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: 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.

Abstract: A system that iteratively performs medical testing is described. During operation, the system receives a test result of a medical test performed on a biological sample associated with an individual, where the test result has an initial uncertainty. Then, the system determines, based on the test result, a second medical test to perform on a second biological sample associated with the individual, where the second biological sample was acquired prior to the biological sample. Moreover, the system performs the second medical test on the second biological sample to obtain a second test result of the second medical test. Next, the system computes a revised result for the medical test based on the test result and the second test result, where the revised result has a second uncertainty that is less than the initial uncertainty.

Abstract: A system that determines an invariant magnetic-resonance (MR) signature of a biological sample is disclosed. During operation, the system determines a magnetic-resonance (MR) model of voxels in a biological sample based on differences between MR signals associated with the voxels in multiple scans and simulated MR signals. The MR signals are measured or captured by an MR scanner in the system during multiple MR scans, and based on scanning instructions, and the simulated MR signals for the biological sample are generated using the MR model and the scanning instructions. Moreover, the system iteratively modifies the scanning instructions (including a magnetic-field strength and/or a pulse sequence) in the MR scans based on the differences until a convergence criterion is achieved. Then, the system stores, in memory, an identifier of the biological sample and a magnetic-field-strength-invariant MR signature of the biological sample that is associated with the MR model.

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 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: 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: 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: 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 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 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 that determines an invariant magnetic-resonance (MR) signature of a biological sample is disclosed. During operation, the system determines a magnetic-resonance (MR) model of voxels in a biological sample based on differences between MR signals associated with the voxels in multiple scans and simulated MR signals. The MR signals are measured or captured by an MR scanner in the system during multiple MR scans, and based on scanning instructions, and the simulated MR signals for the biological sample are generated using the MR model and the scanning instructions. Moreover, the system iteratively modifies the scanning instructions (including a magnetic-field strength and/or a pulse sequence) in the MR scans based on the differences until a convergence criterion is achieved. Then, the system stores, in memory, an identifier of the biological sample and a magnetic-field-strength-invariant MR signature of the biological sample that is associated with the MR model.