Abstract: Devices, systems, and techniques are disclosed for verifying the occurrence of an acute health event. An example device includes communication circuitry configured to receive a communication indicative of an acute health event of a patient and memory communicatively coupled to the communication circuitry and being configured to store the indication of the acute health event. The device includes processing circuitry communicatively coupled to the communication circuitry and the memory. The processing circuitry is configured to, in response to the communication, verify the acute health event and based on the verification of the acute health event, send an alert regarding the acute health event.

Abstract: Systems and methods for training and implementing a machine learning algorithm to generate feature maps depicting spatial patterns of features associated with osteolysis, synovitis, or both. MRI data, including multispectral imaging data, are input to the trained machine learning algorithm to generate the feature maps, which may indicate features such as a location and probability of a pathology classification, a severity of synovitis, a type of synovitis, a synovial membrane thickness, and other features associated with osteolysis or synovitis. In some implementations, synovial anatomy are segmented in the MRI data before inputting the MRI data to the machine learning algorithm. These segmented MRI data may be generated using another trained machine learning algorithm.

Abstract: Aneurysms are classified and quantitatively analyzed based on medical image data acquired from a subject. In general, one or more algorithms are implemented to automatically classify, or otherwise diagnose, and measure aneurysms and their change over time. These algorithms make use of artificial intelligence and deep learning to develop quantitative analytics that can be consolidated into diagnostic reports.

Abstract: Systems and methods for quantitative susceptibility mapping (“QSM”) using magnetic resonance imaging (“MRI”) are described. Localized magnetic field information is used when performing the inversion to compute quantitative susceptibility maps. The localized magnetic field information can include multi-resolution subvolumes obtained by segmenting, or dividing, a field shift map. In some instances, a trained machine learning algorithm, such as a trained neural network, can be implemented to convert the localized magnetic field information into quantitative susceptibility data. These local susceptibility maps can be combined to form a composite quantitative susceptibility map of the imaging volume.

Abstract: Systems and methods for quantitative susceptibility mapping (“QSM”) using magnetic resonance imaging (“MRI”) are described. Localized magnetic field information is used when performing the inversion to compute quantitative susceptibility maps. The localized magnetic field information can include multi-resolution subvolumes obtained by segmenting, or dividing, a field shift map. In some instances, a trained machine learning algorithm, such as a trained neural network, can be implemented to convert the localized magnetic field information into quantitative susceptibility data. These local susceptibility maps can be combined to form a composite quantitative susceptibility map of the imaging volume.

Abstract: Aneurysms are classified and quantitatively analyzed based on medical image data acquired from a subject. In general, one or more algorithms are implemented to automatically classify, or otherwise diagnose, and measure aneurysms and their change over time. These algorithms make use of artificial intelligence and deep learning to develop quantitative analytics that can be consolidated into diagnostic reports.

Abstract: Described here are systems and methods for using a magnetic resonance imaging (“MRI”) system to estimate parameters of spectral profiles contained in multispectral data acquired using multispectral imaging (“MSI”) techniques, such as MAVRIC. These spectral profile parameters are reliably extracted using an iterative perturbation theory technique and utilized in a number of different applications, including fat suppression, artifact correction, and providing accelerated data acquisitions.

Abstract: Systems and methods for training and implementing a machine learning algorithm to generate feature maps depicting spatial patterns of features associated with osteolysis, synovitis, or both. MRI data, including multispectral imaging data, are input to the trained machine learning algorithm to generate the feature maps, which may indicate features such as a location and probability of a pathology classification, a severity of synovitis, a type of synovitis, a synovial membrane thickness, and other features associated with osteolysis or synovitis. In some implementations, synovial anatomy are segmented in the MRI data before inputting the MRI data to the machine learning algorithm. These segmented MRI data may be generated using another trained machine learning algorithm.

Abstract: Systems and methods are provided for performing a calibration “pre-scan” prior to acquiring data using a magnetic resonance imaging (“MRI”) system performing a multi-spectral imaging (“MSI”) acquisition. Information from the calibration scan is used to optimize the scanning and data collection during the MSI scan. As a result, scan times and motion artifacts are reduced. In addition, image resolution can also be increased, thereby improving image quality. As one example, the MSI acquisition can be a MAVRIC acquisition. In general, the calibration data is used to determine the minimum number of spectral bins required to achieve acceptable image quality near a specific metallic implant or device.

Abstract: Systems and methods for performing diffusion-weighted multi-spectral imaging (“MS!”) with a magnetic resonance imaging (“MRI”) system are provided, Diffusion-weighted images can thus be acquired from a subject in which a metallic object, such as an implant or other device, is present. In general, a two-dimensional or three-dimensional diffusion-weighted PROPELLER acquisition is performed to acquire data from multiple different spectral bins. Images from the spectral bins are reconstructed and combined to form diffusion-weighted composite images. Non-CPMG phase-cycling and split-blade PROPELLER techniques are combined with PROPELLER MSI metal artifact mitigation principles to this end.

Abstract: Described here are systems and methods for using a magnetic resonance imaging (“MRI”) system to estimate parameters of spectral profiles contained in multispectral data acquired using multispectral imaging (“MSI”) techniques, such as MAVRIC. These spectral profile parameters are reliably extracted using an iterative perturbation theory technique and utilized in a number of different applications, including fat suppression, artifact correction, and providing accelerated data acquisitions.

Abstract: Systems and methods are provided for performing a calibration “pre-scan” prior to acquiring data using a magnetic resonance imaging (“MRI”) system performing a multi-spectral imaging (“MSI”) acquisition. Information from the calibration scan is used to optimize the scanning and data collection during the MSI scan. As a result, scan times and motion artifacts are reduced. In addition, image resolution can also be increased, thereby improving image quality. As one example, the MSI acquisition can be a MAV-RIC acquisition. In general, the calibration data is used to determine the minimum number of spectral bins required to achieve acceptable image quality near a specific metallic implant or device.

Abstract: Systems and methods for performing diffusion-weighted multi-spectral imaging (“MS!”) with a magnetic resonance imaging (“MRI”) system are provided, Diffusion-weighted images can thus be acquired from a subject in which a metallic object, such as an implant or other device, is present. In general, a two-dimensional or three-dimensional diffusion-weighted PROPELLER acquisition is performed to acquire data from multiple different spectral bins. Images from the spectral bins are reconstructed and combined to form diffusion-weighted composite images. Non-CPMG phase-cycling and split-blade PROPELLER techniques are combined with PROPELLER MSI metal artifact mitigation principles to this end.

Abstract: A system and method for generating MR phase contrast images near metal include an MRI apparatus that includes an MRI system having a plurality of gradient coils and an RF transceiver system and an RF switch controlled by a pulse module to transmit RF signals to an RF coil assembly. The MRI apparatus also includes a computer programmed to acquire a plurality of three-dimensional (3D) MR data sets and to generate a plurality of frequency images based on the plurality of 3D MR data sets. Each 3D MR data set is acquired using a central transmit frequency and a central receive frequency set to an offset frequency value that is distinct for each 3D MR data set. The computer is also programmed to convert the plurality of frequency images to a plurality of time domain images and to generate a phase image based on the plurality of time domain images.

Abstract: A system and method for generating MR phase contrast images near metal include an MRI apparatus that includes an MRI system having a plurality of gradient coils and an RF transceiver system and an RF switch controlled by a pulse module to transmit RF signals to an RF coil assembly. The MRI apparatus also includes a computer programmed to acquire a plurality of three-dimensional (3D) MR data sets and to generate a plurality of frequency images based on the plurality of 3D MR data sets. Each 3D MR data set is acquired using a central transmit frequency and a central receive frequency set to an offset frequency value that is distinct for each 3D MR data set. The computer is also programmed to convert the plurality of frequency images to a plurality of time domain images and to generate a phase image based on the plurality of time domain images.

Abstract: A system and method for reducing blurring artifacts in multi-spectral MR imaging near metal include an MRI system having a plurality of gradient coils positioned about a bore of a magnet and an RF transceiver system and an RF switch controlled by a pulse module to transmit RF signals to an RF coil assembly to acquire MR images. A computer is also included and programmed to acquire a plurality of three-dimensional (3D) MR data sets, each 3D MR data set acquired using a central transmit frequency and a central receive frequency set to an offset frequency value that is distinct for each 3D MR data set. The computer is also programmed to reconstruct a subimage for each of the plurality of 3D data sets, apply a de-blurring correction to each of the subimages, and generate a composite image based on the plurality of 3D MR data sets.

Abstract: A system and method for multi-spectral MR imaging near metal include a computer programmed to calculate an MR pulse sequence comprising a plurality of RF pulses configured to excite spins in an imaging object and comprising a plurality of volume selection gradients and determine a plurality of distinct offset frequency values. For each respective determined offset frequency value, the computer is programmed to execute the MR pulse sequence having a central transmit frequency and a central receive frequency of the MR pulse sequence set to the respective determined offset frequency value. The computer is also programmed to acquire a three-dimensional (3D) MR data set for each MR pulse sequence execution and generate a composite image based on data from each of the acquired 3D MR data sets.

Abstract: An apparatus and method for reconstructing multi-spectral 3D MR images includes a magnetic resonance (MRI) apparatus that includes an MRI system having a plurality of gradient coils positioned about a bore of a magnet, and an RF transceiver system and an RF switch controlled by a pulse module to transmit RF signals to an RF coil assembly to acquire MR images. The MRI apparatus also includes a computer programmed to acquire a plurality of three-dimensional (3D) MR data sets, wherein each 3D MR data set is acquired using a central transmit and receive frequency set to an offset frequency value that is distinct for each 3D MR data set. The computer is also programmed to simultaneously generate a composite image and a magnetic field map based on the plurality of 3D MR data sets.

Abstract: A system and method for reducing blurring artifacts in multi-spectral MR imaging near metal include an MRI system having a plurality of gradient coils positioned about a bore of a magnet and an RF transceiver system and an RF switch controlled by a pulse module to transmit RF signals to an RF coil assembly to acquire MR images. A computer is also included and programmed to acquire a plurality of three-dimensional (3D) MR data sets, each 3D MR data set acquired using a central transmit frequency and a central receive frequency set to an offset frequency value that is distinct for each 3D MR data set. The computer is also programmed to reconstruct a subimage for each of the plurality of 3D data sets, apply a de-blurring correction to each of the subimages, and generate a composite image based on the plurality of 3D MR data sets.

Abstract: A system and method for multi-spectral MR imaging near metal include a computer programmed to calculate an MR pulse sequence comprising a plurality of RF pulses configured to excite spins in an imaging object and comprising a plurality of volume selection gradients and determine a plurality of distinct offset frequency values. For each respective determined offset frequency value, the computer is programmed to execute the MR pulse sequence having a central transmit frequency and a central receive frequency of the MR pulse sequence set to the respective determined offset frequency value. The computer is also programmed to acquire a three-dimensional (3D) MR data set for each MR pulse sequence execution and generate a composite image based on data from each of the acquired 3D MR data sets.