-specific abnormal bone model reflects an actual anatomy of the patient's bone including at least one of a partial bone, a deformed bone, and a shattered bone, wherein the patient-specific abnormal bone model comprises at least one of a patient-specific abnormal point cloud and a patient-specific abnormal bone surface model, and wherein the reconstructed patient-specific bone model comprises at least one of a reconstructed patient-specific point cloud and a reconstructed patient-specific bone surface model; (b) optimizing one or more parameters for a patient-specific orthopedic implant to be mounte

Abstract: Methods and apparatus for evaluating an impact of injury to brain networks or regions are provided. The method comprises receiving MRI data of a brain of an individual, including a first volumetric dataset recorded using first imaging parameters and a second volumetric dataset recorded using second imaging parameters, combining, on a voxel-by-voxel basis, first MRI data based on the first volumetric dataset and second MRI data based on the second volumetric dataset to produce a volumetric injury map, performing a structural-functional analysis of one or more brain networks or regions by refining the volumetric injury map using a volumetric eloquence map that specifies eloquent brain tissue within the one or more brain networks or regions to determine an impact of injury within the one or more brain networks or regions, and displaying a visualization of the determined impact of injury within the one or more brain networks or regions.

Abstract: The disclosure relates to a computer-implemented method for the provision of a transformation instruction for registering a 2D image with a 3D image. The method includes: receiving the 2D image and the 3D image; generating input data based on the 2D image having contour pixels and the 3D image having contour voxels; applying a trained function to the input data for identification of contour pixels of the 2D image and contour voxels of the 3D image, wherein at least one parameter of the trained function is adjusted based on a comparison of training contour pixels with comparison contour pixels and a comparison of training contour voxels corresponding thereto with comparison contour voxels; determining the transformation instruction based on the identified contour pixels of the 2D image and the contour voxels corresponding thereto of the 3D image for registering the 2D image with the 3D image; and providing the transformation instruction.

Abstract: An ophthalmic imaging apparatus includes an OCT optical system that detects an interference signal between measurement light with which a subject eye is irradiated and reference light, and acquires OCT data of the subject eye by processing the interference signal, a scanner that scans with the measurement light on a scanning line on the subject eye, and a processor. The processor determines whether or not the OCT data acquired on the scanning line is proper, controls the scanner based on a determination result to change the scanning line for the measurement light to the next scanning line, and interpolates an alternative to OCT data determined not to be proper, based on OCT data corresponding to scanning lines around a scanning line on which the OCT data determined not to be proper is acquired.

Abstract: A system for scanning anatomical structures and for visualizing the scanning result, wherein the system includes an intraoral scanner, which intraorally captures an image of the anatomical structures, an extraoral detection unit, which detects a spatial position of the intraoral scanner relative to an observer or a person conducting the scan, and a computing unit, which, during the scanning procedure, connects the scanner with a screen and the detection unit and generates a scanning result based on the intraorally captured image of the anatomical structures and the detected spatial position of the intraoral scanner relative to the observer, and which, during pauses in the scanning procedure, estimates the position, orientation and scaling of the anatomical structures and, as a scanning result, generates an image of the anatomical structures corresponding to the estimation, and wherein the screen displays the scanning result generated by the computing unit.

Abstract: An imaging system (102) includes a radiation source (112) configured to emit X-ray radiation, a detector array (114) configured to detect X-ray radiation and generate a signal indicative thereof, an a reconstructor (116) configured to reconstruct the signal and generate non-spectral image data. The imaging system further includes a processor (124) configured to process the non-spectral image data using a deep learning regression algorithm to estimate spectral data from a group consisting of spectral basis components and a spectral image.

Abstract: Methods and systems suitable for obtaining information related to at least one of: respiration rate, heart rate, respiration rate variability, heart rate variability, temporal characteristics of at least a part of a heartbeat, temporal characteristics of at least a part of a respiration cycle, or a duration of a time interval of propagation of a blood pressure pulse from a first point or area or part of a body to a second point or area or part of the body, in a non-contact fashion.

Abstract: In certain embodiments, training of a prediction model (e.g., recognition or other prediction model) may be facilitated via a training set based on one or more logos or other graphics. In some embodiments, graphics information associated with a logo or graphic (e.g., to be recognized via a recognition model) may be obtained. Media items (e.g., images, videos, etc.) may be generated based on the graphics information, where each of the media items includes (i) content other than the logo and (ii) a given representation of the logo integrated with the other content. In some embodiments, the media items may be processed via the recognition model to generate predictions (related to recognition of the logo or graphic for the media items). The recognition model may be updated based on (i) the generated predictions and (ii) corresponding reference indications (related to recognition of the logo for the media items).

Abstract: A method of inspecting a component using computed tomography is described, the method comprising the steps of: (a) providing a computed tomography (CT) scanner; (b) providing a target component; (c) reviewing the geometry of the component; (d) estimating the best component orientation; (e) orienting the component; (f) scanning the component with the CT scanner; (g) loading CT scan data into 3D image software; (h) registering the best CT scan data; (i) determining acceptable and unacceptable regions of CT scan data; (j) determining additional component orientations; (k) repeating steps (e) through (i) until all regions of CT scan data for the component are acceptable; and (l) creating a merged volume of acceptable CT scan data.

Abstract: A respiratory motion estimation method (30) includes reconstructing emission imaging data (22) to generate a reconstructed image (50). The emission imaging data comprises lines of response (LORs) acquired by a positron emission tomography (PET) imaging device or projections acquired by a gamma camera. One or several assessment volumes (66) are defined within the reconstructed images. The emission imaging data are binned into time interval bins based on time stamps of the LORs or projections. A displacement versus time curve (70) is generated by computing, for each time interval bin, a statistical displacement metric of the LORs or projections that both are binned in the time interval bin and intersect the motion assessment volume. The motion assessment volume may be selected to overlap a motion assessment image feature (60) identified in the reconstructed image.

Abstract: A non-label diagnosis apparatus for a hematologic malignancy may include a 3-D refractive index cell imaging unit configured to generate a 3-D refractive index slide image of a blood smear specimen by capturing a 3-D refractive index image in the form of the blood smear specimen in which blood (including a bone-marrow or other body fluids) of a patient has been smeared on a slide glass, an ROI detection unit configured to sample a suspected cell segment in the blood smear specimen based on the 3-D refractive index slide image and to determine, as ROI patches, cells determined as abnormal cells, and a diagnosis unit configured to determine a sub-classification of a cancer cell corresponding to each of the ROI patches using a cancer cell sub-classification determination model constructed based on a deep learning algorithm and to generate hematologic malignancy diagnosis results by gathering sub-classification results of the ROI patches.

Abstract: The present disclosure pertains to computer-assisted search of image slices. In some embodiments, an image slice having a detected finding (and representing a cross section of at least a portion of an individual) may be determined. A search space may be reduce to a subset of image slices respectively representing a cross section corresponding to the cross section represented by the determined image slice. The search space reduction may comprise filtering a set of image slices based on the cross section represented by the determined image slice. Image slice information related to the determined image slice and related one or more image slices (of the image slices subset) may be obtained based on the search space reduction. A determination of whether indications of the detected finding exist in the image slices (of the image slices subset) may be effectuated based on the image slice information.

Abstract: Techniques are described relating to analyzing user transactions based on time of day of occurrence, and using time of a day as a factor in determining whether a new transaction should be allowed or disallowed. People may have particular tendencies to engage in transaction at certain times of a day. When a new transaction occurs that does not fit a previous pattern, this can indicate someone else has gained access to the account. Past times of transactions can be transformed to a two-dimensional representation that avoids discontinuity. A smoothed probability distribution can indicate a likelihood of whether a new transaction fits previous patterns. If a new transaction is unlikely due to time of day, the new transaction might be denied/prevented from completing. Denial of the transaction may also be based on additional factors besides the time of day.

Abstract: In the field of obesity related disease, identification of patients with nonalcoholic steatohepatitis (NASH) would be useful to counsel them more intensively on diet and lifestyle changes and propose new pharmacological treatments. As a consequence, the inventors worked on a method for post-processing images of a region of interest of the liver for reconstructing a map of the internal magnetic susceptibility by using a Bayesian regularization approach to inverse the internal magnetic field. Such method can be implemented on computer and provides better results than other known methods for obesity related disease. This method may be applied for predicting that a subject is at risk of suffering from such disease, diagnosing a disease, identifying a therapeutic or a biomarker and screening compounds useful as a medicine.

Abstract: An image segmentation method is provided for a computer device. The method includes obtaining a plurality of sample images, calling an initial model to input the plurality of sample images into the initial model and to train the initial model based on the plurality of sample images to obtain an image segmentation model and, based on the initial model, determining a number of image segmentation modules according to a number of types of pixels of the plurality of sample images. Different image segmentation modules are used for segmenting different regions of an image. The method also includes calling the image segmentation model in response to obtaining a first image to be segmented, and segmenting the first image by using the image segmentation model based on a plurality of image segmentation modules, to output a second image. Computer device and non-transitory computer-readable storage medium counterparts are also contemplated.

Abstract: The medical information display apparatus includes a first data acquisition unit that acquires a table showing relevance between at least one item included in clinical diagnostic information on dementia and a brain area, a second data acquisition unit that acquires clinical diagnostic information on dementia of a subject, an image acquisition unit that receives an input of a three-dimensional brain image, a brain area division unit that divides the three-dimensional brain image of the subject, an image analysis unit that calculates an analysis value for each brain area, an operation unit that selects one item among items included in the clinical diagnostic information of the subject, a display unit, and a display controller that specifies a brain area corresponding to the one item selected by the operation unit and displays brain area specifying information for specifying the specified brain area and an analysis value of the brain area.

Abstract: Embodiments discussed herein facilitate system-independent quantitative perfusion measurements. One example embodiment is a method, comprising: accessing 4D (Four Dimensional) perfusion imaging data of a tissue, where the 4D perfusion imaging data comprises a plurality of 3D (Three Dimensional) stacks of perfusion imaging data over time; performing at least one of artifact reduction or post-processing on the 4D perfusion image data to generate processed 4D perfusion image data; computing one or more quantitative perfusion parameters for the tissue based at least in part on the processed 4D perfusion image data; and outputting a visual representation of the one or more quantitative perfusion parameters.

Abstract: A novel GAN is trained to predict high fidelity synthetic images based on low quality input dental images. The GAN further takes input anatomic masks as inputs with each image, the masks labeling pixels of the image corresponding to dental features. The GAN includes an encoder-decoder generator with semantically aware normalization between stages of the decoder according to the masks. The predicted synthetic dental image and an unpaired dental image are evaluated by a first discriminator of the GAN to obtain a realism estimate. The synthetic image and an unpaired dental image may be processed using a pretrained dental encoder to obtain a perceptual loss. The GAN is trained with the realism estimate, perceptual loss, and L1 loss. Utilization may include inputting noisy, low contrast, low resolution, blurry, or degraded dental images and outputting high resolution, denoised, high contrast, deobfuscated, and sharp dental images.

Abstract: Methods for correcting a non-uniform power response of a radiofrequency (“RF”) transmit coil used in magnetic resonance imaging (“MRI”) are described. Transmit power response data for an RF transmit coil are processed to compute RF amplitude scaling factors for the RF transmit coil as a function of transmit frequency offset. The RF amplitude scaling factors can be used to correct transmitted RF power, and thus flip angle, to be more uniform over a range of transmit frequency offsets, as may be encountered when imaging with lower field MRI systems or MRI systems with high strength or asymmetric gradients.

Abstract: This disclosure relates generally to bio-signal detection, and more particularly to method and system for non-contact bio-signal detection using ultrasound signals. In an embodiment, the method includes acquiring an in-phase I(t) baseband signal and a quadrature Q(t) baseband signal associated with an ultrasound signal directed from the sensor assembly towards the target. Magnitude and phase signals are calculated from the in-phase and quadrature baseband signals, and are filtered by passing through a band pass filter associated with a predefined frequency range to obtain filtered magnitude and phase signals. Fast Fourier Transformation (FFT) of the filtered magnitude and phase signals is performed to identify frequency of dominant peaks of spectrum of the magnitude and phase signals in the ultrasound signal. Value of the bio-signal associated with the target is determined based on weighted values of the frequency of the dominant peaks of the magnitude and phase signals.

Abstract: The present invention discloses methods suitable for obtaining information related to at least one physiologic parameter of a person belonging to the group comprising respiration rate, heart rate, respiration rate variability, heart rate variability, temporal characteristics of at least a part of a heartbeat, and temporal characteristics of at least a part of a respiration cycle in a non-contact fashion. The present invention also discloses systems suitable for obtaining information related to at least one physiologic parameter of a person belonging to said group of physiologic parameters in a non-contact fashion.

Abstract: A system according to the present disclosure may include a display unit, a processor communicatively coupled to the display unit and to an ultrasound imaging apparatus for generating an image from ultrasound data representative of a bodily structure and fluid flowing within the bodily structure. The processor may be configured to generate vector field data including axial and lateral (or transverse) velocity components of the fluid flowing within the bodily structure, calculate velocity profiles for a plurality of locations along a wall of the bodily structure based on the axial and lateral velocity components, generate wall shear stress (WSS) visualization data based, at least in part, on the velocity profiles, and cause the display unit to concurrently display the image including the bodily structure overlaid with the WSS visualization data.