86 results about "Mri scan" patented technology

The present invention relates to a method for diagnosing and predicting the course of Alzheimer's disease (AD)-related dementia after feature extraction of brain images by Variational Autoencoders (VAE) and using Artificial intelligence and Machine learning (AI / ML) algorithms, as compared with non-demented control cases (CN), mild cognitive impairment (MCI), and other non-Alzheimer's dementia (non-ADD) cases. Method 1 extracts the reduced dimensional latent feature vector from the sMRI scans / brain images using a VAE and does multiple sampling from the latent distribution to reduce the class imbalance followed by classification using different AI / ML models. Method 2, the extracted feature vectors from VAE are used as input to a mixture of class-Restricted Boltzmann Machines (cl-RBM) for AD, MCI, and CN classification. Method-1 diagnoses AD and distinguishes it from MCI, CN, and non-ADD using MRI scans with or without additional aids. Method-2 further extends to diagnosis and prediction of AD.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

The invention discloses an Alzheimer's disease early diagnosis method and device based on a diffusion model. The method comprises the steps that a plurality of brain MRI images of a diagnosis object in different periods are acquired and preprocessed; obtaining an MRI compression feature corresponding to each brain MRI image by using an encoder in the trained auto-encoder network; taking the MRI feature as a conditional variable, and performing prediction by using the trained diffusion model to obtain an MRI prediction feature; and obtaining a brain MRI image obtained by performing MRI scanning on the diagnosis object again, obtaining an MRI real feature through the trained encoder, taking an error between the MRI real feature and the MRI prediction feature as an abnormal score, and if the abnormal score exceeds a predetermined threshold, determining that the Alzheimer's disease is positive. The method combines the generation capability of the diffusion model and the time sequence modeling advantages, has the advantages of high calculation efficiency, high interpretability and the like, and can realize early diagnosis of the Alzheimer's disease only through healthy population data.

Described herein are various embodiments of techniques for controlling creation of a magnetic resonance (MR) image from a magnetic resonance imaging (MRI) scan of a subject using an MRI scanner, through real-time, dynamic control of the MRI scanner during the MRI scan and / or of processing during the MRI scan of acquired MR signals as part of generating MR data for creation of the MR image. More particularly, some embodiments described herein relate to identifying changes that occur in a magnetic field within an MRI scanner during a course of an MRI scan. In response to changes in the magnetic field, an MRI scanner may be adjusted to change a way in which additional MR data is captured and / or a processing of captured MR signals (that are to be used in generating an MR image) may be adjusted.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

An exemplary AI system and method are disclosed for reconstructing high-fidelity motion-corrected MRI scans from low-field, motion-corrupted MRI scans (e.g., neonatal MRI data) using a neural network (NN) model in combination with a physics-based motion model. The exemplary AI system and method can enhance image quality and remove motion artifacts which can allow for scans to be performed with significantly reduced scan time, a particular benefit for neonatal subjects as well as all patients when employing low-field MRI.

The invention belongs to the technical field of medical care informatics, and particularly relates to a carotid plaque and vascularization assessment method and system based on FF and R2 * atlas, T1W-TSE and FF / R2 * atlas are generated through single MRI scanning, the vascularization and plaque components represented by PVAT can be quantitatively assessed at the same time, such as LRNC (through the FF atlas), calcified plaque (through the R2 * atlas) and IPH staging (through T1W-TSE / R2 * fusion), and the assessment accuracy is high. The defects that traditional MRI qualitative analysis cannot distinguish IPH periods are overcome; and moreover, the inherent T1 / T2 * deviation in chemical shift imaging is eliminated by the Dixon-based FF / R2 * calculation, and the quantitative analysis precision is improved.

The invention relates to an apparatus (10) for monitoring a patient undergoing a magnetic resonance image (MRI) scan. The apparatus comprises an input unit (20), a processing unit (30) and an output unit (40). The input unit is configured to provide the processing unit with at least one sensor data of a patient undergoing an MRI scan of an MRI scanner. The input unit is configured to provide the processing unit with at least one scan parameter of the MRI scanner for the MRI scan. The input unit is configured to provide the processing unit with at least one characteristic of the patient. The processing unit is configured to predict a stress level of the patient and / or a predicted motion state of the patient, one or more predictions comprising utilizing the at least one sensor data of the patient, the at least one scan parameter of the MRI scanner and the at least one characteristic of the patient. The output unit is configured to output information related to the predicted stress level of the patient and / or the predicted motion state of the patient.

This invention discloses a coherent electrical stimulation method for idiopathic scoliosis combined with exercise therapy, belonging to the field of electrical stimulation methods for idiopathic scoliosis. It aims to address the problem of how to dynamically adjust stimulation parameters based on individual differences to improve treatment efficacy. The method includes acquiring raw image sequences of the patient's spine through MRI scans, preprocessing the raw image sequences to construct a three-dimensional geometric model of the spine, configuring simulated electrical property parameters for the tissue structures, and then adding coherent electrical surface electrodes to the model. The electrode placement area on the three-dimensional geometric model of the spine is initially defined, and a coherent electrical stimulation signal is generated using a function generator. A comprehensive evaluation index value is calculated through finite element analysis to dynamically adjust the coherent electrical stimulation signal. Based on the coherent electrical stimulation signal parameters and its comprehensive evaluation index value, a coherent electrical stimulation scheme is determined, and ultrasound-guided technology is used to locate the electrode positions and implement coherent electrical stimulation therapy.

Device (1) for obtaining a spatial instrument image (60) of a medical instrument (50) and an anatomical image (62) using a magnetic resonance imaging (MRI) device (10), wherein the device (1) comprises a medical instrument (50), a MRI device (10), and a computing and control unit (70), wherein the medical instrument (50) comprises at least one marker material in at least one region (52, 52', 52'') that exhibits a nuclear magnetic resonance outside the proton resonance range, and wherein the computing and control unit (70) for controlling the MRI device (10) is configured such that, by means of the MRI device (10), a nuclear magnetic resonance imaging scan using proton resonance is performed to obtain a spatial anatomical image (62), and a nuclear magnetic resonance imaging scan using the nuclear magnetic resonance of the at least one marker material is performed. Acquisition of a spatial instrument image (60) is carried out,wherein the computing and control means (70) is designed to receive the anatomy image (62) and the instrument image (60) and to superimpose the anatomy image (62) and the instrument image (60) in the correct position and orientation, characterized in that the computing and control means (70) comprises an image model (56) of the medical instrument (50) and is designed to determine the position and orientation of the medical instrument (50) by means of the instrument image (60) and to superimpose the image model (56) of the medical instrument (50) onto the anatomy image (62) in the correct position and orientation.

A multi-task deep learning system for the detection of Alzheimer's and Parkinson's disease using MRI, consisting of: a ResNet18-based Convolutional Neural Network (CNN) module configured to extract spatial features from preprocessed T1-weighted MRI scans; a first classification head coupled to the CNN module and configured to predict the disease type from a group consisting of Alzheimer's disease (AD), Parkinson's disease (PD) and a healthy control group; a second classification head which is conditionally activated by the first classification head when Alzheimer's disease is predicted and is configured to classify the Alzheimer's stage into one of the following stages: very mild, mild or moderate dementia; a combined loss function module configured to integrate categorical cross-entropy loss for disease classification and weighted cross-entropy loss for Alzheimer stage classification; an interpretability engine configured to provide predictive explanations and including the following: a Grad-CAM module for class-specific spatial activation mapping, a SHAP module for global feature mapping at the voxel level and a LIME module for the local, surrogate-based explanation of individual MRI scan predictions.

The embodiment of the invention provides a high-precision spatial registration method and system based on a total ankle skeleton, and belongs to the technical field of medical image processing, and the method comprises the steps: obtaining tibia and talus three-dimensional data through CT or MRI scanning, and constructing and storing a high-precision skeleton simulation model; positioning trackers containing asymmetrically distributed passive infrared reflection balls are fixed to the tibia and the talus respectively, based on the positioning trackers, double cameras of an optical tracker are used for shooting the reflection balls, three-dimensional coordinates of mark points are obtained through parallax calculation, the real-time space posture of the bone is fitted, a registration matrix is updated, and based on a preoperative model and the tracking posture, the three-dimensional coordinates of the mark points are obtained. A talus coordinate system and a tibia coordinate system are constructed respectively, coarse registration is carried out through coarse registration points based on the coordinate systems, then fine registration points are collected through a probe, fine registration is carried out through a weighted iteration nearest point algorithm, and space registration is completed. According to the invention, through multi-modal data fusion, dynamic pose tracking and a weighted iteration nearest point algorithm, full-ankle bone submillimeter-level high-precision space registration is realized.

A method for performing a scan of a subject utilizing a magnetic resonance imaging (MRI) system includes triggering a prescan by an MRI scanner of the MRI system upon the subject being setup on a table of the MRI scanner and the table reaching an iso-center of the MRI scanner. The method includes subsequent to the prescan, triggering a calibration scan of the subject with the MRI scanner, wherein the calibration scan is an acoustic noise suppressed MRI scan. The method includes obtaining calibration data from the calibration scan. The method includes obtaining prescription parameters for subsequent scans of the subject with the MRI scanner from the calibration data. The method includes triggering at least one scan of the subject with the MRI scanner based on the prescription parameters.

This utility model discloses an MRI scan positioning patch containing cod liver oil, belonging to the field of MRI scan auxiliary technology. It includes an upper transparent membrane and a lower transparent membrane, with a cod liver oil layer disposed between the upper and lower transparent membranes. The sides of the upper and lower transparent membranes are sealed together by a heat-sealing layer. This MRI scan positioning patch has a scientific and reasonable design. It uses a patch form to carry the cod liver oil, has a simple and novel structure, is easy to apply to the patient's skin, and is convenient to use, avoiding the discomfort caused by adhesive tape. Furthermore, the adhesive layer ensures the stability of the patch after application, improving the efficiency of clinical operations during MRI scans and demonstrating strong practicality.

A dynamic contrast-enhanced MRI reconstruction method may include: when preparing to inject a contrast agent and during injection thereof, using a DCE-MRI sequence to scan an imaging target, and using a high temporal resolution scanning parameter value pre-inputted by a user to acquire K-space data of each phase with high temporal resolution; based on the acquired K-space data of each phase with high temporal resolution, reconstructing an image of each phase with high temporal resolution; based on a low temporal resolution reconstruction parameter value pre-inputted by the user, subjecting adjacent K-space data of multiple phases with high temporal resolution to summing and averaging in a complex field, and subjecting K-space data obtained after averaging to image reconstruction, to obtain an image of each phase with low temporal resolution. Advantageously, images with high and low temporal resolution can be obtained simultaneously with just a single DCE-MRI scan.

Systems, computer software, and methods are disclosed for generating a relative electron density map (RED) from a magnetic resonance imaging (MRI) scan. This may include acquiring an MRI scan of a portion of a patient and segmenting a first region and a second region in the MRI scan. A RED map may then be generated from the MRI scan by assigning a first RED to the first region, assigning a second RED to the second region, and assigning a RED to unsegmented regions in the MRI scan based on their intensity in the MRI scan.

The invention relates to a method for performing an MRI examination of a patient using an MRI system, the MRI system, a computer-implemented method for providing a media signal using an AI system, the AI ​​system, and an associated computer program product. The inventive method for performing an MRI examination of a patient using an MRI system comprises the following steps: - Adjusting an examination parameter of the MRI examination to the patient using a control unit of the MRI system, - Providing the adapted investigation parameter via the control unit as an input parameter for a generative AI system, wherein the generative AI system is configured to generate a media signal as an output parameter in response to an input of the provided input parameter and to make the generated media signal available for retrieval, wherein the generated media signal depends on the adapted investigation parameter and includes an audio track and / or a video track, - Receiving the generated media signal by means of a playback unit, - Performing the MRI examination of the patient according to the adapted MRI examination parameters using the MRI system in such a way that the received media signal is played back to the patient via the playback unit during the procedure.

The present invention relates to the field of neuroscience, and proposes a method and system for realizing the cause identification of ischemic stroke based on neuroimaging, wherein the method comprises: obtaining a patient's brain nerve image, including CT and MRI scan images, diagnosing and analyzing the stroke patient, extracting the vascular load value by identifying the vascular tissue and the high-density sign of the cerebral artery in the CT scan image, identifying the thrombus pore through the MRI scan image, extracting the thrombus morphological characteristics, calculating the thrombus shape coefficient, combining the vascular load value and the thrombus shape coefficient, determining the abnormal area in the brain nerve image, identifying the ischemic area, and detecting the damaged nerve tissue, and finally, using the cause identification network to classify the lesions of the damaged nerve tissue, correlating the cause of stroke with the clinical index, and generating a cause report. The present invention can improve the accuracy of the cause identification of ischemic stroke.

Methods and systems for assessing gut barrier function with medical imaging techniques and orally ingested contrast are discussed herein. For example, a method for assessing gut health of a patient includes administering a contrast agent to the patient. The method further includes performing an magnetic resonance imaging (MRI) scan of the patient. The method further includes determining a metric corresponding to the contrast agent in a gut of the patient based on the performed MRI scan. The method further includes assessing health of the gut of the patient based on the metric corresponding to the contrast agent in the gut of the patient.

A method and system provide means to assess the likelihood, risk, or possible occurrence of a pathology that impacts multiple organs and / or systems, such as endocrine, exocrine, hypertension, blood and so forth that incorporates organ specific information and its relative role in and on the systemic risk. The method includes collecting medical metrics from the person, the medical metrics comprising quantitative parameters of phenotype of the person, and quantitative measures of a plurality of organs of the person from at least one MRI scan performed to obtain MRI data of the organs, using the metrics to assess a state of each of the organs, and selecting a plurality of the states and at least one of the quantitative parameters to estimate the risk of the multi-organ pathology in the person.

A computer-implemented method for generating a chemical shift artifact corrected reconstructed image from magnetic resonance imaging (MRI) data includes inputting into a trained deep neural network an image generated from the MRI data acquired during a non-Cartesian MRI scan of a subject. The method also includes utilizing the trained deep neural network to generate the chemical shift artifact corrected reconstructed image from the image, wherein the trained deep neural network was trained utilizing a tissue mixing model that models interactions between different tissue types to mitigate chemical shift artifacts. The method further includes outputting from the trained deep neural network the chemical shift artifact corrected reconstructed image.

The present invention discloses an automatic scanning and positioning method for MRI head 3D images based on deep learning, comprising the following steps: step s1: collecting MRI head 3D images and manually marking them, dividing the manually marked MRI head 3D images into training set, verification set and test set, and then preprocessing the training set, verification set and test set respectively; step s2: building a 3D convolutional neural network model; step s3: gradually inputting the preprocessed training set into the 3D convolutional neural network model for training, and verifying it through the verification set; step s4: running the preprocessed test set through the trained 3D convolutional neural network model, comparing the output result with the manually marked MRI head 3D image and calculating the error. The present invention searches for feature points in the MRI head 3D image through the 3D convolutional neural network model, realizes automatic positioning of MRI scanning, and improves efficiency through automated processes while maintaining a small error.

A method and system for modifying a series of magnetic resonance imaging (MRI) scan sequences for use in a single MRI examination. In one embodiment, a method and system intersperse a set of B1 amplitude measurement sequences within a received series of MRI scan sequences such that an RF parameter value of at least one scan sequence of the received series of MRI scan sequences is altered based on the results of at least one of the interspersed set of B1 amplitude measurement sequences.

This invention relates to the field of ultrasound therapy technology, specifically to an ultrasound localization method based on low-field magnetic resonance imaging (MRI). The method addresses the issues of large equipment size and high cost in existing ultrasound targeting techniques. By introducing a parallel ultrasound scanning process during the initial MRI scan to obtain ultrasound echo data, which is then stitched together with the MRI data. Subsequently, during target area localization, the ultrasound echo data and MRI data are analyzed simultaneously, enabling accurate localization of the target area in the brain and identification of easily penetrable locations on the skull where ultrasound energy can pass through. Based on this, an ultrasound probe with better penetration is selected as the focusing probe, eliminating probes with low emission efficiency, thereby improving the efficiency of ultrasound localization. Furthermore, improvements to the model allow the examination process to be performed using a low-field MRI system, reducing system costs.

This invention provides a device and method for acquiring a three-dimensional model of abdominal organs, comprising: a coil support assembly, an MRI scanner, and an image processing device; the coil support assembly is clamped and fixed to the side of the examination bed to support the abdominal coil and reduce pressure on the abdomen of the user; the MRI scanner is positioned above the abdomen of the user, with the axis of the examination positioning light aligned with the midpoint of the axis of the coil support assembly, and a magnetic resonance imaging (MRI) scan sequence is acquired by scanning with the MRI scanner; the image processing device is communicatively connected to the MRI scanner, acquires the MRI scan sequence, and obtains a three-dimensional model of the abdominal organs based on the MRI scan sequence. This invention achieves precise extraction of abdominal organs at all levels through multi-region seed point strategies and adaptive threshold segmentation, overcoming the inefficiency and subjective limitations of traditional manual segmentation, and ensuring the reliability of the results through automated algorithms, thus providing support for the image analysis of abdominal organs.

The invention relates to the technical field of orthopedic surgery navigation, and discloses an orthopedic surgery navigation system based on an AR technology, and the method comprises the steps: obtaining skeleton structure data of a patient through MRI scanning, carrying out the segmentation and reconstruction of original data through an image processing algorithm, and extracting a three-dimensional model of a skeleton structure; tracking and positioning the key feature region according to the extracted three-dimensional model of the skeletal structure so as to realize real-time tracking of the skeletal structure of the patient; constructing a virtual operation scene according to the skeleton structure data of the patient, and vividly rendering a skeleton model, an operation tool, a tissue and other elements by using a computer graphics technology; performing sensor calibration on the AR equipment and the surgical workstation, determining the position and direction of the AR equipment in the space by using a preset reference point or a marker, and establishing a mapping relation of a camera-world coordinate system; and performing virtual-real fusion on pre-planned navigation information and the actual skeleton structure of the patient according to the skeleton structure tracked in real time and the reconstructed operation scene, and superposing the navigation information in the visual field of a doctor in a virtual form through AR equipment to guide the doctor to perform an operation.

A computer-implemented method for generating a magnetic resonance (MR) image of an object includes applying, via a processing system comprising one or more processors, a Hadamard encoded radiofrequency (RF) excitation pulse sequence to adjacent slices within a magnetic resonance imaging (MRI) scanner to generate Hadamard encoded adjacent slices; multiplexing, via the processing system, the Hadamard encoded adjacent slices with secondary RF excitation pulses to generate a composite RF pulse; and generating, via the processing system, the MR image of the object using the composite RF pulse.

The provided is a personalized target selection method for a non-invasive neuromodulation technology, including: preprocessing functional magnetic resonance imaging (fMRI) data from MRI scan data of a current patient to acquire fMRI brain image feature data; inputting the fMRI brain image feature data into a pre-trained inter-subtype classification model to acquire a subtype label of the current patient and all feature voxels of the subtype label; preprocessing T1-weighted MRI data of structural magnetic resonance imaging (sMRI) data from the MRI scan data of the current patient to acquire a skull outline and a transformation matrix between the sMRI and fMRI data; performing coordinate transformation on the feature voxels, calculating a distance between each voxel on the skull outline and each feature voxel, marking response feature voxels, and counting a number of response feature voxels; and sorting the number of response feature voxels.

A system and method for controlling rhythmic sensory stimulation provided to a subject with post-stroke depression, PSD, during an MRI scan. A control unit is configured to control a rhythmic sensory stimulation signal such that the signal is rhythmically synchronous to a scanning rhythm of an MRI scanner performing the MRI scan.

The invention relates to a method for performing an MRI examination of a patient using an MRI system, the MRI system, a computer-implemented method for providing a media signal using an AI system, the AI ​​system and an associated computer program product. The inventive method for performing an MRI examination of a patient using an MRI system comprises the following steps: - Adjusting an examination parameter of the MRI examination to the patient using a control unit of the MRI system, - Providing the adapted investigation parameter via the control unit as an input parameter for a generative AI system, wherein the generative AI system is configured to generate a media signal as an output parameter in response to an input of the provided input parameter and to make the generated media signal available for retrieval, wherein the generated media signal depends on the adapted investigation parameter and includes an audio track and / or a video track, - Receiving the generated media signal by means of a playback unit, - Performing the MRI examination of the patient according to the adapted MRI examination parameters using the MRI system in such a way that the received media signal is played back to the patient via the playback unit during the procedure.