Abstract: An apparatus for use in a magnetic resonance (MR) system for capturing an MR Elastography measurement of a biological lifeform may include a platform; a gel pad on a surface of the platform; and a sensor array. In some embodiments, the sensor array includes at least one ultrasound transducer, and at least one radiofrequency (RF) transmitter and receiver coil. The sensor array is at least partially embedded within the gel pad, and the gel pad is configured to provide mechanical impedance matching between the at least one ultrasound transducer and the biological lifeform. In some embodiments, a system includes the apparatus and an MR system, the MR system including an ultrasonic wave generator, an interface circuit, and a computing device. In some such embodiments, the ultrasonic wave generator is configured to generate one or more shear waves in the biological lifeform.

Abstract: A system may measure, using a measurement device, a response associated with a sample to an excitation. Then, the system may compute, using the measured response and the excitation as inputs to a predetermined predictive model, model parameters on a voxel-by-voxel basis in a forward model with multiple voxels that represent the sample. The forward model may simulate response physics occurring within the sample to a given excitation. For example, the forward model may be based on differential or phenomenological equations that approximates the response physics. Moreover, the system may determine an accuracy of the model parameters by comparing at least the measured response and a calculated predicted value of the response using the forward model, the model parameters and the excitation. When the accuracy exceeds a predefined value, the system may provide the model parameters as an output to: a user, another electronic device, a display, and/or a memory.

Abstract: A system performs one or more magnetic resonance (MR) measurements on at least a portion of a biological life form. Moreover, the system quantitatively simulates an MR response of at least the portion of the biological life form, and compares the one or more MR measurements and the quantitative simulation to obtain a first test result. Next, the system determines one or more additional medical tests to perform. In response, the system accesses the biological sample in storage, and performs the one or more additional medical tests on at least a second portion of the biological sample to obtain one or more additional test results. Furthermore, the system computes a second test result based at least in part on the first test result and the one or more additional test results, where the second test result has an improved accuracy relative to the first test result.

Abstract: During operation, a computer system may acquire magnetic resonance (MR) signals associated with a sample from a measurement device or memory. Then, the computer system may access a predetermined set of coil magnetic field basis vectors associated with a surface surrounding the sample, where coil sensitivities of coils in the measurement device are represented by weighted superpositions of the predetermined set of coil magnetic field basis vectors using coefficients, and where the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations. Next, the computer system may solve, on a voxel-by-voxel basis for voxels associated with the sample, a nonlinear optimization problem for MR information associated with the sample and the coefficients using: a forward model that uses the MR information as inputs and simulates response physics of the sample, the MR signals and the predetermined set of coil magnetic field basis vectors.

Abstract: A computer system that performs a sparsity technique is described. During operation, the computer system accesses or obtains information associated with non-invasive measurements performed on at least an individual, historical non-invasive measurements, and a dictionary of predetermined features or basis functions associated with the historical non-invasive measurements. Note that the non-invasive measurements and the historical non-invasive measurements may include or correspond to magnetic resonance (MR) measurements. For example, the MR measurements may include magnetic resonance imaging (MRI) scans. Then, the computer system updates the dictionary of predetermined features based at least in part on the non-invasive measurements and the historical non-invasive measurements, where the updating includes performing a minimization technique with a cost function having an L2-norm term and an L0-norm term.

Abstract: A system may measure, using a measurement device, a response associated with a sample to an excitation. Then, the system may compute, using the measured response and the excitation as inputs to one of an inverse model and a predetermined predictive model, model parameters on a voxel-by-voxel basis in a forward model with multiple voxels that represent the sample. The forward model may simulate response physics occurring within the sample to a given excitation, and the model parameters may include magnetic susceptibilities of the multiple voxels. Moreover, the system may determine an accuracy of the model parameters by comparing at least the measured response and a calculated predicted value of the response using the forward model, the model parameters and the excitation. When the accuracy exceeds a predefined value, the system may provide the model parameters as an output to: a user, another electronic device, a display, and/or a memory.

Abstract: A system may measure, using a measurement device, a response associated with a sample to an excitation. Then, the system may compute, using the measured response and the excitation as inputs to a predetermined predictive model, model parameters on a voxel-by-voxel basis in a forward model with multiple voxels that represent the sample. The forward model may simulate response physics occurring within the sample to a given excitation. For example, the forward model may be based on differential or phenomenological equations that approximates the response physics. Moreover, the system may determine an accuracy of the model parameters by comparing at least the measured response and a calculated predicted value of the response using the forward model, the model parameters and the excitation. When the accuracy exceeds a predefined value, the system may provide the model parameters as an output to: a user, another electronic device, a display, and/or a memory.

Abstract: A computer that determines coefficients in a representation of coil sensitivities and MR information associated with a sample is described. During operation, the computer may acquire MR signals associated with a sample from the measurement device. Then, the computer may access a predetermined set of coil magnetic field basis vectors, where weighted superpositions of the predetermined set of coil magnetic field basis vectors using the coefficients represent coil sensitivities of coils in the measurement device, and where the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations. Next, the computer may solve a nonlinear optimization problem for the MR information associated with the sample and the coefficients using the MR signals and the predetermined set of coil magnetic field basis vectors.

Abstract: During operation, a system may apply an external magnetic field and an RF pulse sequence to a sample. Then, the system may measure at least a component of a magnetization associated with the sample, such as MR signals of one or more types of nuclei in the sample. Moreover, the system may calculate at least a predicted component of the magnetization for voxels associated with the sample based on the measured component of the magnetization, a forward model, the external magnetic field and the RF pulse sequence. Next, the system may solve an inverse problem by iteratively modifying the parameters associated with the voxels in the forward model until a difference between the predicted component of the magnetization and the measured component of the magnetization is less than a predefined value. Note that the calculations may be performed concurrently with the measurements and may not involve performing a Fourier transform.

Abstract: A computer system may iteratively modify a local medical rule that is based on an initial sub-population. In particular, after information specifying the local medical rule and sharing instructions are received from a user of the computer system, the computer system may iteratively apply the local medical rule to one or more additional sub-populations that are associated with other users of the computer system based on the sharing instructions without sharing PHI associated with the initial sub-population. Then, the computer system may aggregate results for the one or more additional sub-populations, and may generate the population-based medical rule by modifying the local medical rule based on the aggregated results and one or more quality metrics. Moreover, the computer system may selectively provide the population-based medical rule to the user without sharing PHI associated with the one or more additional sub-populations.

Abstract: A user interface for medical information includes a timeline that can be highlighted or selected by a time window with a time duration, and the information plots of biomarkers displayed in subsystem displays can update to display the biomarker information for the time duration highlighted by the time window. The trendline, baseline, and data points shown on the information plot(s) can also be adjusted to display only information during the time duration highlighted or selected by the time window.

Abstract: A system may measure, using a measurement device, a response associated with a sample to an excitation. Then, the system may compute, using the measured response and the excitation as inputs to one of an inverse model and a predetermined predictive model, model parameters on a voxel-by-voxel basis in a forward model with multiple voxels that represent the sample. The forward model may simulate response physics occurring within the sample to a given excitation, and the model parameters may include magnetic susceptibilities of the multiple voxels. Moreover, the system may determine an accuracy of the model parameters by comparing at least the measured response and a calculated predicted value of the response using the forward model, the model parameters and the excitation. When the accuracy exceeds a predefined value, the system may provide the model parameters as an output to: a user, another electronic device, a display, and/or a memory.

Abstract: A system may measure, using a measurement device, a response associated with a sample to an excitation. Then, the system may compute, using the measured response and the excitation as inputs to a predetermined predictive model, model parameters on a voxel-by-voxel basis in a forward model with multiple voxels that represent the sample. The forward model may simulate response physics occurring within the sample to a given excitation. For example, the forward model may be based on differential or phenomenological equations that approximates the response physics. Moreover, the system may determine an accuracy of the model parameters by comparing at least the measured response and a calculated predicted value of the response using the forward model, the model parameters and the excitation. When the accuracy exceeds a predefined value, the system may provide the model parameters as an output to: a user, another electronic device, a display, and/or a memory.

Abstract: A computer system may perform substitutions for fields in a set of records, where performing a given substitution involves replacing a field in the set of records with a replacement field, and the substitutions remove the context information in the set of records while maintaining relevance of the set of records. Then, the computer system may generate an artificial set of records based, at least in part, on the set of records, where a given artificial record includes one or more modified portions of the set of records. Next, the computer system may combine the set of records and the artificial set of records into a second set of records, where at least some phrases or values in the second set of records are uniformly distributed.

Abstract: During operation, a system may apply a polarizing field and an excitation sequence to a sample. Then, the system may measure a signal associated with the sample for a time duration that is less than a magnitude of a relaxation time associated with the sample. Next, the system may calculate the relaxation time based on a difference between the measured signal and a predicted signal of the sample, where the predicted signal is based on a forward model, the polarizing field and the excitation sequence. After modifying at least one of the polarizing field and the excitation sequence, the aforementioned operations may be repeated until a magnitude of the difference is less than a convergence criterion. Note that the calculations may be performed concurrently with the measurements and may not involve performing a Fourier transform on the measured signal.

Abstract: During operation, a system may apply an external magnetic field and an RF pulse sequence to a sample. Then, the system may measure at least a component of a magnetization associated with the sample, such as MR signals of one or more types of nuclei in the sample. Moreover, the system may calculate at least a predicted component of the magnetization for voxels associated with the sample based on the measured component of the magnetization, a forward model, the external magnetic field and the RF pulse sequence. Next, the system may solve an inverse problem by iteratively modifying the parameters associated with the voxels in the forward model until a difference between the predicted component of the magnetization and the measured component of the magnetization is less than a predefined value. Note that the calculations may be performed concurrently with the measurements and may not involve performing a Fourier transform.

Abstract: An apparatus for use in a magnetic resonance (MR) system for capturing an MR Elastography measurement of a biological lifeform may include a platform; a gel pad on a surface of the platform; and a sensor array. In some embodiments, the sensor array includes at least one ultrasound transducer, and at least one radiofrequency (RF) transmitter and receiver coil. The sensor array is at least partially embedded within the gel pad, and the gel pad is configured to provide mechanical impedance matching between the at least one ultrasound transducer and the biological lifeform. In some embodiments, a system includes the apparatus and an MR system, the MR system including an ultrasonic wave generator, an interface circuit, and a computing device. In some such embodiments, the ultrasonic wave generator is configured to generate one or more shear waves in the biological lifeform.

Abstract: During operation, a system may apply an external magnetic field and an RF pulse sequence to a sample. Then, the system may measure at least a component of a magnetization associated with the sample, such as MR signals of one or more types of nuclei in the sample. Moreover, the system may calculate at least a predicted component of the magnetization for voxels associated with the sample based on the measured component of the magnetization, a forward model, the external magnetic field and the RF pulse sequence. Next, the system may solve an inverse problem by iteratively modifying the parameters associated with the voxels in the forward model until a difference between the predicted component of the magnetization and the measured component of the magnetization is less than a predefined value. Note that the calculations may be performed concurrently with the measurements and may not involve performing a Fourier transform.

Abstract: During operation, a system iteratively captures MR signals of one or more types of nuclei in one or more portions of a biological lifeform based on scanning instructions that correspond to a dynamic scan plan. The MR signals in a given iteration may be associated with voxels having associated sizes at three-dimensional (3D) positions in at least a corresponding portion of the biological lifeform. If the system detects a potential anomaly when analyzing the MR signals from the given iteration, the system dynamically modifies the scan plan based on the detected potential anomaly, a medical history and/or an MR-scan history. Subsequent measurements of MR signals may be associated with the same or different: types of nuclei, portions of the biological lifeform, voxels sizes and/or 3D positions.

Abstract: A system performs one or more magnetic resonance (MR) measurements on at least a portion of a biological life form. Moreover, the system quantitatively simulates an MR response of at least the portion of the biological life form, and compares the one or more MR measurements and the quantitative simulation to obtain a first test result. Next, the system determines one or more additional medical tests to perform. In response, the system accesses the biological sample in storage, and performs the one or more additional medical tests on at least a second portion of the biological sample to obtain one or more additional test results. Furthermore, the system computes a second test result based at least in part on the first test result and the one or more additional test results, where the second test result has an improved accuracy relative to the first test result.