Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: A computer-implemented method for medical measurement reconstruction may comprise obtaining a first reconstruction of at least one representation of at least one set of medical measurements; presenting the first reconstruction or information about the first reconstruction to a reviewer; receiving an input from the reviewer relating to the first reconstruction or the information about the first reconstruction; processing the received input; and generating a second, modified reconstruction based on the received input.

Abstract: A computer-implemented method for medical measurement reconstruction may comprise: receiving a measurement acquisition signal; based on the received measurement acquisition signal, creating a plurality of representations of the measurement acquisition signal, wherein each of the plurality of representations relates to a different aspect of the measurement acquisition signal; modifying one or more of the plurality of representations; and generating an output signal including the modified one or more of the plurality of representations.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parameterized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parameterized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for associating medical images with a patient. One method includes: receiving two or more medical images of patient anatomy in an electronic storage medium; generating an anatomical model for each of the received medical images; comparing the generated anatomical models; determining a score assessing the likelihood that the two or more medical images belong to the same patient, using the comparison of the generated anatomical models; and outputting the score to an electronic storage medium or display.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for associating medical images with a patient. One method includes: receiving two or more medical images of patient anatomy in an electronic storage medium; generating an anatomical model for each of the received medical images; comparing the generated anatomical models; determining a score assessing the likelihood that the two or more medical images belong to the same patient, using the comparison of the generated anatomical models; and outputting the score to an electronic storage medium or display.

Abstract: Systems and methods are disclosed for associating medical images with a patient. One method includes: receiving two or more medical images of patient anatomy in an electronic storage medium; generating an anatomical model for each of the received medical images; comparing the generated anatomical models; determining a score assessing the likelihood that the two or more medical images belong to the same patient, using the comparison of the generated anatomical models; and outputting the score to an electronic storage medium or display.

Abstract: Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

Abstract: Systems and methods are disclosed for associating medical images with a patient. One method includes: receiving two or more medical images of patient anatomy in an electronic storage medium; generating an anatomical model for each of the received medical images; comparing the generated anatomical models; determining a score assessing the likelihood that the two or more medical images belong to the same patient, using the comparison of the generated anatomical models; and outputting the score to an electronic storage medium or display.